2018 CVPR

下面是2018 CVPR文章的主题标签,文章列表来源于http://cvpr2018.thecvf.com/program/overview
Topic: Scene parsing; Object segmentation; Image segmentation; Video segmentation; Boundary detection; Contour analysis; Object tracking; Action recognition; Crowd analysis; Video analysis; Human detection; Human parsing; Face recognition; Face parsing; Object recognition; Object detection; Saliency detection; Scene recognition; Text recognition; Image retrieval; 3D modeling; Feature matching; Motion estimation; Stereo matching; Optical flow; Region matching; Image editing; Computational photography; Texture analysis; Data clustering; Space reduction; Machine learning; Deep learning; Multimodel learning; Pointcloud analysis;
【Scene parsing】PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing

Dan Xu,:

Wanli Ouyang, The University of Sydney: http://www.ee.cuhk.edu.hk/~wlouyang/

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

Nicu Sebe, University of Trento:

【Scene parsing】On the Robustness of Semantic Segmentation Models to Adversarial Attacks

Anurag Arnab, University of Oxford:

Ondrej Miksik, University of Oxford:

Phil Torr, Oxford:

【Scene parsing】Recurrent Scene Parsing with Perspective Understanding in the Loop

Shu Kong, University of California, Irvine:

Charless Fowlkes, University of California, Irvine, USA: http://www.ics.uci.edu/~fowlkes/

【Scene parsing】Human Semantic Parsing for Person Re-identification

Mahdi Kalayeh, UCF:

Emrah Basaran,:

Mubarak Shah, UCF: http://crcv.ucf.edu/people/faculty/shah.html

【Scene parsing】Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features

Xiang Wang, Tsinghua University:

Shaodi You, Data61:

Xi Li, Tsinghua University:

Huimin Ma, Tsinghua University:

【Scene parsing】Bootstrapping the Performance of Webly Supervised Semantic Segmentation

Tong Shen, The University of Adelaide:

Guosheng Lin, Nanyang Technological Universi:

Chunhua Shen, University of Adelaide:

Ian Reid,: http://www.robots.ox.ac.uk/~ian/

【Scene parsing】Deep Voting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion

Zhishuai Zhang, Johns Hopkins University:

Cihang Xie, JHU:

Jianyu Wang,:

Lingxi Xie, UCLA:

Alan Yuille, JHU: http://www.stat.ucla.edu/~yuille/

【Scene parsing】On the Importance of Label Quality for Semantic Segmentation

Aleksandar Zlateski, MIT:

ronnachai Jaroensri, Massachusetts Institute of Technology:

Prafull Sharma, MIT:

Fredo Durand,: http://people.csail.mit.edu/fredo/

【Scene parsing】Learning a Discriminative Feature Network for Semantic Segmentation

Changqian Yu, HUST:

Jingbo Wang, Peking University:

Chao Peng, Megvii:

Changxin Gao, HUST:

Gang Yu, Face++:

Nong Sang,:

【Scene parsing】Context Contrasted Feature and Gated Multi-scale Aggregation for Scene Segmentation

Henghui Ding, Nanyang Technological University:

Xudong Jiang, Nanyang Technological University:

Bing Shuai,:

Ai Qun Liu, Nanyang Technological University:

Gang Wang,:

【Scene parsing】Tags2Parts: Discovering Semantic Regions from Shape Tags

Sanjeev Muralikrishnan, IIT Bombay:

Vladimir Kim, Adobe Research:

Siddhartha Chaudhuri, IIT Bombay:

【Scene parsing】Structured Set Matching Networks for One-Shot Part Labeling

Jonghyun Choi,:

Jayant Krishnamurthy, Semantic Machines:

Aniruddha Kembhavi, Allen Institute for Artificial Intelligence:

Ali Farhadi,: http://homes.cs.washington.edu/~ali/index.html

【Scene parsing】Dense ASPP: Densely Connected Networks for Semantic Segmentation

Maoke Yang, Deep Motion:

Kun Yu, Deep Motion:

Kuiyuan Yang, Deep Motion:

【Scene parsing】Learning from Synthetic Data: Semantic Segmentation using Generative Adversarial Networks

Swami Sankaranarayanan, University of Maryland:

Yogesh Balaji, University of Maryland:

Arpit Jain,:

Ser-Nam Lim, GE Global Research:

Rama Chellappa, University of Maryland, USA:

【Scene parsing】Finding beans in burgers: Deep semantic-visual embedding with localization

Patrick Perez, Technicolor Research:

Matthieu Cord,:

Louis Chevallier, technicolor:

Martin Engilberge, technicolor:

【Scene parsing】Learning to Segment Every Thing

Ronghang Hu, UC Berkeley:

Piotr Dollar, Facebook AI Research, Menlo Park, USA: http://vision.ucsd.edu/~pdollar/

Kaiming He,: http://research.microsoft.com/en-us/um/people/kahe/

Trevor Darrell, UC Berkeley, USA: http://www.eecs.berkeley.edu/~trevor/

Ross Girshick,: http://www.cs.berkeley.edu/~rbg/

【Scene parsing】Scan Complete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans

Angela Dai,:

Daniel Ritchie, Brown University:

Martin Bokeloh, Google:

Scott Reed, Google:

Juergen Sturm, Google:

Matthias Nießner, Technical University of Munich:

【Scene parsing】Automatic Map Inference from Aerial Images

Favyen Bastani, MIT CSAIL:

Songtao He, MIT CSAIL:

Mohammad Alizadeh, MIT CSAIL:

Hari Balakrishnan, MIT CSAIL:

Sam Madden, MIT CSAIL:

Sanjay Chawla, Qatar Computing Research Institute:

Sofiane Abbar, Qatar Computing Research Institute:

David De Witt, MIT CSAIL:

【Scene parsing】Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation

Jiwoon Ahn, DGIST:

Suha Kwak, POSTECH:

【Scene parsing】Regularizing Deep Networks by Modeling and Predicting Label Structure

Mohammadreza Mostajabi, TTI-Chicago:

Michael Maire,: http://ttic.uchicago.edu/~mmaire/

Greg Shakhnarovich,:

【Scene parsing】End-to-end Convolutional Semantic Embeddings

Quanzeng You, Microsoft:

Zhengyou Zhang, Microsoft Research:

Jiebo Luo, University of Rochester: http://www.cs.rochester.edu/u/jluo/

【Scene parsing】Neural Motifs: Scene Graph Parsing with Global Context

Rowan Zellers, University of Washington:

Mark Yatskar, University of Washington:

Samuel Thomson, Carnegie Mellon University:

Yejin Choi, University of Washington:

【Scene parsing】Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation

Piotr Bilinski, University of Oxford:

Victor Prisacariu, Oxford:

【Scene parsing】Fully Convolutional Adaptation Networks for Semantic Segmentation

Yiheng Zhang, University of Science and Technology of China:

Zhaofan Qiu, University of Science and Technology of China:

Ting Yao, Microsoft Research Asia:

Dong Liu, Univ Sci Tech China:

Tao Mei, Microsoft Research Asia:

【Scene parsing】Objects as context for detecting their semantic parts

Abel Gonzalez-Garcia, University of Edinburgh:

Davide Modolo, Amazon:

Vitto Ferrari,:

【Scene parsing】Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing

Zilong Huang, HUST:

Xinggang Wang,:

Jiasi Wang, HUST:

Wenyu Liu,:

Jingdong Wang, Microsoft Research:

【Scene parsing】Context Encoding for Semantic Segmentation

Hang Zhang, Rutgers University:

Kristin Dana,:

Jianping Shi, Sense Time:

Zhongyue Zhang, Amazon:

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

Ambrish Tyagi, Amazon:

Amit Agrawal, Amazon:

【Scene parsing】Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation

Yunchao Wei,:

Huaxin Xiao,:

Honghui Shi, UIUC:

Zequn Jie,:

Jiashi Feng,:

Thomas Huang,:

【Scene parsing】Learning to Adapt Structured Output Space for Semantic Segmentation

Yi-Hsuan Tsai, NEC Labs America:

Wei-Chih Hung, University of California, Merced:

Samuel Schulter, NEC Labs:

Kihyuk Sohn, NEC Laboratories America:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

Manmohan Chandraker, NEC Labs America:

【Scene parsing】Weakly Supervised Coupled Networks for Visual Sentiment Analysis

Jufeng Yang, Nankai University:

Dongyu She,:

Yu-Kun Lai, Cardiff University:

Paul Rosin,:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Scene parsing】Preserving Semantic Relations for Zero-Shot Learning

Yashas Annadani, NITK:

Soma Biswas, Indian Institute of Science:

【Scene parsing】ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes

Yuhua Chen, CVL@ETHZ:

Wen Li, ETH:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

【Object segmentation】Interactive Image Segmentation with Latent Diversity

Zhuwen Li, Intel Labs:

Qifeng Chen, Intel Labs:

Vladlen Koltun, Intel Labs: http://vladlen.info/publications/

【Object segmentation】Deep Extreme Cut: From Extreme Points to Object Segmentation

Kevis-Kokitsi Maninis, ETH Zurich:

Sergi Caelles, ETH Zurich:

Jordi Pont-Tuset, ETHZ:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

【Object segmentation】Learned Shape-Tailored Descriptors for Segmentation

Naeemullah Khan, KAUST:

Ganesh Sundaramoorthi,:

【Object segmentation】Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++

David Acuna, University of Toronto:

Huan Ling, Uof T:

Amlan Kar, University of Toronto:

Sanja Fidler,:

【Object segmentation】Mo Net: Deep Motion Exploitation for Video Object Segmentation

Huaxin Xiao, Nudt:

Jiashi Feng,:

Guosheng Lin, Nanyang Technological Universi:

Yu Liu, NUDT:

Maojun Zhang,:

【Object segmentation】Multi-Evidence Fusion and Filtering for Weakly Supervised Object Recognition, Detection and Segmentation

Weifeng Ge, The University of Hong Kong:

Yizhou Yu, The University of Hong Kong: http://i.cs.hku.hk/~yzyu/

【Object segmentation】Motion-Guided Cascaded Refinement Network for Video Object Segmentation

Ping Hu,:

Gang Wang,:

Xiangfei Kong, Nanyang Technological University:

Jason Kuen, NTU, Singapore:

Yap-Peng Tan,:

【Object segmentation】Seed Net : Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segmentation

Gwangmo Song, Seoul National University:

Heesoo Myeong, Samsung:

Kyoung Mu Lee,: http://cv.snu.ac.kr/kmlee/

【Object segmentation】SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation

Weiyue Wang, USC:

Ronald Yu,:

Qiangui Huang, U of Southern CA:

Ulrich Neumann, USC:

【Object segmentation】Recurrent Slice Networks for 3D Segmentation on Point Clouds

Qiangui Huang, U of Southern CA:

Weiyue Wang, USC:

Ulrich Neumann, USC:

【Object segmentation】Weakly Supervised Instance Segmentation using Class Peak Response

Yanzhao Zhou, UCAS, China:

Yi Zhu, UCAS:

Qixiang Ye,:

Qiang Qiu,:

Jianbin Jiao,:

【Object segmentation】Mask Lab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features

Liang-Chieh Chen,:

Alexander Hermans, RWTH Aachen University:

George Papandreou, Google Inc.: http://www.stat.ucla.edu/~gpapan/index.html

Florian Schroff, Google Inc.:

Peng Wang, Baidu:

Hartwig Adam, Google:

【Object segmentation】Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

Mark Sandler, Google:

Andrew Howard, Google:

Menglong Zhu,:

Andrey Zhmoginov, Google:

Liang-Chieh Chen,:

【Object segmentation】Analysis of Hand Segmentation in the Wild

Aisha Urooj, University of Central Florida:

Ali Borji, UCF: http://ilab.usc.edu/borji/

【Object segmentation】Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation

Qihang Yu, Peking University:

Lingxi Xie, UCLA:

Yan Wang, JHU:

Yuyin Zhou, JHU:

Elliot Fishman,:

Alan Yuille, JHU: http://www.stat.ucla.edu/~yuille/

【Object segmentation】TOM-Net: Learning Transparent Object Matting from a Single Image

Guanying Chen, The University of Hong Kong:

Kai Han,:

Kwan-Yee Kenneth Wong, The University of Hong Kong:

【Object segmentation】Accurate and Diverse Sampling of Sequences based on a “Best of Many” Sample Objective

Apratim Bhattacharyya, MPI Informatics:

Mario Fritz, MPI, Saarbrucken, Germany: https://scalable.mpi-inf.mpg.de/

Bernt Schiele, MPI Informatics Germany: http://www.d2.mpi-inf.mpg.de/schiele/

【Object segmentation】Guide Me: Interacting with Deep Networks

Christian Rupprecht, Technische Unitversit?t M?nchen:

Iro Laina,:

Federico Tombari,: http://vision.deis.unibo.it/fede/

Nassir Navab, Technical University of Munich: http://campar.in.tum.de/Main/NassirNavab

Gregory Hager, Johns Hopkins University:

【Object segmentation】Path Aggregation Network for Instance Segmentation

Shu Liu, CUHK:

Lu Qi, CUHK:

Haifang Qin,:

Jianping Shi, Sense Time:

Jiaya Jia, Chinese University of Hong Kong: http://www.cse.cuhk.edu.hk/leojia/

【Object segmentation】TOM-Net: Learning Transparent Object Matting from a Single Image

Guanying Chen, The University of Hong Kong:

Kai Han,:

Kwan-Yee Kenneth Wong, The University of Hong Kong:

【Image segmentation】Learning Superpixels with Segmentation-Aware Affinity Loss

Wei-Chih Tu, National Taiwan University:

Ming-Yu Liu, NVIDIA:

Varun Jampani, NVIDIA Research:

Deqing Sun, NVIDIA: http://cs.brown.edu/~dqsun/index.html

Shao-Yi Chien, National Taiwan University:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

Jan Kautz, NVIDIA:

【Image segmentation】Compassionately Conservative Balanced Cuts for Image Segmentation

Nathan Cahill, Rochester Institute of Technol:

Tyler Hayes, Rochester Institute of Tech:

Renee Meinhold, Rochester Institute of Technology:

John Hamilton, RIT:

【Image segmentation】Normalized Cut Loss for Weakly Supervised CNN Segmentation

Meng Tang, UWO:

Federico Perazzi, Disney Research Zurich:

Abdelaziz Djelouah, The Walt Disney Company:

Yuri Boykov, University of Western Ontario: http://www.csd.uwo.ca/~yuri/

Christopher Schroers, Disney Research Zurich:

【Image segmentation】Referring Image Segmentation via Recurrent Refinement Networks

Ruiyu Li, CUHK:

Kaican Li, CUHK:

Yi-Chun Kuo, CUHK:

Michelle Shu,:

Xiaojuan Qi, CUHK:

Xiaoyong Shen, CUHK:

Jiaya Jia, Chinese University of Hong Kong: http://www.cse.cuhk.edu.hk/leojia/

【Image segmentation】Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation

Xiaowei Xu, University of Notre Dame:

Yiyu Shi, University of Notre Dame:

Qing Lu, University of Notre Dame:

Lin Yang, University of Notre Dame:

Sharon Hu, University of Notre Dame:

Danny Chen, University of Notre Dame:

【Image segmentation】Guided Proofreading of Automatic Segmentations for Connectomics

Daniel Haehn, Harvard University:

Verena Kaynig,:

James Tompkin, Brown University:

Jeff Lichtman, Harvard University:

Hanspeter Pfister, Harvard University:

【Video segmentation】The Best of Both Worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation

Pia Bideau, University of Massachusets:

Aruni Roy Chowdhury, University of Massachusetts:

Rakesh Radhakrishnan Menon, University of Massachusetts:

Erik Miller,:

【Video segmentation】Content-Sensitive Supervoxels via Uniform Tessellations on Video Manifolds

Ran Yi, Tsinghua University:

Yong-Jin Liu,:

Yu-Kun Lai, Cardiff University:

【Video segmentation】Dynamic-Structured Semantic Propagation Network

Xiaodan Liang, Carnegie Mellon University:

Hongfei Zhou,:

Eric Xing, Carnegie Mellon University: http://www.cs.cmu.edu/~epxing/

【Video segmentation】Video Representation Learning Using Discriminative Pooling

Jue Wang, ANU: http://www.juew.org/

Anoop Cherian,:

Fatih Porikli, NICTA, Australia: http://www.porikli.com/

Stephen Gould, Australian National University: http://users.cecs.anu.edu.au/~sgould/index.html

【Video segmentation】Pixel-Wise Metric Learning for Blazingly Fast Video Object Segmentation

Yuhua Chen, CVL@ETHZ:

Jordi Pont-Tuset, ETHZ:

Alberto Montes, ETHZ:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

【Video segmentation】Motion Segmentation by Exploiting Complementary Geometric Models

Xun Xu, National University of Singapore:

Loong Fah Cheong, National University of Singapore:

Zhuwen Li, Intel Labs:

【Video segmentation】Actor and Action Video Segmentation from a Sentence

Kirill Gavrilyuk, University of Amsterdam:

Amir Ghodrati, University of Amsterdam:

zhenyang Li, University of Amsterdam:

Cees Snoek, University of Amsterdam:

【Video segmentation】Video Object Segmentation via Inference in A CNN-Based Higher-Order Spatio-Temporal MRF

Linchao Bao, Tencent AI Lab:

Baoyuan Wu, Tencent AI Lab:

Wei Liu,:

【Video segmentation】Efficient Video Object Segmentation via Network Modulation

Linjie Yang, Snap Research:

YANRAN WANG, NORTHWESTERN:

Xuehan Xiong, Snapchat:

Jianchao Yang, Snap: http://www.ifp.illinois.edu/~jyang29/

Aggelos Katsaggelos, Northwestern University:

【Video segmentation】Weakly-Supervised Action Segmentation with Iterative Soft Boundary Assignment

Li Ding, MIT:

Chenliang Xu, University of Rochester:

【Video segmentation】Instance Embedding Transfer to Unsupervised Video Object Segmentation

Siyang Li, USC:

Bryan Seybold, Google Research:

Alexey Vorobyov, Google Inc.:

Alireza Fathi, Stanford University:

Qin Huang, University of Southern California:

C.-C. Jay Kuo, University of Southern California:

【Video segmentation】Dynamic Video Segmentation Network

Yu-Shuan Xu, National Tsing Hua University:

Chun-Yi Lee, National Tsing Hua University:

TSUJUI FU, NTHUCS:

Hsuan Kung Yang, National Tsing Hua University:

【Video segmentation】Temporal Deformable Residual Networks for Action Segmentation in Videos

Peng Lei, Oregon State University:

Sinisa Todorovic,: http://web.engr.oregonstate.edu/~sinisa/

【Video segmentation】Semantic Video Segmentation by Gated Recurrent Flow Propagation

David Nilsson, Lund University:

Cristian Sminchisescu,:

【Video segmentation】Fast Video Object Segmentation by Reference-Guided Mask Propagation

Seoung Wug Oh, Yonsei Univeristy:

Joon-Young Lee,:

Kalyan Sunkavalli, Adobe Systems Inc.:

Seon Joo Kim, Yonsei University:

【Video segmentation】Fast and Accurate Online Video Object Segmentation via Tracking Parts

Jingchun Cheng, Tsinghua University:

Yi-Hsuan Tsai, NEC Labs America:

Wei-Chih Hung, University of California, Merced:

Shengjin Wang,:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Video segmentation】Unsupervised Learning and Segmentation of Complex Activities from Video

Fadime Sener, University of Bonn:

Angela Yao, University of Bonn:

【Video segmentation】Reinforcement Cutting-Agent Learning for Video Object Segmentation

Junwei Han, Northwestern Polytechnical U.:

Le Yang, Northwestern Polytechnical Uni:

Dingwen Zhang,:

Xiaojun Chang, Carnegie Mellon University:

Xiaodan Liang, Carnegie Mellon University:

【Video segmentation】Deep Spatio-Temporal Random Fields for Efficient Video Segmentation

Siddhartha Chandra, INRIA:

Camille Couprie, Facebook Artificial Intelligence Research:

Iasonas Kokkinos, FAIR/UCL: http://cvn.ecp.fr/personnel/iasonas/index.html

【Boundary detection】LEGO: Learning Edge with Geometry all at Once by Watching Videos

Zhenheng Yang,:

Peng Wang, Baidu:

Yang Wang, Baidu USA:

Wei Xu,:

Ram Nevatia,: http://iris.usc.edu/USC-Computer-Vision.html

【Boundary detection】Boundary Flow: A Siamese Network that Predicts Boundary Motion without Training on Motion

Peng Lei, Oregon State University:

Fuxin Li, Oregon State University: http://www.cc.gatech.edu/~fli/

Sinisa Todorovic,: http://web.engr.oregonstate.edu/~sinisa/

【Contour analysis】Learning to Sketch with Shortcut Cycle Consistency

Jifei Song, Queen Mary, Uni. of London:

Kaiyue Pang, QMUL:

Yi-Zhe Song,:

Tao Xiang, Queen Mary University of London:

Timothy Hospedales, University of Edinburgh:

【Contour analysis】Generating a Fusion Image: One’ s Identity and Another’s Shape

Dong Gyu Joo, KAIST:

Doyeon Kim, KAIST:

Junmo Kim, KAIST:

【Contour analysis】Generative Non-Rigid Shape Completion with Graph Convolutional Autoencoders

Or Litany, Tel Aviv University:

Alex Bronstein,:

Michael Bronstein,:

Ameesh Makadia, Google Research:

【Contour analysis】Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers

Stephan Richter, TU Darmstadt:

Stefan Roth,: http://www.igp.ethz.ch/photogrammetry/

【Contour analysis】Monocular 3D Pose and Shape Estimation of Multiple People in Natural Scenes

Elisabeta Marinoiu, IMAR and Lund University:

Andrei Zanfir, IMAR and Lund University:

Cristian Sminchisescu,:

【Contour analysis】Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View

Albert Pumarola, IRI (CSIC-UPC):

Antonio Agudo, IRI (CSIC-UPC):

Lorenzo Porzi, Mapillary Research:

Alberto Sanfeliu, IRI (CSIC-UPC):

Vincent Lepetit, University of Bordeaux: http://cvlabwww.epfl.ch/~lepetit/

Francesc Moreno-Noguer, Institut de Robotica i Informatica Industrial (UPC/CSIC):

【Contour analysis】Deep Marching Cubes: Learning Explicit Surface Representations

Yiyi Liao, Zhejiang University:

Simon Donné, Ghent University:

Andreas Geiger, MPI Tuebingen / ETH Zuerich:

【Contour analysis】Planar Shape Detection at Structural Scales

Hao Fang, Inria:

Florent Lafarge,:

Mathieu Desbrun, Caltech:

【Contour analysis】Pixels, voxels, and views: A study of shape representations for single view 3D object shape prediction

Daeyun Shin, UC Irvine:

Charless Fowlkes, University of California, Irvine, USA: http://www.ics.uci.edu/~fowlkes/

Derek Hoiem,: http://www.cs.illinois.edu/~dhoiem/

【Contour analysis】Curve Reconstruction via the Global Statistics of Natural Curves

Ehud Barnea, Ben-Gurion University:

Ohad Ben-Shahar, Ben-Gurion University:

【Contour analysis】Shape from Shading through Shape Evolution

Dawei Yang, University of Michigan:

Jia Deng,:

【Contour analysis】Lions and Tigers and Bears: Capturing Non-Rigid, 3D, Articulated Shape from Images

Silvia Zuffi, IMATI-CNR:

Angjoo Kanazawa, University of Maryland:

Michael Black, Max Planck Institute for Intelligent Systems: http://ps.is.tue.mpg.de/person/black

【Contour analysis】CSGNet: Neural Shape Parser for Constructive Solid Geometry

Gopal Sharma, University of Massachusetts:

Subhransu Maji,: http://people.cs.umass.edu/~smaji/

Rishabh Goyal, Indian Institute of Technology, Kanpu:

Difan Liu, UMass Amherst:

Evangelos Kalogerakis, UMass:

【Contour analysis】Learning Deep Sketch Abstraction

Umar Riaz Muhammad, Queen Mary Uni of London:

Yongxin Yang, Queen Mary University of London:

Yi-Zhe Song,:

Tao Xiang, Queen Mary University of London:

Timothy Hospedales, University of Edinburgh:

【Contour analysis】A Variational U-Net for Conditional Appearance and Shape Generation

Ekaterina Sutter, HCI, IWR,Heidelberg University:

Patrick Esser, Heidelberg University:

Bjorn Ommer, Heidelberg:

【Contour analysis】Learning deep structured active contours end-to-end

Diego Marcos,:

Devis Tuia, Wageningen University:

Benjamin Kellenberger, Wageningen University and Research:

Lisa Zhang, University of Toronto:

Min Bai,:

Renjie Liao,:

Raquel Urtasun, University of Toronto: http://www.cs.toronto.edu/~urtasun/

【Contour analysis】Point Grid: A Deep Network for 3D Shape Understanding

Truc Le, University of Missouri – Columbia:

Ye Duan, University of Missouri – Columbia:

【Contour analysis】Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms

Alexandre Bône, Brain and Spine Institute:

Olivier Colliot, Institut du Cerveau et de la Moelle épinière:

Stanley Durrleman, Institut du Cerveau et de la Moelle épinière:

【Contour analysis】3D Registration of Curves and Surfaces using Local Differential Information

Carolina Raposo, Institute of Systems and Robot:

Joao Barreto, University of Coimbra, Portugal:

【Object tracking】GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB

Franziska Mueller, MPI Informatics:

Florian Bernard, MPI Informatics:

Oleksandr Sotnychenko, MPI Informatics:

Dushyant Mehta, MPI For Informatics:

Srinath Sridhar,:

Dan Casas, MPI:

Christian Theobalt, MPI Informatics:

【Object tracking】Detect-and-Track: Efficient Pose Estimation in Videos

Rohit Girdhar, CMU:

Georgia Gkioxari, Facebook:

Lorenzo Torresani, Darthmout College, USA:

Manohar Paluri,:

Du Tran, Dartmouth College:

【Object tracking】Context-aware Deep Feature Compression for High-speed Visual Tracking

Jongwon Choi,:

Hyung Jin Chang, Imperial College London:

Tobias Fischer, Imperial College London:

Sangdoo Yun, Seoul National University:

Jiyeoup Jeong, Seoul National University:

kyuewang Lee, Seoul National University:

Yiannis Demiris,:

Jin Choi,:

【Object tracking】Correlation Tracking via Joint Discrimination and Reliability Learning

Chong Sun, Dalian Universityof Technology:

Dong Wang, DUT:

Huchuan Lu, Dalian University of Technology: http://ice.dlut.edu.cn/lu/index.html

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Object tracking】Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning

Xingping Dong, Beijing Institute of Technology:

Jianbing Shen, Beijing Institute of Technolog: http://cs.bit.edu.cn/shenjianbing/

Wenguan Wang, Beijing Institute of Technology:

Yu Liu, Beijing Institute of Technology:

Ling Shao, University of East Anglia: http://lshao.staff.shef.ac.uk/

Fatih Porikli, NICTA, Australia: http://www.porikli.com/

【Object tracking】A Prior-Less Method for Multi-Face Tracking in Unconstrained Videos

CHUNG-CHING LIN, IBM Research:

Ying Hung, Rutgers University:

【Object tracking】End-to-end Flow Correlation Tracking with Spatial-temporal Attention

Zheng Zhu, Institute of Automation, CAS:

Wei Wu,:

Wei Zou,:

Junjie Yan,:

【Object tracking】A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects

Yuanlu Xu, University of California, Los Angeles:

Lei Qin, Institute of Computing Technology, Chinese Academy of Sciences:

Xiaobai Liu, San Diego State University:

Song-Chun Zhu,: http://www.stat.ucla.edu/~sczhu/

【Object tracking】Towards dense object tracking in a 2D honeybee hive

Katarzyna Bozek, Okinawa Institute of Science a:

Laetitia Hebert,:

Alexander Mikheyev,:

Greg Stephens, OIST Graduate University and Vrije Universiteit Amsterdam:

【Object tracking】Efficient Diverse Ensemble for Discriminative Co-Tracking

Kourosh Meshgi, Kyoto University:

Shigeyuki Oba, Kyoto University:

Shin Ishii, Kyoto University:

【Object tracking】Rolling Shutter and Radial Distortion are Features for High Frame Rate Multi-camera Tracking

Akash Bapat, UNC Chapel Hill:

Jan-Michael Frahm, UNC Chapel Hill:

True Price, UNC Chapel Hill:

【Object tracking】A Twofold Siamese Network for Real-Time Object Tracking

Anfeng He, USTC:

Chong Luo, Microsoft Research Asia:

Xinmei Tian, USTC:

Wenjun Zeng,:

【Object tracking】Multi-Cue Correlation Filters for Robust Visual Tracking

Ning Wang, USTC:

Wengang Zhou, USTC:

Qi Tian,: http://www.cs.utsa.edu/~qitian/

Richang Hong,:

Meng Wang, He Fei University of Technology:

Houqiang Li,:

【Object tracking】Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking

Qiang Wang, CASIA:

Zhu Teng, Beijing Jiaotong University:

Junliang Xing, Institute of Automation, Chinese Academy of Sciences:

Jin Gao, Institute of Automation, Chinese Academy of Sciences:

Weiming Hu,:

【Object tracking】SINT++: Robust Visual Tracking via Adversarial Hard Positive Generation

Xiao Wang, Anhui university:

Chenglong Li, Anhui University:

Bin Luo,:

Jin Tang,:

【Object tracking】High-speed Tracking with Multi-kernel Correlation Filters

Ming Tang, NLPR, IA, CAS:

Bin Yu, NLPR, IA, CAS:

Fan Zhang, BUPT:

Jinqiao Wang,:

【Object tracking】Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking

Feng Li, Harbin Institute of Technology:

Cheng Tian, Harbin Institute of Technology:

Wangmeng Zuo, Harbin Institute of Technology:

Lei Zhang, The Hong Kong Polytechnic University: http://www4.comp.polyu.edu.hk/~cslzhang/

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Object tracking】A Benchmark for Articulated Human Pose Estimation and Tracking

Mykhaylo Andriluka, MPI Informatics:

Umar Iqbal,:

Eldar Insafutdinov, MPI Informatics:

Anton Milan, University of Adelaide:

Leonid Pishchulin, MPI Informatik:

Juergen Gall, University of Bonn, Germany: http://www.iai.uni-bonn.de/~gall/

Bernt Schiele, MPI Informatics Germany: http://www.d2.mpi-inf.mpg.de/schiele/

【Object tracking】Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes

Weihong Ren, City University of Hong Kong:

Di Kang,:

Yandong Tang, Shenyang Institute of Automation, Chinese Academy of Sciences:

Antoni Chan, City University of Hong Kong, Hong Kong:

【Object tracking】Tracking Multiple Objects Outside the Line of Sight using Speckle Imaging

Brandon Smith, University of Wisconsin-Madiso:

Matthew O’Toole, Stanford University:

Mohit Gupta, Wisconsin:

【Object tracking】Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies

Hanbyul Joo, CMU:

Tomas Simon, Oculus Research:

Yaser Sheikh: http://www.cs.cmu.edu/~yaser/

【Object tracking】Learning Spatial-Aware Regressions for Visual Tracking

Chong Sun, Dalian Universityof Technology:

Dong Wang, DUT:

Huchuan Lu, Dalian University of Technology: http://ice.dlut.edu.cn/lu/index.html

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Object tracking】High Performance Visual Tracking with Siamese Region Proposal Network

Bo Li, Sense Time:

Wei Wu,:

Zheng Zhu, Institute of Automation, CAS:

Junjie Yan,:

【Object tracking】VITAL: VIsual Tracking via Adversarial Learning

Yibing Song, Tencent AI Lab:

Chao Ma,:

Xiaohe Wu, Harbin Institute of technology:

Lijun Gong, City University of Hong Kong:

Linchao Bao, Tencent AI Lab:

Wangmeng Zuo, Harbin Institute of Technology:

Chunhua Shen, University of Adelaide:

Rynson Lau, City University of Hong Kong:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Action recognition】First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations

Guillermo Garcia-Hernando, Imperial College London:

Shanxin Yuan, Imperial College London:

Seungryul Baek, Imperial College London:

Tae-Kyun Kim, Imperial College London:

【Action recognition】Mi CT: Mixed 3D/2D Convolutional Tube for Human Action Recognition

Yizhou Zhou, Univ of Scienc.&Tech. of China:

Xiaoyan Sun, Microsoft:

Zheng-Jun Zha,:

Wenjun Zeng,:

【Action recognition】Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points

Fabien Baradel, LIRIS, INSA-Lyon:

Christian Wolf, INRIA, INSA-Lyon, CITI, LIRIS:

Julien Mille, INSA Val de Loire:

Graham Taylor, University of Guelph: http://cs.nyu.edu/~gwtaylor/

【Action recognition】Learning to Act Properly: Predicting and Explaining Affordances from Images

Ching-Yao Chuang, University of Toronto:

Jiaman Li, University of Toronto:

Antonio Torralba, MIT: http://web.mit.edu/torralba/www/

Sanja Fidler,:

【Action recognition】Rethinking the Faster R-CNN Architecture for Temporal Action Localization

Yu-Wei Chao, University of Michigan:

Sudheendra Vijayanarasimhan, Google Research:

Bryan Seybold, Google Research:

David Ross, Google Research:

Jia Deng,:

Rahul Sukthankar, Google Research:

【Action recognition】Recognizing Human Actions as Evolution of Pose Estimation Maps

Mengyuan Liu, Nanyang Technological University:

Junsong Yuan,:

【Action recognition】Optical Flow Guided Feature: A Motion Representation for Video Action Recognition

Shuyang Sun, The University of Sydney:

Zhanghui Kuang, Sense Time:

Wanli Ouyang, The University of Sydney: http://www.ee.cuhk.edu.hk/~wlouyang/

Lu Sheng, The Chinese University of HK:

Wei Zhang,:

【Action recognition】One-shot Action Localization by Sequence Matching Network

Hongtao Yang, Australian National University:

Xuming He, Shanghai Tech: http://users.cecs.anu.edu.au/~hexm/

Fatih Porikli, NICTA, Australia: http://www.porikli.com/

【Action recognition】Human Pose Estimation with Parsing Induced Learner

Xuecheng Nie, National University of Singapo:

Jiashi Feng,:

Yiming Zuo, Tsinghua University:

Shuicheng Yan,: http://www.lv-nus.org/index.html

【Action recognition】3D Human Pose Reconstruction and Action Classification in Robot Assisted Therapy of Children with Autism

Elisabeta Marinoiu, IMAR and Lund University:

Mihai Zanfir, IMAR and Lund University:

Vlad Olaru,:

Cristian Sminchisescu,:

【Action recognition】Non-Linear Temporal Subspace Representations for Activity Recognition

Anoop Cherian,:

Suvrit Sra, MIT:

Stephen Gould, Australian National University: http://users.cecs.anu.edu.au/~sgould/index.html

Richard Hartley, Australian National University Australia: http://users.cecs.anu.edu.au/~hartley/

【Action recognition】Multiple Granularity Group Interaction Prediction

Taiping Yao, Shanghai Jiaotong University:

Minsi Wang, Shanghai Jiao Tong University:

Huawei Wei, Shanghai Jiao Tong University:

Bingbing Ni,:

Xiaokang Yang,:

【Action recognition】Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

Agrim Gupta, Stanford University:

Justin Johnson, Stanford University:

Fei-Fei Li, Stanford University: http://vision.stanford.edu/resources_links.html

Silvio Savarese,: http://cvgl.stanford.edu/silvio/

Alexandre Alahi, EPFL:

【Action recognition】Coding Kendall’s Shape Trajectories for 3D Action Recognition

Amor Ben Tanfous, IMT Lille Douai:

Hassen Drira, IMT Lille Douai:

Boulbaba Ben Amor, IMT Lille Douai:

【Action recognition】Active Fixation Control to Predict Saccade Sequences

Calden Wloka, York University:

Iuliia Kotseruba, York University:

John Tsotsos, York University Canada:

【Action recognition】Who Let The Dogs Out? Modeling Dog Behavior From Visual Data

KIANA EHSANI, 1993:

Hessam Bagherinezhad, University of Washington:

Joe Redmon, University of Washington:

Roozbeh Mottaghi, Allen Institute for Artificial Intelligence: http://www.cs.stanford.edu/~roozbeh/

Ali Farhadi,: http://homes.cs.washington.edu/~ali/index.html

【Action recognition】Object Referring in Videos with Language and Human Gaze

Arun Balajee Vasudevan , ETH Zurich:

Dengxin Dai, ETH Zurich:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

【Action recognition】Soccer on Your Tabletop

Konstantinos Rematas, University of Washington:

Ira Kemelmacher,:

Brian Curless, Washington: http://homes.cs.washington.edu/~curless/

Steve Seitz, Washington/Google:

【Action recognition】From Lifestyle VLOGs to Everyday Interactions

David Fouhey, UC Berkeley:

WEICHENG KUO, Berkeley:

Alexei Efros, UC Berkeley: http://www.cs.cmu.edu/~efros/

Jitendra Malik,: http://www.cs.berkeley.edu/~malik/

【Action recognition】2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning

Diogo Luvizon, ETIS Lab:

David Picard, ETIS /LIP6:

Hedi Tabia, ETIS / ENSEA:

【Action recognition】LSTM Pose Machines

Yue Luo, Sense Time:

Jimmy Ren, Sense Time Group Limited:

Zhouxia Wang, Sense Time:

Wenxiu Sun, Sense Time Group Limited:

Jinshan Pan, UC Merced:

Jianbo Liu, Sense Time:

Jiahao Pang, Sense Time Group Limited:

Liang Lin,: http://ss.sysu.edu.cn/~ll/index.html

【Action recognition】Learning Latent Super-Events to Detect Multiple Activities in Videos

AJ Piergiovanni, Indiana University:

Michael Ryoo, Indiana University:

【Action recognition】Temporal Hallucinating for Action Recognition with Few Still Images

Lei Zhou,:

Yali Wang, SIAT, CAS:

Yu Qiao, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences:

【Action recognition】Deep Progressive Reinforcement Learning for Skeleton-based Action Recognition

Yansong Tang, Tsinghua University:

Yi Tian,:

Peiyang Li,:

Jiwen Lu, Tsinghua University:

Jie Zhou,:

【Action recognition】When will you do what? – Anticipating Temporal Occurrences of Activities

Alexander Richard, University of Bonn:

Juergen Gall, University of Bonn, Germany: http://www.iai.uni-bonn.de/~gall/

Yazan Abu Farha, University of Bonn:

【Action recognition】Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars

Antonio Loquercio, University of Zurich:

Ana Maqueda, Universidad Politecnica de Madrid:

Guillermo Gallego, University of Zurich:

Narciso Garcia, Universidad Politecnica de Madrid:

Davide Scaramuzza, University of Zurich:

【Action recognition】Im2Flow: Motion Hallucination from Static Images for Action Recognition

Ruohan Gao, University of Texas at Austin:

Bo Xiong, UT-Austin:

Kristen Grauman,: http://www.cs.utexas.edu/~grauman/

【Action recognition】Egocentric Activity Recognition on a Budget

Rafael Possas, University of Sydney:

Sheila Maricela Pinto Caceres, University of Sydney:

Fabio Ramos, University of Sydney:

【Action recognition】Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints

Alexander Richard, University of Bonn:

Hilde Kuehne, University of Bonn:

Juergen Gall, University of Bonn, Germany: http://www.iai.uni-bonn.de/~gall/

【Action recognition】Compressed Video Action Recognition

Chao-Yuan Wu, UT Austin:

Manzil Zaheer, Carnegie Mellon University:

Hexiang Hu,:

  1. Manmatha, A9:

Alexander Smola,:

Philipp Krahenbuhl,:

【Action recognition】AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions

Chunhui Gu, Google:

Chen Sun, Google:

David Ross, Google Research:

Carl Vondrick, Google:

Caroline Pantofaru, Google:

Yeqng Li, Google Inc.:

Sudheendra Vijayanarasimhan, Google Research:

George Toderici, Google:

Susanna Ricco, Google:

Rahul Sukthankar, Google Research:

Cordelia Schmid, INRIA Grenoble, France: http://lear.inrialpes.fr/~schmid/

Jitendra Malik,: http://www.cs.berkeley.edu/~malik/

【Action recognition】A Closer Look at Spatiotemporal Convolutions for Action Recognition

Du Tran, Dartmouth College:

heng Wang,:

Lorenzo Torresani, Darthmout College, USA:

Jamie Ray, Facebook:

Manohar Paluri,:

【Action recognition】Recognize Actions by Disentangling Components of Dynamics

Yue Zhao, CUHK:

Yuanjun Xiong, Amazon:

Dahua Lin, CUHK: http://dahua.me/

【Action recognition】Weakly Supervised Action Localization by Sparse Temporal Pooling Network

Phuc Nguyen, University of California, Irvine:

Ting Liu, Google, Inc.:

Gautam Prasad, Google, Inc.:

Bohyung Han, Seoul National University: http://cvlab.postech.ac.kr/~bhhan/

【Action recognition】Pose Flow: A Deep Motion Representation for Understanding Human Behaviors in Videos

Dingwen Zhang,:

Guangyu Guo,:

Dong Huang, Carnegie Mellon University:

Fernando de la Torre,:

Junwei Han, Northwestern Polytechnical U.:

【Action recognition】Po Tion: Pose Mo Tion Representation for Action Recognition

Vasileios Choutas, Naver Labs Europe:

Philippe Weinzaepfel, Xerox:

Jerome Revaud, Naver Labs Europe: http://lear.inrialpes.fr/people/revaud/

Cordelia Schmid, INRIA Grenoble, France: http://lear.inrialpes.fr/~schmid/

【Action recognition】Pulling Actions out of Context: Explicit Separation for Effective Combination

Yang Wang, Stony Brook University:

Minh Hoai, Stony Brook University:

【Action recognition】Through-Wall Human Pose Estimation Using Radio Signals

Mingmin Zhao, MIT:

Tianhong Li, MIT:

Mohammad Abu Alsheikh, MIT:

Yonglong Tian, Massachusetts Institute of Technology:

Hang Zhao, MIT:

Antonio Torralba, MIT: http://web.mit.edu/torralba/www/

Dina Katabi, MIT:

【Action recognition】What have we learned from deep representations for action recognition?

Christoph Feichtenhofer,:

Axel Pinz, Graz University of Technology:

Richard Wildes, York University:

Andrew Zisserman, Oxford: http://www.robots.ox.ac.uk/~vgg/

【Action recognition】Virtual Home: Simulating Household Activities via Programs

Xavier Puig, MIT:

Kevin Ra,:

Marko Boben,:

Jiaman Li, University of Toronto:

Tingwu Wang,:

Sanja Fidler,:

Antonio Torralba, MIT: http://web.mit.edu/torralba/www/

【Action recognition】SSNet: Scale Selection Network for Online 3D Action Prediction

Jun Liu, Nanyang Technological University:

Amir Shahroudy, NTU Singapore:

Gang Wang,:

Ling-Yu Duan,:

Alex Kot,:

【Action recognition】Learning Monocular 3D Human Pose estimation on weakly-supervised Multi-view Images

Helge Rhodin, epfl.ch:

Jörg Spörri, Balgrist:

Isinsu Katircioglu, EPFL Lausanne, Switzerland:

Victor Constantin, EPFL:

Frédéric Meyer,:

Erich Müller,:

Mathieu Salzmann, EPFL:

Pascal Fua,: http://cvlabwww.epfl.ch/~fua/

【Action recognition】Embodied Real-World Active Perception

Fei Xia, Stanford University:

Amir Zamir, Stanford, UC Berkeley:

Zhi-Yang He, Stanford University:

Alexander Sax, Stanford University:

Jitendra Malik,: http://www.cs.berkeley.edu/~malik/

Silvio Savarese,: http://cvgl.stanford.edu/silvio/

【Action recognition】Towards Universal Representation for Unseen Action Recognition

Yi Zhu, University of California Merced:

Yang Long, Newcastle University:

Yu Guan, Newcastle University:

Shawn Newsam,:

Ling Shao, University of East Anglia: http://lshao.staff.shef.ac.uk/

【Crowd analysis】Lean Multiclass Crowdsourcing

Grant van Horn, California Institute of Technology:

Pietro Perona, California Institute of Technology, USA: http://vision.caltech.edu/Perona.html

Serge Belongie,: http://vision.ucsd.edu/person/serge-belongie

【Crowd analysis】Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN

Deepak Babu Sam, Indian Institute of Science:

Neeraj Sajjan, Indian Institute of Science:

Venkatesh Babu Radhakrishnan, Indian Institute of Science:

Mukundhan Srinivasan, NVIDIA:

【Crowd analysis】Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty

Apratim Bhattacharyya, MPI Informatics:

Bernt Schiele, MPI Informatics Germany: http://www.d2.mpi-inf.mpg.de/schiele/

Mario Fritz, MPI, Saarbrucken, Germany: https://scalable.mpi-inf.mpg.de/

【Crowd analysis】Crowd Counting via Adversarial Cross-Scale Consistency Pursuit

Zan Shen, Institute of Image Communication and Network Engineering, Shanghai Jiao Tong U:

Bingbing Ni,:

Yi Xu, Shanghai Jiao Tong University:

Minsi Wang, Shanghai Jiao Tong University:

jianguo Hu, Minivision:

Xiaokang Yang,:

【Crowd analysis】Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction

Yanyu Xu, Shanghaitech University:

Zhixin Piao,:

Shenghua Gao, Shanghai Tech University:

【Crowd analysis】Crowd Counting with Deep Negative Correlation Learning

Zenglin Shi, University of Bern:

Le Zhang, Advanced Digital Sciences Cent:

Xiao Feng Cao, university of technology sydney:

Yun Liu, Nankai University:

yangdong Ye, Zhengzhou University, China:

Guoyan Zheng, University of Bern:

【Crowd analysis】Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

Xialei Liu, Computer Vision Center of UAB:

Joost van de Weijer, Computer Vision Center Barcelona:

Andrew Bagdanov, Computer Vision Center, Barcelona:

【Video analysis】Recurrent Residual Module for Fast Inference in Videos

Bowen Pan, Shanghai Jiao Tong University:

Wuwei Lin, Shanghai Jiao Tong University:

Xiaolin Fang, Zhejiang University:

Chaoqin Huang, Shanghai Jiaotong University:

Bolei Zhou, Massachuate Institute of Technology:

Cewu Lu, Shanghai Jiao Tong University:

【Video analysis】Demo2Vec: Reasoning Object Affordances from Online Videos

Te-Lin Wu, USC:

Kuan Fang, Stanford University:

Daniel Yang, University of Southern California:

Joseph Lim, University of Southern California:

【Video analysis】Memory Based Online Learning of Deep Representations from Video Streams

Federico Pernici, MICC University of Florence:

federico Bartoli, Micc – University of Florence:

Matteo Bruni, Micc – University of Florence:

Alberto Del Bimbo, University of Florence:

【Video analysis】Video Captioning via Hierarchical Reinforcement Learning

Xin Wang, UCSB:

Wenhu Chen,:

Jiawei Wu, UCSB:

Yuan-Fang Wang, UCSB:

William Yang Wang, UCSB:

【Video analysis】Geometry-Guided CNN for Self-supervised Video Representation learning

Chuang Gan, Tsinghua University:

Boqing Gong, University of Central Florida:

Kun Liu, Beijing University of Posts and Telecommunications:

hao Su,:

Leonidas J. Guibas,:

【Video analysis】Li DAR-Video Driving Dataset: Learning Driving Policies Effectively

Yiping Chen, Xiamen University:

Jingkang Wang, Shanghai Jiao Tong University:

Cewu Lu, Shanghai Jiao Tong University:

Zhipeng Luo, Xiamen University:

Jonathan Li, University of Waterloo:

Han Xue, Shanghai Jiao Tong University:

Cheng Wang, Xiamen University:

【Video analysis】Finding It”: Weakly-Supervised Reference-Aware Visual Grounding in Instructional Video”

De-An Huang, Stanford University:

Shyamal Buch, Stanford University:

Lucio Dery, Stanford University:

Animesh Garg, Stanford University:

Fei-Fei Li, Stanford University: http://vision.stanford.edu/resources_links.html

Juan Carlos Niebles, Stanford University:

【Video analysis】Low-Latency Video Semantic Segmentation

Yule Li, Ict:

Jianping Shi, Sense Time:

Dahua Lin, CUHK: http://dahua.me/

【Video analysis】End-to-End Learning of Motion Representation for Video Understanding

Lijie Fan, Tsinghua University:

Wenbing Huang, Tencent AI Lab:

Chuang Gan, Tsinghua University:

Stefano Ermon, Stanford University:

Junzhou Huang, UT Arlingtron:

Boqing Gong, University of Central Florida:

【Video analysis】MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses

Irtiza Hasan, University of Verona:

Francesco Setti,:

Theodore Tsesmelis,:

Alessio Del Bue, Istituto Italiano di Tecnologia (IIT):

Fabio Galasso,:

Marco Cristani, U. Verona:

【Video analysis】Inferring Co-Attention in Social Scene Videos

Lifeng Fan, VCLA@UCLA:

Yixin Chen, VCLA@UCLA:

Ping Wei, Xi’an Jiaotong University:

Song-Chun Zhu,: http://www.stat.ucla.edu/~sczhu/

【Video analysis】Future Frame Prediction for Anomaly Detection A New Baseline

Wen Liu, Shanghai Tech University:

Weixin Luo, Shanghaitech University:

Dongze Lian, Shanghai Tech University:

Shenghua Gao, Shanghai Tech University:

【Video analysis】Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and Image Net?

Kensho Hara, AIST:

Hirokatsu Kataoka, AIST:

Yutaka Satoh, AIST:

【Video analysis】Motion-Appearance Co-Memory Networks for Video Question Answering

Jiyang Gao,:

Runzhou Ge, Univ. of Southern California:

Kan Chen, Univ. of Southern California:

Ram Nevatia,: http://iris.usc.edu/USC-Computer-Vision.html

【Video analysis】FFNet: Video Fast-Forwarding via Reinforcement Learning

Shuyue Lan, Northwestern University:

Rameswar Panda, UC Riverside:

Qi Zhu, UC Riverside:

Amit Roy-Chowdhury, UC Riverside:

【Video analysis】Attend and Interact: Higher-Order Object Interactions for Video Understanding

CHIH-YAO MA, GEORGIA TECH:

Asim Kadav, NEC Labs:

Iain Melvin,:

Zsolt Kira,:

Ghassan Al Regib,:

Hans Peter Graf,:

【Video analysis】Interpretable Video Captioning via Trajectory Structured Localization

Xian Wu, Sysu:

Guanbin Li,:

Liang Lin,: http://ss.sysu.edu.cn/~ll/index.html

【Video analysis】Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning

Jingwen Wang, SCUT:

Wenhao Jiang, Tencent AI Lab:

Lin Ma, Tencent AI Lab:

Wei Liu,:

Yong Xu, South China University of Technology:

【Video analysis】Towards High Performance Video Object Detection

Xizhou Zhu,:

Jifeng Dai, Microsoft Research:

Lu Yuan, Microsoft Research Asia: http://research.microsoft.com/en-us/um/people/luyuan/index.htm

Yichen Wei, Microsoft Research Asia:

【Video analysis】What Makes a Video a Video: Analyzing Temporal Information in Video Understanding Models and Datasets

De-An Huang, Stanford University:

Vignesh Ramanathan, Facebook:

Dhruv Mahajan,:

Juan Carlos Niebles, Stanford University:

Fei-Fei Li, Stanford University: http://vision.stanford.edu/resources_links.html

Lorenzo Torresani, Darthmout College, USA:

Manohar Paluri,:

【Video analysis】Neural Network-Viterbi: A Framework for Weakly Supervised Video Learning

Alexander Richard, University of Bonn:

Hilde Kuehne, University of Bonn:

Ahsan Iqbal, University of Bonn:

Juergen Gall, University of Bonn, Germany: http://www.iai.uni-bonn.de/~gall/

【Video analysis】Actor and Observer: Joint Modeling of First and Third-Person Videos

Gunnar Sigurdsson, CMU:

Cordelia Schmid, INRIA Grenoble, France: http://lear.inrialpes.fr/~schmid/

Ali Farhadi,: http://homes.cs.washington.edu/~ali/index.html

Abhinav Gupta,: http://www.cs.cmu.edu/~abhinavg/

Karteek Alahari,:

【Video analysis】HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization

Bin Zhao, Northwestern Polytechnical Uni:

Xuelong Li,:

Xiaoqiang Lu,:

【Video analysis】Viewpoint-aware Video Summarization

Atsushi Kanehira, University of Tokyo:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

Yoshitaka Ushiku,:

Tatsuya Harada, University of Tokyo:

【Video analysis】Reconstruction Network for Video Captioning

Bairui Wang,:

Lin Ma, Tencent AI Lab:

Wei Zhang,:

Wei Liu,:

【Video analysis】LAMV: Learning to align and match videos with kernelized temporal layers

Lorenzo Baraldi, University of Modena:

Matthijs Douze,:

Rita Cucchiara,:

Herve Jegou, Facebook AI Research:

【Video analysis】Optimizing Video Object Detection via a Scale-Time Lattice

Kai Chen, CUHK:

Jiaqi Wang, CUHK:

Shuo Yang,:

Xingcheng Zhang, CUHK:

Yuanjun Xiong, Amazon:

Chen-Change Loy, the Chinese University of Hong Kong:

Dahua Lin, CUHK: http://dahua.me/

【Video analysis】Learning Compressible 360° Video Isomers

Yu-Chuan Su, UT Austin:

Kristen Grauman,: http://www.cs.utexas.edu/~grauman/

【Human detection】Person Transfer GAN to Bridge Domain Gap for Person Re-Identification

Longhui Wei, Peking University:

Shiliang Zhang, Peking University:

Wen Gao,: http://www.jdl.ac.cn/

Qi Tian,: http://www.cs.utsa.edu/~qitian/

【Human detection】Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification

Shuang Li, The Chinese University of HK:

Slawomir Bak, Disney Research:

Peter Carr, Disney Research:

【Human detection】A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking

  1. Saquib Sarfraz, KIT:

Arne Schumann, KIT:

Andreas Eberle, KIT:

Rainer Stiefelhagen, Karlsruhe Institute of Technology:

【Human detection】Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification

Weijian Deng, University of Chinese Academy:

Liang Zheng, University of Texas at San Ant:

GUOLIANG KANG, UTS:

Yi Yang,: http://www.cs.cmu.edu/~yiyang/

Qixiang Ye,:

Jianbin Jiao,:

【Human detection】Video Person Re-identification with Competitive Snippet-similarity Aggregation and Co-attentive Snippet Embedding

Dapeng Chen, CUHK:

Hongsheng Li,:

Tong Xiao, The Chinese University of HK:

Shuai Yi, The Chinese University of Hong Kong:

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

【Human detection】Mask-guided Contrastive Attention Model for Person Re-Identification

Chunfeng Song, CASIA:

Yan Huang,:

Wanli Ouyang,: http://www.ee.cuhk.edu.hk/~wlouyang/

Liang Wang, unknown:

【Human detection】Person Re-identification with Cascaded Pairwise Convolutions

Yicheng Wang,:

Zhenzhong Chen, Wuhan University:

Feng Wu,:

Gang Wang,:

【Human detection】Multi-Level Factorisation Net for Person Re-Identification

Xiaobin Chang, Queen Mary Univ. of London:

Timothy Hospedales, University of Edinburgh:

Tao Xiang, Queen Mary University of London:

【Human detection】Attention-aware Compositional Network for Person Re-Identification

Jing Xu, Sense Nets Technology Limited:

Rui Zhao, Sense Nets Technology Limited:

Feng Zhu, Sense Nets Technology Limited:

Huaming Wang, Sense Nets Technology Limited:

Wanli Ouyang, The University of Sydney: http://www.ee.cuhk.edu.hk/~wlouyang/

【Human detection】Unifying Identification and Context Learning for Person Recognition

Qingqiu Huang, CUHK:

Yu Xiong, CUHK:

Dahua Lin, CUHK: http://dahua.me/

【Human detection】Deep Group-shuffling Random Walk for Person Re-identification

Yantao Shen, CUHK:

Hongsheng Li,:

Tong Xiao, The Chinese University of HK:

Shuai Yi, The Chinese University of Hong Kong:

Dapeng Chen, CUHK:

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

【Human detection】Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification

Jingya Wang, QMUL:

Xiatian Zhu, Vision Semantics Ltd.:

Shaogang Gong, Queen Mary University: http://www.eecs.qmul.ac.uk/~sgg/

Wei Li, Queen Mary University of Lond:

【Human detection】Harmonious Attention Network for Person Re-Identication

Wei Li, Queen Mary University of Lond:

Xiatian Zhu, Vision Semantics Ltd.:

Shaogang Gong, Queen Mary University: http://www.eecs.qmul.ac.uk/~sgg/

【Human detection】Efficient and Deep Person Re-Identification using Multi-Level Similarity

Yiluan Guo, SUTD:

Ngai-Man Cheung,:

【Human detection】Pose Transferrable Person Re-Identification

Jinxian Liu, Shanghai Jiao Tong University:

Yichao Yan, Shanghai Jiao Tong University:

Bingbing Ni,:

Peng Zhou, Sjtu:

Shuo Cheng, SJTU:

jianguo Hu, Minivision:

【Human detection】Name-removed-for-review: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection

Tatjana Chavdarova, Idiap and EPFL:

Pierre Baqué, EPFL:

Andrii Maksai,:

STÉPHANE BOUQUET, EPFL:

Cijo Jose, Idiap and EPFL:

Louis Lettry, ETH Zürich:

Francois Fleuret, Idiap Research Institute:

Pascal Fua,: http://cvlabwww.epfl.ch/~fua/

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

【Human detection】Adversarially Occluded Samples for Person Re-identification

Houjing Huang, CASIA:

Dangwei Li,:

Zhang Zhang,:

Xiaotang Chen,:

Kaiqi Huang,:

【Human detection】Camera Style Adaptation for Person Re-identification

Zhun Zhong, Xiamen University:

Liang Zheng, University of Texas at San Ant:

Zhedong Zheng, UTS:

Shaozi Li,:

Yi Yang, University of Technology, Sydney: http://www.cs.cmu.edu/~yiyang/

【Human detection】Exploit the Unknown Gradually:~ One-Shot Video-Based Person Re-Identification by Stepwise Learning

Yu Wu, University of technology sydne:

Yutian Lin,:

Xuanyi Dong, UTS:

Yan Yan, UTS:

Wanli Ouyang, The University of Sydney: http://www.ee.cuhk.edu.hk/~wlouyang/

Yi Yang,: http://www.cs.cmu.edu/~yiyang/

【Human detection】Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification

Jianlou Si, BUPT:

Honggang Zhang,:

Chun-Guang Li, Beijing Univ. of Posts&Telecom:

Jason Kuen, NTU, Singapore:

Xiangfei Kong, Nanyang Technological University:

Alex Kot,:

Gang Wang,:

【Human detection】Easy Identification from Better Constraints: Multi-Shot Person Re-Identification from Reference Constraints

Jiahuan Zhou, Northwestern University:

Bing Su, Chinese Academy of Sciences:

Ying Wu, Northwestern University, USA:

【Human detection】Eliminating Background-bias for Robust Person Re-identification

Maoqing Tian, Sensetime Limited:

Shuai Yi, The Chinese University of Hong Kong:

Hongsheng Li,:

Shihua Li,:

Xuesen Zhang, Sense Time:

Jianping Shi, Sense Time:

Junjie Yan,:

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

【Human detection】Multi-shot Pedestrian Re-identification via Sequential Decision Making

Jianfu Zhang, Shanghai Jiaotong University:

Naiyan Wang, tusimple:

Liqing Zhang, Shanghai Jiaotong University: http://bcmi.sjtu.edu.cn/~zhangliqing/

【Human detection】End-to-End Deep Kronecker-Product Matching for Person Re-identification

Yantao Shen, CUHK:

Tong Xiao, The Chinese University of HK:

Hongsheng Li,:

Shuai Yi, The Chinese University of Hong Kong:

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

【Human detection】Occluded Pedestrian Detection through Guided Attention in CNNs

Shanshan Zhang, MPI:

Jian Yang, Nanjing University of Science and Technology:

Bernt Schiele, MPI Informatics Germany: http://www.d2.mpi-inf.mpg.de/schiele/

【Human detection】Exploiting Transitivity for Learning Person Re-identification Models on a Budget

Sourya Roy, UC Riverside:

Sujoy Paul, UC Riverside:

Neal Young, UC Riverside:

Amit Roy-Chowdhury, UC Riverside:

【Human detection】Deep Spatial Feature Reconstruction for Partial Person Re-identification

Lingxiao He, Institute of Automation Chines:

Jian Liang, CASIA:

Haiqing Li,:

Zhenan Sun, CRIPAC:

【Human detection】Future Person Localization in First-Person Videos

Takuma Yagi, The University of Tokyo:

Karttikeya Mangalam, IIT Kanpur:

Ryo Yonetani, The University of Tokyo:

Yoichi Sato, Univ of Tokyo:

【Human detection】Repulsion Loss: Detecting Pedestrians in a Crowd

Xinlong Wang, Tongji University:

Tete Xiao, Peking University:

Yuning Jiang, Megvii inc.:

Shuai Shao, Megvii:

Jian Sun,: http://research.microsoft.com/en-us/groups/vc/

Chunhua Shen, University of Adelaide:

【Human detection】Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns

Jianming Lv, South China University of Technology:

Weihang Chen, South China University of Technology:

Qing Li, City University of Hong Kong:

Can Yang, South China University of Technology:

【Human detection】Resource Aware Person Re-identification across Multiple Resolutions

Yan Wang, Cornell university:

Lequn Wang, Cornell University:

yurong you, shang hai jiao tong university:

xu zou, tsinghua university:

Vincent Chen, cornell university:

Serena Li, CORNELL UNIVERSITY:

Bharath Hariharan, Cornell University:

Gao Huang,:

Kilian Weinberger, Cornell University:

【Human detection】Group Consistent Similarity Learning via Deep CRFs for Person Re-Identification

Dapeng Chen, CUHK:

Dan Xu,:

Hongsheng Li,:

Nicu Sebe, University of Trento, Italy:

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

【Human parsing】Weakly Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer

Hao-Shu Fang, Shanghai Jiao Tong University:

Guansong Lu, Shanghai Jiao Tong University:

Xiaolin Fang, Zhejiang University:

Yu-Wing Tai, Tencent You Tu:

Cewu Lu, Shanghai Jiao Tong University:

【Human parsing】Cross-modal Deep Variational Hand Pose Estimation

Adrian Spurr, ETH Zurich:

Jie Song, ETHZ:

Seonwook Park, ETH Zurich:

Otmar HIlliges, ETH Zurich:

【Human parsing】Learning to Estimate 3D Human Pose and Shape from a Single Color Image

Georgios Pavlakos,:

Luyang Zhu, Peking University:

Xiaowei Zhou, Zhejiang University:

Kostas Daniilidis, University of Pennsylvania:

【Human parsing】Improved Human Pose Estimation through Adversarial Data Augmentation

Zhiqiang Tang, Rutgers:

Xi Peng,:

Fei Yang, facebook:

Rogerio Feris, IBM: http://rogerioferis.com/

Dimitris Metaxas, Rutgers:

【Human parsing】Attentive Fashion Grammar Network for Fashion Landmark Detection and Clothing Category Classification

Wenguan Wang, Beijing Institute of Technology:

Yuanlu Xu, University of California, Los Angeles:

Jianbing Shen, Beijing Institute of Technolog: http://cs.bit.edu.cn/shenjianbing/

Song-Chun Zhu,: http://www.stat.ucla.edu/~sczhu/

【Human parsing】V2V-Pose Net: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map

Gyeongsik Moon, Seoul National University:

Ju Yong Chang, Kwangwoon University:

Kyoung Mu Lee,: http://cv.snu.ac.kr/kmlee/

【Human parsing】Dense 3D Regression for Hand Pose Estimation

Chengde Wan,:

Thomas Probst,:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

Angela Yao, University of Bonn:

【Human parsing】Convolutional Sequence to Sequence Model for Human Dynamics

Chen Li,:

Zhen Zhang, National University of Singapore:

Wee Sun Lee,:

Gim Hee Lee, National Univeristy of Singapore:

【Human parsing】Gesture Recognition: Focus on the Hands

Pradyumna Narayana, Colorado State University:

Ross Beveridge, Colorado State University:

Bruce Draper, Colorado State University:

【Human parsing】3D Human Pose Estimation in the Wild by Adversarial Learning

Wei Yang, The Chinese University of Hong Kong:

Wanli Ouyang, The University of Sydney: http://www.ee.cuhk.edu.hk/~wlouyang/

Xiaolong Wang, Carnegie Mellon University:

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

【Human parsing】Human Appearance Transfer

Mihai Zanfir, IMAR and Lund University:

Alin-Ionut Popa, IMAR:

Andrei Zanfir, IMAR and Lund University:

Cristian Sminchisescu,:

【Human parsing】Cascaded Pyramid Network for Multi-Person Pose Estimation

Yilun Chen, Beihang University:

Zhicheng Wang, Megvii(Face++):

Yuxiang Peng, Tsinghua University:

Zhiqiang Zhang, HUST:

Gang Yu, Face++:

Jian Sun,: http://research.microsoft.com/en-us/groups/vc/

【Human parsing】End-to-end Recovery of Human Shape and Pose

Angjoo Kanazawa, University of Maryland:

Michael Black, Max Planck Institute for Intelligent Systems: http://ps.is.tue.mpg.de/person/black

David Jacobs, University of Maryland:

Jitendra Malik,: http://www.cs.berkeley.edu/~malik/

【Human parsing】Double Fusion: Real-time Capture of Human Performance with Inner Body Shape from a Single Depth Sensor

Tao Yu, Beihang University:

Zerong Zheng, Tsinghua University:

Kaiwen Guo, Google:

Jianhui Zhao, Beihang University:

Qionghai Dai,: http://media.au.tsinghua.edu.cn/people.jsp

Hao Li,:

Gerard Pons-Moll, Max Planck for Informatics:

Yebin Liu, Tsinghua University: http://media.au.tsinghua.edu.cn/liuyebin.jsp

【Human parsing】Dense Pose: Multi-Person Dense Human Pose Estimation In The Wild

Alp Guler, INRIA:

Natalia Neverova, Facebook AI Research:

Iasonas Kokkinos, FAIR/UCL: http://cvn.ecp.fr/personnel/iasonas/index.html

【Human parsing】Ordinal Depth Supervision for 3D Human Pose Estimation

Georgios Pavlakos,:

Xiaowei Zhou, Zhejiang University:

Kostas Daniilidis, University of Pennsylvania:

【Human parsing】Audio to Body Dynamics

Eli Shlizerman, Facebook:

Lucio Dery, Stanford:

Hayden Schoen, Facebook:

Ira Kemelmacher,:

【Human parsing】Neural Sign Language Translation

Necati Cihan Camgoz, CVSSP:

Simon Hadfield,:

Richard Bowden, University of Surrey UK:

Oscar Koller,:

Hermann Ney,:

【Face recognition】Finding Tiny Faces in the Wild with Generative Adversarial Network

Yancheng Bai, Kaust/Iscas:

Yongqiang Zhang, Harbin institute of Technology/KAUST:

Mingli Ding,:

Bernard Ghanem,:

【Face recognition】Learning Face Age Progression: A Pyramid Architecture of GANs

Hongyu Yang, BEIHANG UNIVERSITY:

Di Huang,:

Yunhong Wang,: http://irip.buaa.edu.cn/Chinese.html

Anil Jain, MSU:

【Face recognition】Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation

Kai Li, Chinese Academy of Sciences:

Junliang Xing, Institute of Automation, Chinese Academy of Sciences:

Chi Su, King Soft:

Weiming Hu,:

Yundong Zhang, Vimicro Corporation:

Stephen Maybank, Birkbeck University of London:

【Face recognition】Learning from Millions of 3D Scans for Large-scale 3D Face Recognition

Syed Zulqarnain Gilani, The University of Western Aust:

Ajmal Mian, UWA:

【Face recognition】Facial Expression Recognition by De-expression Residue Learning

Huiyuan Yang, Binghamton University-SUNY:

Umur Ciftci, Binghamton University-SUNY:

Lijun Yin, Binghamton University State University of New York:

【Face recognition】Towards Pose Invariant Face Recognition in the Wild

Jian Zhao, NUS:

Yu Cheng, Nanyang Technological University:

Yan Xu, Core Technology Group, Learning & Vision, Panasonic R&D Center Singapore:

Lin Xiong, Core Technology Group, Learning & Vision, Panasonic R&D Center Singapore:

Jianshu Li, National University of Singapo:

Fang Zhao, National University of Singapore:

Karlekar Jayashree, Core Technology Group, Learning & Vision, Panasonic R&D Center Singapore:

Sugiri Pranata, Core Technology Group, Learning & Vision, Panasonic R&D Center Singapore:

Shengmei Shen, Core Technology Group, Learning & Vision, Panasonic R&D Center Singapore:

Junliang Xing, Institute of Automation, Chinese Academy of Sciences:

Shuicheng Yan, National University of Singapore: http://www.lv-nus.org/index.html

Jiashi Feng,:

【Face recognition】Deep Regression Forests for Age Estimation

Wei Shen, Shanghai University:

Yilu Guo, Shanghai University:

Yan Wang, JHU:

KAI ZHAO, Nankai University:

Bo Wang, Hik Vision USA Inc.:

Alan Yuille,: http://www.stat.ucla.edu/~yuille/

【Face recognition】Joint Pose and Expression Modeling for Facial Expression Recognition

Feifei Zhang, Jiangsu University:

Tianzhu Zhang, CASIA:

Qirong Mao, Department of Computer Science and Communication Engineering, Jiangsu University:

Changsheng Xu,:

【Face recognition】Partially Shared Multi-Task Convolutional Neural Network with Local Constraint for Face Attribute Learning

Jiajiong Cao,:

Yingming Li, Zhejiang University:

Zhongfei Zhang,:

【Face recognition】Ring loss: Convex Feature Normalization for Face Recognition

Yutong Zheng, Carnegie Mellon University:

Dipan Pal, Carnegie Mellon University:

Marios Savvides,:

【Face recognition】Pose-Robust Face Recognition via Deep Residual Equivariant Mapping

Kaidi Cao, Tsinghua University:

Yu Rong, CUHK:

Cheng Li, Sense Time:

Chen-Change Loy, the Chinese University of Hong Kong:

【Face recognition】Cos Face: Large Margin Cosine Loss for Deep Face Recognition

Hao Wang,:

Yitong Wang, Tencent AI Lab:

Zheng Zhou,:

xing Ji,:

Dihong Gong,:

Zhifeng Li,:

Jingchao Zhou,:

Wei Liu,:

【Face recognition】Mean-Variance Loss for Deep Age Estimation from a Face

Hongyu Pan, Institute of Computing Technol:

Hu Han,:

Shiguang Shan, Chinese Academy of Sciences: http://vipl.ict.ac.cn/members/sgshan

Xilin Chen,:

【Face recognition】Dynamic Feature Learning for Partial Face Recognition

Lingxiao He, Institute of Automation Chines:

Haiqing Li,:

qi Zhang,:

Zhenan Sun, CRIPAC:

【Face recognition】UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition

Jiankang Deng, Imperial College London:

Shiyang Cheng, Imperial College London:

Niannan Xue, Imperial College London:

Yuxiang Zhou, Imperial College:

Stefanos Zafeiriou, Imperial College London:

【Face detection】Exploring Disentangled Feature Representation Beyond Face Identification

Yu Liu, CUHK:

Fangyin Wei, Peking University:

Jing Shao, The Sensetime Group Limited:

Lu Sheng, The Chinese University of HK:

Junjie Yan,:

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

【Face detection】Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks

Shi Xuepeng, ICT:

Shiguang Shan, Chinese Academy of Sciences: http://vipl.ict.ac.cn/members/sgshan

Meina Kan,:

Shuzhe Wu, Chinese Academy of Sciences:

Xilin Chen,:

【Face detection】Seeing Small Faces from Robust Anchor’s Perspective

Chenchen Zhu, Carnegie Mellon University:

Ran Tao, Carnegie Mellon University:

Khoa Luu,:

Marios Savvides,:

【Face detection】Face Detector Adaptation without Negative Transfer or Catastrophic Forgetting

Muhammad Abdullah Jamal, University of Central Florida:

Haoxiang Li, Adobe Research:

Boqing Gong, University of Central Florida:

【Face detection】Beyond Trade-off: Accelerate FCN-based Face Detection with Higher Accuracy

Guanglu Song, Beihang University:

Yu Liu, CUHK:

Ming Jiang, BUAA:

Yujie Wang, Beihang university:

【Face parsing】Integrated facial landmark localization and super-resolution of real-world very low resolution faces in arbitrary poses with GANs

Adrian Bulat,:

Georgios Tzimiropoulos,:

【Face parsing】Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors

Xuanyi Dong, UTS:

Shoou-I Yu, Oculus:

Xinshuo Weng, Carnegie Mellon University:

Shih-En Wei, Oculus Research:

Yi Yang,: http://www.cs.cmu.edu/~yiyang/

Yaser Sheikh,: http://www.cs.cmu.edu/~yaser/

【Face parsing】Style Aggregated Network for Facial Landmark Detection

Xuanyi Dong, UTS:

Yan Yan, UTS:

Wanli Ouyang, The University of Sydney: http://www.ee.cuhk.edu.hk/~wlouyang/

Yi Yang,: http://www.cs.cmu.edu/~yiyang/

【Face parsing】Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision

Yaojie Liu, Michigan State University:

Amin Jourabloo,:

Xiaoming Liu, Michigan State University:

【Face parsing】Disentangling 3D Pose in A Dendritic CNN for Unconstrained 2D Face Alignment

Amit Kumar, University of Maryland:

Rama Chellappa, University of Maryland, USA:

【Face parsing】A Hierarchical Generative Model for Eye Image Synthesis and Eye Gaze Estimation

Kang Wang, RPI:

Rui Zhao, Rensselaer Polytechnic Institu:

Qiang Ji, RPI: http://www.ecse.rpi.edu/~qji/

【Face parsing】Robust Facial Landmark Detection via a Fully-Convolutional Local-Global Context Network

Daniel Merget, Technical University of Munich:

Matthias Rock, TUM:

Rigoll Gerhard, TUM:

【Face parsing】Learning Facial Action Units from Web Images with Scalable Weakly Supervised Clustering

Kaili Zhao, Beijing University of Post & T:

Wen-Sheng Chu, Carnegie Mellon University:

Aleix Martinez, The ohio state university:

【Face parsing】Look at Boundary: A Boundary-Aware Face Alignment Algorithm

Wayne Wu, Sense Time:

Chen Qian, Sense Time:

Shuo Yang,:

Quan Wang, Sense Time:

【Face parsing】Weakly Supervised Facial Action Unit Recognition through Adversarial Training

Guozhu Peng, USTC:

Shangfei Wang,:

【Face parsing】Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

Zhenhua Feng, University of Surrey:

Muhammad Awais, university of surrey:

Josef Kittler,:

Patrik Huber, University of Surrey:

Xiaojun Wu, Jiangnan University:

【Face parsing】Weakly-supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation

Yong Zhang, CASIA:

Weiming Dong,:

Bao-Gang Hu, CASIA:

Qiang Ji, RPI: http://www.ecse.rpi.edu/~qji/

【Face parsing】Facelet-Bank for Fast Portrait Manipulation

Ying-Cong Chen, CUHK:

Lin Huaijia, the Chinese University of Hong Kong:

Ruiyu Li, CUHK:

Michelle Shu,:

Xin Tao, CUHK:

Yangang Ye, Tencent:

Xiaoyong Shen, CUHK:

Jiaya Jia, Chinese University of Hong Kong: http://www.cse.cuhk.edu.hk/leojia/

【Face parsing】Modeling Facial Geometry using Compositional VAEs

Timur Bagautdinov,:

Chenglei Wu, Oculus:

Jason Saragih, Oculus Research:

Pascal Fua,: http://cvlabwww.epfl.ch/~fua/

Yaser Sheikh,: http://www.cs.cmu.edu/~yaser/

【Face parsing】Direct Shape Regression Networks for End-to-End Face Alignment

Xin Miao, UT Arlington:

Xiantong Zhen, Beihang University:

Vassilis Athitsos, University of Texas at Arlington:

Xianglong Liu, Beihang University:

Cheng Deng, Xidian University:

Heng Huang, University of Pittsburgh:

【Face parsing】Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition

Shizhong Han, 1986:

zibo Meng,:

Zhiyuan Li, University of South Carolina:

JAMES O’REILLY, University of South Carolina:

Jie Cai, University of South Carolina:

Xiaofeng Wang, University of South Carolina:

Yan Tong, University of South Carolina:

【Face parsing】Classifier Learning with Prior Probabilities for Facial Action Unit Recognition

Yong Zhang, CASIA:

Weiming Dong,:

Bao-Gang Hu, CASIA:

Qiang Ji, RPI: http://www.ecse.rpi.edu/~qji/

【Face parsing】4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications

Shiyang Cheng, Imperial College London:

Irene Kotsia, Middlesex University London:

Maja Pantic, Imperial College London, UK: http://ibug.doc.ic.ac.uk/research

Stefanos Zafeiriou, Imperial College London:

【Face parsing】Gaze Prediction in Dynamic $360^\circ$ Immersive Videos

Yanyu Xu, Shanghaitech University:

Yanbing Dong,:

Junru Wu,:

Zhengzhong Sun,:

Zhiru Shi,:

Jingyi Yu,:

Shenghua Gao, Shanghai Tech University:

【Face parsing】Towards Open-Set Identity Preserving Face Synthesis

Jianmin Bao, USTC:

Dong Chen, Microsoft Research Asia:

Fang Wen,:

Houqiang Li,:

Gang Hua, Microsoft Research: http://www.cs.stevens.edu/~ghua/

【Face parsing】Bilateral Ordinal Relevance Multi-instance Regression for Facial Action Unit Intensity Estimation

Yong Zhang, CASIA:

Rui Zhao, Rensselaer Polytechnic Institu:

Weiming Dong,:

Bao-Gang Hu, CASIA:

Qiang Ji, RPI: http://www.ecse.rpi.edu/~qji/

【Face parsing】Every Smile is Unique: Landmark-guided Diverse Smile Generation

Wei Wang, University of Trento:

Xavier Alameda-Pineda, University of Trento:

Dan Xu,:

Elisa Ricci, U. Perugia:

Nicu Sebe, University of Trento:

【Face parsing】A Face to Face Neural Conversation Model

Hang Chu, University of Toronto:

Sanja Fidler,:

【Face parsing】Pose-Guided Photorealistic Face Rotation

Yibo Hu, CRIPAC, CASIA:

Xiang Wu, Institute of Automation, Chine:

Bing Yu,:

Ran He,:

Zhenan Sun, CRIPAC:

【Face parsing】Mesoscopic Facial Geometry inference Using Deep Neural Networks

Loc Huynh, USC ICT:

Weikai Chen, USC ICT:

Shunsuke Saito,:

Jun Xing, ICT:

Koki Nagano, Pinscreen, Inc:

Andrew Jones, USC ICT:

Paul Debevec, USC ICT:

Hao Li,:

【Object recognition】Multi-view Harmonized Bilinear Network for 3D Object Recognition

Tan Yu, Nanyang Technological Univ:

Jingjing Meng,:

Junsong Yuan, Nanyang Technological University:

【Object recognition】Deep Texture Manifold for Ground Terrain Recognition

Jia Xue, Rutgers:

Hang Zhang, Rutgers University:

Kristin Dana,:

【Object recognition】Hierarchical Novelty Detection for Visual Object Recognition

Kibok Lee, University of Michigan:

Kimin Lee, KAIST:

Kyle Min, University of Michigan:

Yuting Zhang, University of Michigan:

Jinwoo Shin, KAIST:

Honglak Lee, University of Michigan, USA: http://web.eecs.umich.edu/~honglak/

【Object recognition】Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networks

Long Chen, ZJU:

Hanwang Zhang, Columbia University:

Jun Xiao, ZJU:

Wei Liu,:

Shih-Fu Chang,: http://www.ee.columbia.edu/ln/dvmm/

【Object recognition】COCO-Stuff: Thing and Stuff Classes in Context

Holger Caesar, University of Edinburgh:

Jasper Uijlings, Google:

Vitto Ferrari,:

【Object recognition】Edit Probability for Scene Text Recognition

Fan Bai, Fudan University:

Zhanzhan Cheng, Hikvision Research Institute:

Yi Niu, Hikvision Research Institute:

Shiliang Pu,:

Shuigeng Zhou, Fudan University:

【Object recognition】HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification

Amos Sironi, Prophesee:

Manuele Brambilla, Prophesee:

Nicolas Bourdis, prophesee:

Xavier Lagorce, Prophesee:

Ryad Benosman, Universite Pierre et Marie Curie-Paris:

【Object recognition】Robust Classification with Convolutional Prototype Learning

Hong-Ming Yang, Institute of Automation, Chinese Academy of Sciences:

Xu-Yao Zhang, Institute of Automation, Chinese Academy of Sciences:

Fei Yin, Institute of Automation, Chinese Academy of Sciences:

cheng-lin Liu,:

【Object recognition】Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

Zhirong Wu, UC Berkeley:

Yuanjun Xiong, Amazon:

Stella Yu, UC Berkeley / ICSI:

Dahua Lin, CUHK: http://dahua.me/

【Object recognition】Teaching Categories to Human Learners with Visual Explanations

Oisin Mac Aodha, Caltech:

Shihan Su, Caltech:

Yuxin Chen, Caltech:

Pietro Perona, California Institute of Technology, USA: http://vision.caltech.edu/Perona.html

Yisong Yue,:

【Object recognition】Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition

Yaming Wang, University of Maryland:

Vlad Morariu, University of Maryland:

Larry Davis, University of Maryland, USA: http://www.umiacs.umd.edu/~lsd/

【Object recognition】Rotation Net: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints

Asako Kanezaki, National Institute of Advanced: http://www.mi.t.u-tokyo.ac.jp/

Yasuyuki Matsushita, Osaka University:

Yoshifumi Nishida, National Institute of Advanced Industrial Science and Technology (AIST):

【Object recognition】AON: Towards Arbitrarily-Oriented Text Recognition

Zhanzhan Cheng, Hikvision Research Institute:

Yangliu Xu, Tongji University:

Fan Bai, Fudan University:

Yi Niu, Hikvision Research Institute:

Shiliang Pu,:

Shuigeng Zhou, Fudan University:

【Object recognition】Low-Shot Recognition with Imprinted Weights

Hang Qi, UCLA:

Matthew Brown,:

David Lowe,: http://www.cs.ubc.ca/~lowe/

【Object recognition】Beyond Holistic Object Recognition: Enriching Image Understanding with Part States

Cewu Lu, Shanghai Jiao Tong University:

hao Su,:

CK Tang, HKUST:

【Object recognition】Discriminative Learning of Latent Features for Zero-Shot Recognition

Yan Li, CASIA:

Junge Zhang,:

jianguo Zhang,:

Kaiqi Huang,:

【Object recognition】Webly Supervised Learning Meets Zero-shot Learning: A Hybrid Approach for Fine-grained Classification

Li Niu, Rice University:

Ashok Veeraraghavan, Rice University:

Ashutosh Sabharwal,:

【Object recognition】Multi-Cell Classification by Convolutional Dictionary Learning with Class Proportion Priors

Florence Yellin, Johns Hopkins University:

Benjamin Haeffele, Johns Hopkins University:

Rene Vidal, Johns Hopkins University: http://cis.jhu.edu/~rvidal/

【Object detection】Scale-Transferrable Object Detection

Peng Zhou, Sjtu:

Bingbing Ni,:

Cong Geng, sjtu:

jianguo Hu, Minivision:

Yi Xu, Shanghai Jiao Tong University:

【Object detection】Frustum Point Nets for 3D Object Detection from RGB-D Data

Charles R. Qi, Stanford University:

Wei Liu,:

Chenxia Wu,:

hao Su,:

Leonidas J. Guibas,:

【Object detection】W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection

Yongqiang Zhang, Harbin institute of Technology/KAUST:

Yancheng Bai, Kaust/Iscas:

Mingli Ding,:

Yongqiang Li,:

Bernard Ghanem,:

【Object detection】3D Object Detection with Latent Support Surfaces

Zhile Ren, Brown University:

Erik Sudderth, UC Irvine: http://cs.brown.edu/~sudderth/index.html

【Object detection】Improving Occlusion and Hard Negative Handling for Single-Stage Object Detectors

Junhyug Noh, Seoul National University:

Soochan Lee,:

Beomsu Kim,:

Gunhee Kim, Carnegie Mellon University: http://www.cs.cmu.edu/~gunhee/index.html

【Object detection】Learning Rich Features for Image Manipulation Detection

Peng Zhou, University of Maryland, Colleg:

Xintong Han, University of Maryland:

Vlad Morariu, University of Maryland:

Larry Davis, University of Maryland, USA: http://www.umiacs.umd.edu/~lsd/

【Object detection】R-FCN-3000 at 30fps: Decoupling Detection and Classification

Bharat Singh,:

Hengduo Li,:

Abhishek Sharma,:

Larry Davis, University of Maryland, USA: http://www.umiacs.umd.edu/~lsd/

【Object detection】Revisiting knowledge transfer for training object class detectors

Jasper Uijlings, Google:

Stefan Popov, Google:

Vitto Ferrari,:

【Object detection】Multi-scale Location-aware Kernel Representation for Object Detection

Hao Wang, Harbin Institute of Technology:

Qilong Wang,:

Mingqi Gao, Harbin Institute of Technology:

Peihua Li,:

Wangmeng Zuo, Harbin Institute of Technology:

【Object detection】Min-Entropy Latent Model for Weakly Supervised Object Detection

Fang Wan, UCAS:

Pengxu Wei,:

Jianbin Jiao,:

Zhenjun Han,:

Qixiang Ye,:

【Object detection】Adversarial Complementary Learning for Weakly Supervised Object Localization

Xiaolin Zhang, University of Technology Sydey:

Yunchao Wei,:

Jiashi Feng,:

Yi Yang,: http://www.cs.cmu.edu/~yiyang/

Thomas Huang,:

【Object detection】Geometry-Aware Scene Text Detection with Instance Transformation Network

Fangfang Wang, Zhejiang University:

Liming Zhao, Zhejiang University:

Xi Li, Zhejiang University:

Xinchao Wang,:

Dacheng Tao, University of Sydney:

【Object detection】Towards Human-Machine Cooperation: Evolving Active Learning with Self-supervised Process for Object Detection

Keze Wang,:

Liang Lin,: http://ss.sysu.edu.cn/~ll/index.html

Xiaopeng Yan, Sun Yat-sen University:

Lei Zhang, The Hong Kong Polytechnic University: http://www4.comp.polyu.edu.hk/~cslzhang/

【Object detection】Focus Manipulation Detection via Photometric Histogram Analysis

Can Chen, University of Delaware:

Scott Mc Closkey, Honeywell:

Jingyi Yu, University of Delaware, USA:

【Object detection】Multi-Level Fusion based 3D Object Detection from Monocular Images

Bin Xu,:

Zhenzhong Chen, Wuhan University:

【Object detection】Domain Adaptive Faster R-CNN for Object Detection in the Wild

Yuhua Chen, CVL@ETHZ:

Wen Li, ETH:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

【Object detection】Adversarially Learned One-Class Classifier for Novelty Detection

Mohammad Sabokrou, Institute for Research in Fundamental Sciences (IPM):

Mohammad Khalooie,:

Mahmood Fathi,:

Ehsan Adeli, Stanford University:

【Object detection】An Analysis of Scale Invariance in Object Detection – SNIP

Bharat Singh,:

Larry Davis, University of Maryland, USA: http://www.umiacs.umd.edu/~lsd/

【Object detection】Relation Networks for Object Detection

Han Hu,:

Jiayuan Gu, Microsoft:

Zheng Zhang, Microsoft:

Jifeng Dai, Microsoft Research:

Yichen Wei, Microsoft Research Asia:

【Object detection】DOTA: A Large-scale Dataset for Object Detection in Aerial Images

Gui-Song Xia, Wuhan University:

Xiang Bai, Huazhong University of Science and Technology:

Jian Ding, Wuhan University:

Zhen Zhu, Huazhong University of Science and Technology:

Serge Belongie,: http://vision.ucsd.edu/person/serge-belongie

Jiebo Luo, University of Rochester: http://www.cs.rochester.edu/u/jluo/

Mihai Datcu,:

Marcello Pelillo, University of Venice:

Liangpei Zhang, Wuhan University:

【Object detection】Cluster Net: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information

Rodney La Londe, University of Central Florida:

Dong Zhang, University of Central Florida:

Mubarak Shah, UCF: http://crcv.ucf.edu/people/faculty/shah.html

【Object detection】Pseudo-Mask Augmented Object Detection

Xiangyun Zhao, Northwestern University:

Shuang Liang, Tongji University:

Yichen Wei, Microsoft Research Asia:

【Object detection】Feature Selective Networks for Object Detection

Yao Zhai, University of Science and Technology of China:

Jingjing Fu,:

Yan Lu,:

Houqiang Li,:

【Object detection】Single-Shot Refinement Neural Network for Object Detection

Shifeng Zhang, CBSR, NLPR, CASIA:

Longyin Wen, GE Global Research Center:

Xiao Bian,:

Zhen Lei, Chinese Academy of Sciences:

Stan Li,: http://www.cbsr.ia.ac.cn/users/szli/

【Object detection】Zigzag Learning for Weakly Supervised Object Detection

Xiaopeng Zhang, National University of Singapore:

Jiashi Feng,:

Hongkai Xiong, Shanghai Jiao Tong University:

Qi Tian,: http://www.cs.utsa.edu/~qitian/

【Object detection】Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation

Naoto Inoue, The University of Tokyo:

Ryosuke Furuta, The University of Tokyo:

Toshihiko Yamasaki, The University of Tokyo:

Kiyoharu Aizawa,:

【Object detection】FOTS: Fast Oriented Text Spotting with a Unified Network

Xuebo Liu, Sense Time Group Ltd.:

Ding Liang, Sensetime:

Shi Yan, Sense Time:

Dagui Chen, Sense Time:

Yu Qiao, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences:

Junjie Yan,:

【Object detection】Mobile Video Object Detection with Temporally-Aware Feature Maps

Menglong Zhu,:

Mason Liu, Georgia Tech:

【Object detection】Generative Adversarial Learning Towards Fast Weakly Supervised Detection

Yunhang Shen, Xiamen University:

Rongrong Ji,:

Shengchuan Zhang,:

Wangmeng Zuo, Harbin Institute of Technology:

Yan Wang, Microsoft:

【Object detection】Dimensionalitys Blessing: Detecting the distributions underlying images

Wen-Yan Lin, ADSC:

Yasuyuki Matsushita, Osaka University:

Siying Liu, I2r.a-star.edu.sg:

Jianhuang Lai, Sun Yat-sen University:

【Object detection】Single-Shot Object Detection with Enriched Semantics

Zhishuai Zhang, Johns Hopkins University:

Siyuan Qiao, Johns Hopkins University:

Cihang Xie, JHU:

Wei Shen, Shanghai University:

Bo Wang, Hik Vision USA Inc.:

Alan Yuille, JHU: http://www.stat.ucla.edu/~yuille/

【Object detection】Rotation-sensitive Regression for Oriented Scene Text Detection

Minghui Liao, Huazhong University of Science and Technology:

Zhen Zhu, Huazhong University of Science and Technology:

Baoguang Shi, Huazhong University of Science and Technology:

Gui-Song Xia, Wuhan University:

Xiang Bai, Huazhong University of Science and Technology:

【Object detection】Cascade R-CNN: Delving into High Quality Object Detection

Zhaowei Cai, UC San Diego:

Nuno Vasconcelos, UCSD, USA: http://www.svcl.ucsd.edu/

【Object detection】Meg Det: A Large Mini-Batch Object Detector

Chao Peng, Megvii:

Tete Xiao, Peking University:

Zeming Li, Tsinghua University, Megvii:

Yuning Jiang, Megvii inc.:

Xiangyu Zhang, Megvii Inc:

Kai Jia, Mevii:

Gang Yu, Face++:

Jian Sun,: http://research.microsoft.com/en-us/groups/vc/

【Object detection】Learning Globally Optimized Object Detector via Policy Gradient

Yongming Rao,:

Dahua Lin, CUHK: http://dahua.me/

Jiwen Lu, Tsinghua University:

【Object detection】Real-world Anomaly Detection in Surveillance Videos

Waqas Sultani,:

Chen Chen, University of Central Florida:

Mubarak Shah, UCF: http://crcv.ucf.edu/people/faculty/shah.html

【Object detection】Viewpoint-aware Attentive Multi-view Inference for Vehicle Re-identification

Yi Zhou, University of East Anglia:

Ling Shao, University of East Anglia: http://lshao.staff.shef.ac.uk/

【Object detection】Improving Object Localization with Fitness NMS and Bounded Io U Loss

Lachlan Tychsen-Smith, CSIRO (Data61):

Lars Petersson,:

【Object detection】Dynamic Zoom-in Network for Fast Object Detection in Large Images

Mingfei Gao, University of Maryland:

Ruichi Yu,:

Ang Li, Google Deep Mind:

Vlad Morariu, University of Maryland:

Larry Davis, University of Maryland, USA: http://www.umiacs.umd.edu/~lsd/

【Object detection】Learning Markov Clustering Networks for Scene Text Detection

ZICHUAN LIU, Nanyang Technological Universi:

Guosheng Lin, Nanyang Technological Universi:

Sheng Yang, Nanyang Technological University:

Jiashi Feng,:

Weisi Lin, Nanyang Technological University:

Wangling Goh, Nanyang Technological University:

【Object detection】Deep Reinforcement Learning of Region Proposal Networks for Object Detection

Aleksis Pirinen, Lund University:

Cristian Sminchisescu,:

【Object detection】Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships

Yong Liu, ICT:

Ruiping Wang, Institute of Computing Technology, Chinese Academy of Sciences:

Shiguang Shan, Chinese Academy of Sciences: http://vipl.ict.ac.cn/members/sgshan

Xilin Chen,:

【Object detection】Direction-aware Spatial Context Features for Shadow Detection

Xiaowei Hu, CUHK:

Lei Zhu,:

Chi-Wing Fu,:

Jing Qin, The Hong Kong Polytechnic University:

Pheng-Ann Heng,:

【Object detection】Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation

Pengyuan Lyu, Huazhong University of Science and Technology:

Cong Yao, Huazhong University of Science and Technology:

Wenhao Wu, Megvii:

Shuicheng Yan, National University of Singapore: http://www.lv-nus.org/index.html

Xiang Bai, Huazhong University of Science and Technology:

【Object detection】Detecting and Recognizing Human-Object Interactions

Georgia Gkioxari, Facebook:

Ross Girshick,: http://www.cs.berkeley.edu/~rbg/

Kaiming He,: http://research.microsoft.com/en-us/um/people/kahe/

Piotr Dollar, Facebook AI Research, Menlo Park, USA: http://vision.ucsd.edu/~pdollar/

【Object detection】Deflecting Adversarial Attacks with Pixel Deflection

Aaditya Prakash, Brandeis University:

Nick Moran, Bradeis University:

Solomon Garber, Brandeis University:

Antonella Di Lillo, Brandeis University:

James Storer, Brandeis University:

【Object detection】The i Naturalist Species Classification and Detection Dataset

Grant van Horn, California Institute of Technology:

Oisin Mac Aodha, Caltech:

Yang Song, Google: http://research.google.com/pubs/author38270.html

Yin Cui, Cornell Tech:

Chen Sun, Google:

Alex Shepard, i Naturalist:

Hartwig Adam, Google:

Pietro Perona, California Institute of Technology, USA: http://vision.caltech.edu/Perona.html

Serge Belongie,: http://vision.ucsd.edu/person/serge-belongie

【Object detection】Real-World Repetition Estimation by Div, Grad and Curl

Tom Runia, University of Amsterdam:

Cees Snoek, University of Amsterdam:

Arnold Smeulders, University of Amsterdam, Netherlands:

【Object detection】Recurrent Pixel Embedding for Instance Grouping

Shu Kong, University of California, Irvine:

Charless Fowlkes, University of California, Irvine, USA: http://www.ics.uci.edu/~fowlkes/

【Object detection】Learning Intelligent Dialogs for Bounding Box Annotation

Ksenia Konyushkova, Google:

Jasper Uijlings, Google:

Christoph Lampert,:

Vittorio Ferrari, google: http://groups.inf.ed.ac.uk/calvin/index.html

【Saliency detection】Progressive Attention Guided Recurrent Network for Salient Object Detection

Xiaoning Zhang, Dalian University of Technolog:

TIANTIAN WANG, Dalian University of Technolog:

Jinqing Qi,:

Huchuan Lu, Dalian University of Technology: http://ice.dlut.edu.cn/lu/index.html

【Saliency detection】Cube Padding for Weakly-Supervised Saliency Prediction in 360$^{\circ}$ Videos

Hsien-Tzu Cheng, National Tsing Hua University:

Chun-Hung Chao,:

Jin-Dong Dong,:

Hao-Kai Wen,:

Tyng-Luh Liu, IIS/Academia Sinica:

Min Sun, University of Washington:

【Saliency detection】Learning to Promote Saliency Detectors

Yu Zeng, Dalian University of Technology:

Huchuan Lu, Dalian University of Technology: http://ice.dlut.edu.cn/lu/index.html

Lihe Zhang, Dalian University of Technology:

Mengyang Feng, DUT, student:

Ali Borji, UCF: http://ilab.usc.edu/borji/

【Saliency detection】Salient Object Detection Driven by Fixation Prediction

Wenguan Wang, Beijing Institute of Technology:

Jianbing Shen, Beijing Institute of Technolog: http://cs.bit.edu.cn/shenjianbing/

Xingping Dong, Beijing Institute of Technology:

Ali Borji, UCF: http://ilab.usc.edu/borji/

【Saliency detection】A Bi-directional Message Passing Model for Salient Object Detection

Lu Zhang, Dalian University of Technolog:

Ju Dai, Dalian University of Technolog:

Huchuan Lu, Dalian University of Technology: http://ice.dlut.edu.cn/lu/index.html

You He,:

Gang Wang,:

【Saliency detection】Progressively Complementarity-aware Fusion Network for RGB-D Salient Object Detection

Hao Chen, City University of Hong Kong:

You fu Li, City University of Hong Kong:

【Saliency detection】Pi CANet: Learning Pixel-wise Contextual Attention for Saliency Detection

Nian Liu, Northwestern Polytechnical University:

Junwei Han, Northwestern Polytechnical U.:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Saliency detection】Detect globally, refine locally: A novel approach to saliency detection

TIANTIAN WANG, Dalian University of Technolog:

Lihe Zhang, Dalian University of Technology:

Huchuan Lu, Dalian University of Technology: http://ice.dlut.edu.cn/lu/index.html

Ali Borji, UCF: http://ilab.usc.edu/borji/

【Saliency detection】Flow Guided Recurrent Neural Encoder for Video Salient Object Detection

Guanbin Li,:

Yuan Xie,:

Tianhao Wei,:

Liang Lin,: http://ss.sysu.edu.cn/~ll/index.html

【Saliency detection】Revisiting Video Saliency: A Large-scale Benchmark and a New Model

Wenguan Wang, Beijing Institute of Technology:

Jianbing Shen, Beijing Institute of Technolog: http://cs.bit.edu.cn/shenjianbing/

Fang Guo, Beijing Institute of Technology:

Ming-Ming Cheng, Nankai University:

Ali Borji, UCF: http://ilab.usc.edu/borji/

【Saliency detection】Fooling Vision and Language Models Despite Localization and Attention Mechanism

Xiaojun Xu, Shanghai Jiao Tong University:

Xinyun Chen, UC Berkeley:

Chang Liu, UC Berkeley:

Anna Rohrbach, UC Berkeley:

Trevor Darrell, UC Berkeley, USA: http://www.eecs.berkeley.edu/~trevor/

Dawn Song, UC Berkeley:

【Saliency detection】An End-to-End Text Spotter with Explicit Alignment and Attention

Tong He, The University of Adelaide:

Zhi Tian, SIAT, CAS:

Weilin Huang, The University of Oxford:

Chunhua Shen, University of Adelaide:

Yu Qiao, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences:

Changming Sun, CSIRO Data61:

【Saliency detection】Decide Net: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation

Jiang Liu, Carnegie Mellon University:

Chenqiang Gao, Chongqing University of Posts and Telecommunications:

Deyu Meng, Xi’an Jiaotong University:

Alexander Hauptmann,:

【Saliency detection】AMNet: Memorability Estimation with Attention

Jiri Fajtl, Kingston University:

Vasileios Argyriou, Kingston University:

Dorothy Monekosso, Leeds Beckett:

Paolo Remagnino, Kingston University:

【Saliency detection】Where and Why Are They Looking? Jointly Inferring Human Attention and Intentions in Complex Tasks

Ping Wei, Xi’an Jiaotong University:

Yang Liu, UCLA:

Tianmin Shu, University of California, Los Angeles:

Nanning Zheng, Xi’an Jiaotong University:

Song-Chun Zhu,: http://www.stat.ucla.edu/~sczhu/

【Saliency detection】Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects

Md Amirul Islam, University of Manitoba:

Mahmoud Kalash, University of Manitoba:

Neil D. B. Bruce, University of Manitoba: http://www.cs.umanitoba.ca/~bruce/datacode.html

【Saliency detection】Going from Image to Video Saliency: Augmenting Image Salience with Dynamic Attentional Push

Siavash Gorji, Mc Gill University:

James Clark, Mc Gill University:

【Saliency detection】Emotional Attention: A Study of Image Sentiment and Visual Attention

Shaojing Fan, National University of Singapo:

Zhiqi Shen, National University of Singapore:

Ming Jiang, University of Minnesota:

Bryan Koenig, Southern Utah University:

Juan Xu, University of Minnesota:

Mohan Kankanhalli, National University of Singapore:

Qi Zhao,:

【Saliency detection】Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective

Jing Zhang,:

Tong Zhang, Australian National University:

Yuchao Dai, Australian National University:

Mehrtash Harandi, Australian National University:

Richard Hartley, Australian National University Australia: http://users.cecs.anu.edu.au/~hartley/

【Scene recognition】Embodied Question Answering

Abhishek Das, Georgia Tech:

Samyak Datta, Georgia Tech:

Georgia Gkioxari, Facebook:

Devi Parikh, Georgia Tech: https://filebox.ece.vt.edu/~parikh/

Dhruv Batra, Georgia Tech:

Stefan Lee, Georgia Tech:

【Scene recognition】Learning by Asking Questions

Ishan Misra, CMU:

Ross Girshick,: http://www.cs.berkeley.edu/~rbg/

Rob Fergus, New York University: http://cs.nyu.edu/~fergus/

Martial Hebert, Carnegie Mellon University: http://www.cs.cmu.edu/~hebert/

Abhinav Gupta,: http://www.cs.cmu.edu/~abhinavg/

Laurens van der Maaten, Facebook:

【Scene recognition】CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

Yuhong Li, Beijing Univ. of Posts & Tels:

Xiaofan Zhang, UIUC:

deming Chen, UIUC:

【Scene recognition】Group Cap: Group-based Image Captioning with Structured Relevance and Diversity Constraints

Fuhai Chen, Xiamen university:

Rongrong Ji,:

Xiaoshuai Sun, Harbin Institute of Technology:

Jinsong Su, Xiamen university:

【Scene recognition】A Memory Network Approach for Story-based Temporal Summarization of 360° Videos

Sangho Lee, Seoul National University:

Jinyoung Sung, Seoul National University:

Youngjae Yu,:

Gunhee Kim, Carnegie Mellon University: http://www.cs.cmu.edu/~gunhee/index.html

【Scene recognition】Appearance-and-Relation Networks for Video Classification

Limin Wang, ETH Zurich:

Wei Li, Google:

Wen Li, ETH:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

【Scene recognition】Structure Preserving Video Prediction

Xu Jingwei, Shanghai Jiao Tong University:

Bingbing Ni,:

Zefan Li, Shanghai Jiaotong University:

Shuo Cheng, SJTU:

Xiaokang Yang,:

【Scene recognition】Improving Landmark Localization with Semi-Supervised Learning

Sina Honari, University of Montreal:

Pavlo Molchanov, NVIDIA Research:

Jan Kautz, NVIDIA:

Stephen Tyree,:

Christopher Pal, Ecole Polytechnique de Montreal:

Pascal Vincent, University of Montreal:

【Scene recognition】Robust Physical-World Attacks on Deep Learning Visual Classification

Ivan Evtimov, University of Washington:

Kevin Eykholt, University of Michigan:

Earlence Fernandes, University of Washington:

Tadayoshi Kohno, University of Washington:

Bo Li, UC Berkeley:

Atul Prakash, University of Michigan:

Amir Rahmati, University of Michigan:

Chaowei Xiao, University of Michigan:

Dawn Song, UC Berkeley:

【Scene recognition】Unsupervised Discovery of Object Landmarks as Structural Representations

Yuting Zhang, University of Michigan:

Yijie Guo, University of Michigan:

Yixin Jin,:

Yijun Luo, University of Michigan:

Zhiyuan He, University of Michigan:

Honglak Lee, University of Michigan, USA: http://web.eecs.umich.edu/~honglak/

【Scene recognition】CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition

Jedrzej Kozerawski, UCSB:

Matthew Turk, UC Santa Barbara USA:

【Scene recognition】Viz Wiz Grand Challenge: Answering Visual Questions from Blind People

Danna Gurari, University of Texas at Austin:

Qing Li, USTC:

Abigale Stangl,:

Anhong Guo,:

Chi Lin,:

Kristen Grauman,: http://www.cs.utexas.edu/~grauman/

Jiebo Luo, University of Rochester: http://www.cs.rochester.edu/u/jluo/

Jeffrey Bigham,:

【Scene recognition】Textbook Question Answering under Teacher Guidance with Memory Networks

Juzheng Li, Tsinghua University:

Hang Su, Tsinghua University:

Jun Zhu, Tsinghua University:

Siyu Wang,:

Bo Zhang,:

【Scene recognition】Memory Matching Networks for One-Shot Image Recognition

Qi Cai, University of Science and Technology of China:

Yingwei Pan, University of Science and Technology of China:

Ting Yao, Microsoft Research Asia:

Chenggang Yan, Hangzhou Dianzi University, China:

Tao Mei, Microsoft Research Asia:

【Scene recognition】IQA: Visual Question Answering in Interactive Environments

Daniel Gordon, University of Washington:

Ali Farhadi,: http://homes.cs.washington.edu/~ali/index.html

Aniruddha Kembhavi, Allen Institute for Artificial Intelligence:

Dieter Fox, University of Washington:

Mohammad Rastegari, AI2:

Joe Redmon, University of Washington:

【Scene recognition】Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge

Damien Teney, Unversity of Adelaide:

Peter Anderson, Australian National University:

Xiaodong He,:

Anton Van den Hengel, University of Adelaide:

【Scene recognition】Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries

Bohan Zhuang, The University of Adelaide:

Qi Wu, University of Adelaide:

Chunhua Shen, University of Adelaide:

Ian Reid,: http://www.robots.ox.ac.uk/~ian/

Anton Van den Hengel, University of Adelaide:

【Scene recognition】Learning to Localize Sound Source in Visual Scenes

Arda Senocak, KAIST:

Junsik Kim, Korea Advanced Institute of Science and Technology (KAIST):

Tae-Hyun Oh, MIT:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

In So Kweon, KAIST: http://rcv.kaist.ac.kr/

【Scene recognition】Dynamic Few-Shot Visual Learning without Forgetting

Spyros Gidaris, Ecole des Ponts Paris Tech:

Nikos Komodakis,: http://imagine.enpc.fr/~komodakn/

【Scene recognition】Transparency by Design: Closing the Gap Between Performance and Interpretabilty in Visual Reasoning

David Mascharka, MIT Lincoln Laboratory:

Philip Tran, Planck Aerosystems:

Ryan Soklaski, MIT Lincoln Laboratory:

Arjun Majumdar, MIT Lincoln Laboratory:

【Scene recognition】Categorizing Concepts with Basic Level for Vision-to-Language

Hanzhang Wang, Tongji University:

Hanli Wang, Tongji University:

Kaisheng Xu, Tongji University:

【Scene recognition】Don’t Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering

Aishwarya Agrawal, Georgia Institute of Technology:

Dhruv Batra, Georgia Tech:

Devi Parikh, Georgia Tech: https://filebox.ece.vt.edu/~parikh/

Aniruddha Kembhavi, Allen Institute for Artificial Intelligence:

【Scene recognition】Learning Answer Embeddings for Visual Question Answering

Hexiang Hu,:

Wei-Lun Chao, USC:

Fei Sha, University of Southern California:

【Scene recognition】Clean Net: Transfer Learning for Scalable Image Classifier Training with Label Noise

Kuang-Huei Lee, Microsoft:

Xiaodong He,:

Lei Zhang, Microsoft: http://www4.comp.polyu.edu.hk/~cslzhang/

Linjun Yang, Facebook:

【Scene recognition】Between-class Learning for Image Classification

Yuji Tokozume, The University of Tokyo:

Yoshitaka Ushiku,:

Tatsuya Harada, University of Tokyo:

【Scene recognition】Convolutional Image Captioning

Jyoti Aneja, UIUC:

Aditya Deshpande, University of Illinois at UC:

Alex Schwing,:

【Scene recognition】DVQA: Understanding Data Visualization via Question Answering

Kushal Kafle,:

Brian Price,:

Scott Cohen,:

Christopher Kanan, RIT:

【Scene recognition】Cross-Dataset Adaptation for Visual Question Answering

Wei-Lun Chao, USC:

Hexiang Hu,:

Fei Sha, University of Southern California:

【Scene recognition】Two can play this Game: Visual Dialog with Discriminative Visual Question Generation and Visual Question Answering

Unnat Jain, UIUC:

Lana Lazebnik,:

Alex Schwing,:

【Scene recognition】Learning Discriminative Evaluation Metrics for Image Captioning

Yin Cui, Cornell Tech:

Guandao Yang, Cornell University:

Andreas Veit, Cornel Tech:

Xun Huang,:

Serge Belongie,: http://vision.ucsd.edu/person/serge-belongie

【Scene recognition】Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

Peter Anderson, Australian National University:

Xiaodong He,:

Chris Buehler,:

Damien Teney, Unversity of Adelaide:

Mark Johnson, Macquarie University:

Stephen Gould, Australian National University: http://users.cecs.anu.edu.au/~sgould/index.html

Lei Zhang, Microsoft: http://www4.comp.polyu.edu.hk/~cslzhang/

【Scene recognition】Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering

Nguyen Duy Kien, Tohoku University:

Takayuki Okatani, Tohoku University/RIKEN AIP:

【Scene recognition】Flip Dial: A Generative Model for Two-Way Visual Dialogue

Daniela Massiceti, University of Oxford:

Siddharth Narayanaswamy, University of Oxford:

Puneet Kumar Dokania, University of Oxford:

Phil Torr, Oxford:

【Scene recognition】Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning

Qi Wu, University of Adelaide:

Peng Wang,:

Chunhua Shen, University of Adelaide:

Ian Reid,: http://www.robots.ox.ac.uk/~ian/

Anton Van den Hengel, University of Adelaide:

【Scene recognition】Fine-grained Video Captioning for Sports Narrative

Huanyu Yu, Shanghai Jiao Tong University:

Shuo Cheng, SJTU:

Bingbing Ni,:

Minsi Wang, Shanghai Jiao Tong University:

Zhang Jian, Shanghai Jiao Tong University:

Xiaokang Yang,:

【Scene recognition】Visual Question Generation as Dual Task of Visual Question Answering

Yikang Li,:

Nan Duan, Microsoft:

Bolei Zhou, Massachuate Institute of Technology:

Xiao Chu, Baidu:

Wanli Ouyang, The University of Sydney: http://www.ee.cuhk.edu.hk/~wlouyang/

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

【Scene recognition】Unsupervised Textual Grounding: Linking Words to Image Concepts

Raymond Yeh, UIUC:

Minh Do, University of Illinois at Urbana-Champaign:

Alex Schwing,:

【Scene recognition】Answer with Grounding Snippets: Focal Visual-Text Attention for Visual Question Answering

Junwei Liang, Carnegie Mellon University:

Lu Jiang,:

Liangliang Cao,: http://researcher.watson.ibm.com/researcher/view.php?person=us-liangliang.cao

Alexander Hauptmann,:

【Scene recognition】Learning Semantic Concepts and Order for Image and Sentence Matching

Yan Huang,:

Qi Wu, University of Adelaide:

Liang Wang, unknown:

【Scene recognition】Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network

Zizhao Zhang, University of Florida:

Yuanpu Xie, University of Florida:

Lin Yang,:

【Scene recognition】Making Convolutional Networks Recurrent for Visual Sequence Learning

Xiaodong Yang, NVIDIA:

Pavlo Molchanov, NVIDIA Research:

Jan Kautz, NVIDIA:

【Scene recognition】Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

Xiaolong Wang, Carnegie Mellon University:

Yufei Ye, Carnegie Mellon University:

Abhinav Gupta,: http://www.cs.cmu.edu/~abhinavg/

【Scene recognition】Discriminability objective for training descriptive captions

Ruotian Luo, Toyota Technological Institute:

Scott Cohen,:

Brian Price,:

Greg Shakhnarovich,:

【Scene recognition】Visual Question Answering with Memory-Augmented Networks

Chao Ma,:

Chunhua Shen, University of Adelaide:

Anthony Dick, University of Adelaide:

Qi Wu, University of Adelaide:

Peng Wang, The University of Adelaide:

Anton Van den Hengel, University of Adelaide:

Ian Reid,: http://www.robots.ox.ac.uk/~ian/

【Scene recognition】Neural Baby Talk

Jiasen Lu, Georgia Institute of Technology:

Jianwei Yang, Georgia Tech:

Dhruv Batra, Georgia Tech:

Devi Parikh, Georgia Tech: https://filebox.ece.vt.edu/~parikh/

【Scene recognition】Few-Shot Image Recognition by Predicting Parameters from Activations

Siyuan Qiao, Johns Hopkins University:

Chenxi Liu, JHU:

Wei Shen, Shanghai University:

Alan Yuille,: http://www.stat.ucla.edu/~yuille/

【Scene recognition】Visual Question Reasoning on General Dependency Tree

Qingxing Cao, Sun Yat-Sen University:

Xiaodan Liang, Carnegie Mellon University:

Bailin Li, SUN-YAT SEN UNIVERSITY:

Liang Lin,: http://ss.sysu.edu.cn/~ll/index.html

【Scene recognition】Now You Shake Me: Towards Automatic 4D Cinema

Yuhao Zhou, University of Toronto:

Makarand Tapaswi, University of Toronto:

Sanja Fidler,:

【Scene recognition】Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

Alex Kendall,:

Yarin Gal, University of Cambridge:

Roberto Cipolla,: http://mi.eng.cam.ac.uk/~cipolla/index.htm

【Scene recognition】Jointly Localizing and Describing Events for Dense Video Captioning

Yehao Li, Sun Yat-Sen University:

Ting Yao, Microsoft Research Asia:

Yingwei Pan, University of Science and Technology of China:

Hongyang Chao, Sun Yat-sen University:

Tao Mei, Microsoft Research Asia:

【Scene recognition】M3: Multimodal Memory Modelling for Video Captioning

Junbo Wang, Institute of Automation, Chine:

Wei Wang,:

Yan Huang,:

Liang Wang, unknown:

Tieniu Tan, NLPR China: http://lab.datatang.com/1984DA173065/Default.aspx

【Scene recognition】Show Me a Story: Towards Coherent Neural Story Illustration

Hareesh Ravi, Rutgers University:

Lezi Wang, Rutgers:

Carlos Muniz, Rutgers University:

Leonid Sigal, University of British Columbia:

Mubbasir Kapadia, Rutgers University:

【Scene recognition】Differential Attention for Visual Question Answering

Badri Patro, IIT Kanpur:

Vinay P. Namboodiri, Indian Institute of Technology Kanpur:

【Scene recognition】Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning

Vasili Ramanishka, Boston University:

Yi-Ting Chen, Honda Research Institute USA:

Teruhisa Misu, Honda Research Institute:

Kate Saenko,:

【Scene recognition】Learning Visual Knowledge Memory Networks for Visual Question Answering

Zhou Su,:

Jianguo Li, Intel Lab:

Zhiqiang Shen, Fudan University:

Yurong Chen,:

【Scene recognition】Visual Grounding via Accumulated Attention

chaorui Deng,:

Qi Wu, University of Adelaide:

Fuyuan Hu,:

Fan Lyu, Suzhou University of Science and Technology:

Mingkui Tan, South China University of Technology:

Qingyao Wu, School of Software Engineering, South China University of Technology:

【Scene recognition】Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification

Xiang Long, Tsinghua University:

Chuang Gan, Tsinghua University:

Gerard De Melo, Rutgers University:

Jiajun Wu, MIT:

Xiao Liu,:

Shilei Wen, Baidu Research:

【Scene recognition】Controllable Video Generation with Sparse Trajectories

Zekun Hao,:

Xun Huang,:

Serge Belongie,: http://vision.ucsd.edu/person/serge-belongie

【Scene recognition】Tagging Like Humans: Diverse and Distinct Image Annotation

Baoyuan Wu, Tencent AI Lab:

Weidong Chen, Tencent:

Wei Liu,:

Peng Sun, Tencent:

Bernard Ghanem,:

Siwei Lyu, SUNY Albany:

【Scene recognition】Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images

Tribhuvanesh Orekondy, MPI-INF:

Mario Fritz, MPI, Saarbrucken, Germany: https://scalable.mpi-inf.mpg.de/

Bernt Schiele, MPI Informatics Germany: http://www.d2.mpi-inf.mpg.de/schiele/

【Scene recognition】Movie Graphs: Towards Understanding Human-Centric Situations from Videos

Paul Vicol, University of Toronto:

Makarand Tapaswi, University of Toronto:

Lluís Castrejón,:

Sanja Fidler,:

【Scene recognition】Captioning Images with Style Transfer from Unaligned Text Corpora

Alexander Mathews, Australian National University:

Xuming He, Shanghai Tech: http://users.cecs.anu.edu.au/~hexm/

Lexing Xie, Australian National University, Data61:

【Scene recognition】i VQA: Inverse Visual Question Answering

Feng Liu, Southeast Univeristy: http://web.cecs.pdx.edu/~fliu/

Tao Xiang, Queen Mary University of London:

Timothy Hospedales, University of Edinburgh:

Wankou Yang, Southeast University:

Changyin Sun, Southeast University:

【Scene recognition】Learning Transferable Architectures for Scalable Image Recognition

Barret Zoph, Google:

Vijay Vasudevan, Google:

Jonathon Shlens, Google:

Quoc Le, Google:

【Scene recognition】End-to-End Dense Video Captioning with Masked Transformer

Luowei Zhou, University of Michigan:

Yingbo Zhou, Salesforce:

Jason Corso,:

Richard Socher, Meta-Mind:

Caiming Xiong, Salesforce:

【Scene recognition】Customized Image Narrative Generation via Interactive Visual Question Generation and Answering

Andrew Shin, The University of Tokyo:

Yoshitaka Ushiku,:

Tatsuya Harada, University of Tokyo:

【Scene recognition】Rethinking Feature Distribution for Loss Functions in Image Classification

Weitao Wan, Tsinghua University:

Yuanyi Zhong, UIUC:

Tianpeng Li, Tsinghua University:

Jiansheng Chen, Tsinghua University:

【Scene recognition】Sketch-a-Classifier: Sketch-based Photo Classifier Generation

Conghui Hu, Queen Mary University of Londo:

Da Li,:

Yi-Zhe Song,:

Tao Xiang, Queen Mary University of London:

Timothy Hospedales, University of Edinburgh:

【Scene recognition】The power of ensembles for active learning in image classification

William Beluch, Bosch Center for Artificial Intelligence:

Tim Genewein, Robert Bosch Center for AI:

Andreas Nürnberger, Otto-von-Guericke-Universität Magdeburg:

Jan Köhler, Bosch Center for AI:

【Scene recognition】Deep Image Prior

Dmitry Ulyanov, Skoltech:

Andrea Vedaldi, U Oxford: http://www.robots.ox.ac.uk/~vedaldi/index.html

Victor Lempitsky,:

【Image retrieval】Deep Cauchy Hashing for Hamming Space Retrieval

Yue Cao, Tsinghua University:

Mingsheng Long, Tsinghua University:

Bin Liu, Tsinghua University:

Jianmin Wang,:

【Image retrieval】Hash GAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN

Yue Cao, Tsinghua University:

Mingsheng Long, Tsinghua University:

Bin Liu, Tsinghua University:

Jianmin Wang,:

【Image retrieval】Triplet-Center Loss for Multi-View 3D Object Retrieval

Xinwei He, HUST:

Yang Zhou, Huazhong University of Science and Technology:

Zhichao Zhou, Huazhong University of Science and Technology:

Song Bai, HUST:

Xiang Bai, Huazhong University of Science and Technology:

【Image retrieval】3D Pose Estimation and 3D Model Retrieval for Objects in the Wild

Alexander Grabner, Graz University of Technology:

Peter Roth, Graz University of Technology:

Vincent Lepetit, TU Graz: http://cvlabwww.epfl.ch/~lepetit/

【Image retrieval】Zero-Shot Sketch-Image Hashing

Yuming Shen, University of East Anglia:

Li Liu, University of East Anglia:

Fumin Shen,:

Ling Shao, University of East Anglia: http://lshao.staff.shef.ac.uk/

【Image retrieval】Unsupervised Deep Generative Adversarial Hashing Network

Kamran Ghasedi Dizaji, University of Pittsburgh:

Feng Zheng, University of Pittsburgh:

Najmeh Sadoughi, University of Texas at Dallas:

Heng Huang, University of Pittsburgh:

【Image retrieval】Hashing as Tie-Aware Learning to Rank

Kun He, Boston University:

Fatih Cakir, Boston University:

Sarah Bargal, Boston University:

Stan Sclaroff, Boston University:

【Image retrieval】Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval

Chao Li, Xidian University:

Cheng Deng, Xidian University:

Ning Li, Xidian University:

Wei Liu,:

Dacheng Tao, University of Sydney:

Xinbo Gao,:

【Image retrieval】High-order tensor regularization with application to attribute ranking

Kwang In Kim, University of Bath:

Juhyun Park, Lancaster University:

James Tompkin, Brown University:

【Image retrieval】Learning a Complete Image Indexing Pipeline

Himalaya Jain, Inria, Technicolor:

Joaquin Zepeda,:

Patrick Perez, Technicolor Research:

Rémi Gribonval, Inria:

【Image retrieval】Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

Filip Radenovic, CTU Prague:

Ahmet Iscen, Inria:

Giorgos Tolias, Czech Technical University in Prague:

Yannis Avrithis, Inria:

Ondrej Chum, Czech Technical University in Prague:

【Image retrieval】Separating Self-Expression and Visual Content in Hashtag Supervision

Andreas Veit, Cornel Tech:

Maximillian Nickel,:

Serge Belongie,: http://vision.ucsd.edu/person/serge-belongie

Laurens van der Maaten, Facebook:

【Image retrieval】Who’s Better? Who’s Best? Pairwise Deep Ranking for Skill Determination

Hazel Doughty, University of Bristol:

Dima Damen, University of Bristol:

Walterio Mayol-Cuevas,:

【Image retrieval】Learning to Hash by Discrepancy Minimization

Zhixiang Chen, Tsinghua University:

Xin Yuan, Tsinghua University:

Jiwen Lu, Tsinghua University:

Jie Zhou,:

【Image retrieval】Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models

Jiuxiang Gu, Nanyang Technological Universi:

Jianfei Cai,: http://www3.ntu.edu.sg/home/asjfcai/

Joty Shafiq Rayhan,:

Li Niu, Rice University:

Gang Wang,:

【Image retrieval】Fast Spectral Ranking for Similarity Search

Ahmet Iscen, Inria:

Yannis Avrithis, Inria:

Giorgos Tolias, Czech Technical University in Prague:

Teddy Furon,:

Ondrej Chum, Czech Technical University in Prague:

【Image retrieval】Learning from Noisy Web Data with Category-level Supervision

Li Niu, Rice University:

Qingtao Tang,:

Ashok Veeraraghavan, Rice University:

Ashutosh Sabharwal,:

【Image retrieval】Learning Attribute Representations with Localization for Flexible Fashion Search

Kenan Ak, National University of Singapo:

Joo Hwee Lim, I2R, Astar:

Ashraf Kassim,:

JO YEW THAM,:

【Image retrieval】Sketch Mate: Deep Hashing for Million-Scale Human Sketch Retrieval

Peng Xu, Beijing University of Posts an:

Yongye Huang, Beijing University of Posts and Telecommunications:

Tongtong Yuan, Beijing University of Posts and Telecommunications:

Kaiyue Pang, QMUL:

Yi-Zhe Song,:

Tao Xiang, Queen Mary University of London:

Timothy Hospedales, University of Edinburgh:

Zhanyu Ma, Beijing University of Posts and Telecommunications:

Jun Guo, Beijing University of Posts and Telecommunications:

【Image retrieval】Deep Lesion Graph in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database

Ke Yan, National Institute of Health:

Xiaosong Wang, NIH:

Le Lu, Nvidia Corp:

Ling Zhang, NIH:

Adam Harrison, National Institutes of Health:

MOHAMMADHADI Bagheri, NIH:

Ronald Summers,:

【3D modeling】A Papier-Mâché Approach to Learning 3D Surface Generation

Thibault GROUEIX, École des ponts Paris Tech:

Bryan Russell, Adobe:

Mathew Fisher, Adobe Systems:

Mathieu Aubry,:

Vladimir Kim, Adobe Research:

【3D modeling】Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene

Shubham Tulsiani, UC Berkeley:

David Fouhey, UC Berkeley:

Saurabh Gupta,:

Alexei Efros, UC Berkeley: http://www.cs.cmu.edu/~efros/

Jitendra Malik,: http://www.cs.berkeley.edu/~malik/

【3D modeling】Spline Error Weighting for Robust Visual-Inertial Fusion

Hannes Ovrén, Linköping University:

Per-Erik Forssen, Linkoping University:

【3D modeling】Learning to Parse Wireframes in Images of Man-Made Environments

Kun Huang, Shanghaitech University:

Yifan Wang, Shanghai Tech University:

Zihan Zhou, Penn State University:

Tianjiao Ding,:

Shenghua Gao, Shanghai Tech University:

Yi Ma, EECS, UC Berkeley: http://yima.csl.illinois.edu/

【3D modeling】Car Fusion: Combining Point Tracking and Part Detection for Dynamic 3D Reconstruction of Vehicles

Dinesh reddy Narapureddy, Carnegie mellon university:

Minh Vo, CMU:

Srinivasa Narasimhan, Carnegie Mellon University:

【3D modeling】Augmenting Crowd-Sourced 3D Reconstructions using Semantic Detections

True Price, UNC Chapel Hill:

Johannes Schönberger, ETH Zurich:

Zhen Wei, University of North Carolina:

Marc Pollefeys, ETH: http://www.inf.ethz.ch/personal/pomarc/

Jan-Michael Frahm, UNC Chapel Hill:

【3D modeling】Layout Net: Reconstructing the 3D Room Layout from a Single RGB Image

Chuhang Zou, UIUC:

Alex Colburn, Zillow Group Inc.:

Qi Shan, Zillow Group:

Derek Hoiem,: http://www.cs.illinois.edu/~dhoiem/

【3D modeling】Plane Net: Piece-wise Planar Reconstruction from a Single RGB Image

Chen Liu, WUSTL:

Jimei Yang,: https://eng.ucmerced.edu/people/jyang44

Duygu Ceylan,:

Ersin Yumer, Argo AI:

Yasutaka Furukawa,:

【3D modeling】Image Collection Pop-up: 3D Reconstruction and Clustering of Rigid and Non-Rigid Categories

Antonio Agudo, IRI (CSIC-UPC):

Francesc Moreno-Noguer, Institut de Robotica i Informatica Industrial (UPC/CSIC):

【3D modeling】Sobolev Fusion: 3D Reconstruction of Scenes Undergoing Free Non-rigid Motion

Miroslava Slavcheva, Siemens AG:

Maximilian Baust, TUM:

Slobodan Ilic, Siemens AG:

【3D modeling】Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250Hz

Ayush Tewari, MPI Informatics:

Michael Zollhöfer, MPI Informatics:

Pablo Garrido,:

Florian Bernard,:

Hyeongwoo Kim, MPII:

Patrick Perez, Technicolor Research:

Christian Theobalt, MPI Informatics:

【3D modeling】Reconstructing Thin Structures of Manifold Surfaces by Integrating Spatial Curves

Shiwei Li, HKUST:

Yao Yao, HKUST:

Tian Fang, HKUST:

Long Quan, The Hong Kong University of Science and Technology, Hong Kong: http://visgraph.cs.ust.hk/index.html

【3D modeling】Pix3D: Dataset and Methods for 3D Object Modeling from a Single Image

Xingyuan Sun, Shanghai Jiao Tong University:

Jiajun Wu, MIT:

Xiuming Zhang, MIT:

Zhoutong Zhang, MIT:

Tianfan Xue, Google:

Joshua Tenenbaum,:

William Freeman, MIT/Google: http://people.csail.mit.edu/billf/

【3D modeling】Surf Conv: Bridging 3D and 2D Convolution for RGBD Images

Hang Chu, University of Toronto:

Wei-Chiu Ma, MIT:

Kaustav Kundu, University of Toronto:

Raquel Urtasun, University of Toronto: http://www.cs.toronto.edu/~urtasun/

Sanja Fidler,:

【3D modeling】A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds

Tolga Birdal, Technical University of Munich:

Benjamin Busam, Framos:

Nassir Navab, Technical University of Munich: http://campar.in.tum.de/Main/NassirNavab

Slobodan Ilic, Siemens AG:

Peter Sturm, INRIA Rhone-Alpes:

【3D modeling】Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View

Shuran Song, Princeton:

Andy Zeng, Princeton:

Angel Chang, Stanford University:

Manolis Savva,:

Silvio Savarese,: http://cvgl.stanford.edu/silvio/

Thomas Funkhouser, Princeton:

【3D modeling】Ray Net: Learning Volumetric 3D Reconstruction with Ray Potentials

Despoina Paschalidou, MPI Tuebingen:

Carolin Schmitt, MPI Tuebingen:

Osman Ulusoy, microsoft corporation:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

Andreas Geiger, MPI Tuebingen / ETH Zuerich:

【3D modeling】Neural 3D Mesh Renderer

Hiroharu Kato, Univ. Tokyo:

Tatsuya Harada, University of Tokyo:

【3D modeling】Automatic 3D Indoor Scene Modeling from Single Panorama

Yang Yang, University of Delaware:

Shi Jin, Shanghai Tech University:

Ruiyang Liu,:

Sing Bing Kang, Microsoft Research: http://research.microsoft.com/en-us/people/sbkang/

Jingyi Yu, University of Delaware, USA:

【3D modeling】Extreme 3D Face Reconstruction: Looking Past Occlusions

Anh Tran, USC:

Tal Hassner, Open Univ Israel:

Iacopo Masi, USC:

Gérard Medioni,:

【3D modeling】View Extrapolation of Human Body from a Single Image

Hao Zhu, Nanjing University:

hao Su,:

Peng Wang, Baidu:

Xun Cao, EE Department, Nanjing Univ:

Ruigang Yang, University of Kentucky: http://vis.uky.edu/~ryang/

【3D modeling】Im2Struct: Recovering 3D Shape Structure from a Single RGB Image

Chengjie Niu, National University of Defense Technology:

Jun Li,:

Kai Xu, NUDT & Princeton Univeristy:

【3D modeling】Sparse Photometric 3D Face Reconstruction Guided by Morphable Models

Xuan Cao, Shanghai Tech University:

Zhang Chen, Shanghai Tech University:

jingyi Yu, Shanghai Tech University:

Anpei Chen,:

【3D modeling】Texture Mapping for 3D Reconstruction with RGB-D Sensor

Yanping Fu, Wu Han University:

Qingan Yan, JD.com:

Long Yang, Northwest A&F University:

Jie Liao , Wu Han University:

Chunxia Xiao, Wuhan University:

【3D modeling】Indoor RGB-D Compass from a Single Line and Plane

Pyojin Kim, Seoul National University:

Brian Coltin, NASA Ames Research Center:

  1. Jin Kim,:

【3D modeling】Deeply Learned Filter Response Functions for Hyperspectral Reconstruction

Shijie Nie, NII, Japan:

Lin Gu, National Institute of Informatics:

Yinqiang Zheng, National Institute of Informatics, Japan:

Antony Lam, Saitama University:

Nobutaka Ono, Tokyo Metropolitan University:

Imari Sato, National Institute of Informatics, Japan:

【3D modeling】3D Semantic Trajectory Reconstruction from 3D Pixel Continuum

Jae Yoon,:

Ziwei Li, UMN:

Hyun Park,:

【3D modeling】Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition

Feng Liu, Sichuan University: http://web.cecs.pdx.edu/~fliu/

Dan Zeng, Sichuan University:

Qijun Zhao, Sichuan University:

Xiaoming Liu, Michigan State University:

【3D modeling】Probabilistic Joint Face-Skull Modelling for Facial Reconstruction

Dennis Madsen, University of Basel:

Marcel Lüthi,:

Andreas Schneider,:

Thomas Vetter, U. Basel:

【3D modeling】Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion

Rushil Anirudh, Lawrence Livermore National La:

Hyojin Kim, Lawrence Livermore National Laboratory:

Jayaraman J. Thiagarajan, LLNL:

  1. Aditya Mohan, Lawrence Livermore National Laboratory:

Kyle Champley, Lawrence Livermore National Laboratory:

Timo Bremer, Lawrence Livermore National Laboratory:

【3D modeling】Robust Hough Transform Based 3D Reconstruction from Circular Light Fields

Alessandro Vianello, Robert Bosch Gmb H:

Jens Ackermann, Robert Bosch Gmb H:

Maximilian Diebold, Heidelberg University:

Bernd Jähne, University of Heidelberg:

【3D modeling】Nonlinear 3D Face Morphable Model

LUAN TRAN, Michigan State University:

Xiaoming Liu, Michigan State University:

【3D modeling】A Volumetric Descriptive Network for 3D Object Synthesis

Jianwen Xie, UCLA:

Zilong Zheng, ucla:

【3D modeling】Unsupervised Training for 3D Morphable Model Regression

Kyle Genova, Princeton University:

Forrester Cole, Google:

Aaron Maschinot, Google:

Daniel Vlasic, Google:

Aaron Sarna, Google:

William Freeman, Google: http://people.csail.mit.edu/billf/

【3D modeling】Video Based Reconstruction of 3D People Models

Thiemo Alldieck, TU Braunschweig:

Marcus Magnor, TU Braunschweig:

Weipeng Xu, MPI Informatics:

Christian Theobalt, MPI Informatics:

Gerard Pons-Moll, Max Planck for Informatics:

【Feature matching】Kernelized Subspace Pooling for Deep Local Descriptors

Xing Wei, Xi’an Jiaotong University:

Yihong Gong, Xi’an Jiaotong University:

Yue Zhang, IAIR,Xi’an Jiaotong University:

Nanning Zheng, Xi’an Jiaotong University:

【Feature matching】Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection

David Novotny, Oxford University:

Samuel Albanie, Oxford University:

Diane Larlus, NAVER LABS Europe:

Andrea Vedaldi, U Oxford: http://www.robots.ox.ac.uk/~vedaldi/index.html

【Feature matching】Multi-Task Adversarial Network for Disentangled Feature Learning

Yang Liu, University of Cambridge:

Zhaowen Wang, Adobe:

Hailin Jin,: http://vision.ucla.edu/~hljin/

Ian Wassell,:

【Feature matching】Domain Generalization with Adversarial Feature Learning

Haoliang Li, Nanyang Technological Universi:

Sinno Jilain Pan, Nanyang Technological University, Singapore:

Shiqi Wang, City University of Hong Kong:

Alex Kot,:

【Feature matching】Feature Quantization for Defending Against Distortion of Images

Zhun Sun, Tohoku University:

Mete Ozay,:

Yan Zhang, RIKEN Center for AIP:

Xing Liu, Tohoku University:

Takayuki Okatani, Tohoku University/RIKEN AIP:

【Motion estimation】Hybrid Camera Pose Estimation

Federico Camposeco, ETH:

Andrea Cohen, ETH Zurich:

Marc Pollefeys, ETH: http://www.inf.ethz.ch/personal/pomarc/

Torsten Sattler, ETH Zurich:

【Motion estimation】A Globally Optimal Solution to the Non-Minimal Relative Pose Problem

Jesus Briales, University of Malaga:

Laurent Kneip,:

Javier Gonzalez-Jimenez,:

【Motion estimation】Five-point Fundamental Matrix Estimation for Uncalibrated Cameras

Daniel Barath, MTA SZTAKI:

【Motion estimation】Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective

Suryansh Kumar, Australian National University:

Anoop Cherian,:

Yuchao Dai, Australian National University:

Hongdong Li, Australian National University:

【Motion estimation】Depth and Transient Imaging with Compressive SPAD Array Cameras

Qilin Sun, KAUST:

Xiong Dun, KAUST:

Yifan (Evan) Peng, UBC:

Wolfgang Heidrich,:

【Motion estimation】Real-Time Seamless Single Shot 6D Object Pose Prediction

Bugra Tekin,:

Sudipta Sinha, Microsoft Research:

Pascal Fua,: http://cvlabwww.epfl.ch/~fua/

【Motion estimation】Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks

Dinesh Jayaraman, UT Austin:

Kristen Grauman,: http://www.cs.utexas.edu/~grauman/

【Motion estimation】p OSE: Pseudo Object Space Error for Initialization-Free Bundle Adjustment

Je Hyeong Hong, University of Cambridge:

Christopher Zach, Toshiba Research:

【Motion estimation】ICE-BA: Efficient, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM

Haomin Liu, Baidu:

Mingyu Chen, Baidu:

Guofeng Zhang, Zhejiang University: http://www.cad.zju.edu.cn/home/gfzhang/

Hujun Bao, Zhejiang University:

Yingze Bao, Baidu LLC:

【Motion estimation】Geo Net: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose

Zhichao Yin, Sensetime Group Limited:

Jianping Shi, Sense Time:

【Motion estimation】Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras

Pedro Miraldo, Instituto Superior Técnico, Lisboa:

Francisco Girbal Eiras, University of Oxford:

Srikumar Ramalingam,:

【Motion estimation】A Perceptual Measure for Deep Single Image Camera Calibration

Yannick Hold-Geoffroy, Université Laval:

Kalyan Sunkavalli, Adobe Systems Inc.:

Jonathan Eisenmann, Adobe Systems:

Matthew Fisher, Adobe:

Emiliano Gambaretto, Adobe Systems:

Sunil Hadap,:

Jean-Francois Lalonde, Laval University:

【Motion estimation】Map Net: Geometry-Aware Learning of Maps for Camera Localization

Samarth Brahmbhatt, Georgia Tech:

Jinwei Gu, NVIDIA:

Kihwan Kim, NVIDIA Research:

James Hays, Georgia Tech: http://www.cs.brown.edu/~hays/

Jan Kautz, NVIDIA:

【Motion estimation】3D Hand Pose Estimation: From Current Achievements to Future Goals

Shanxin Yuan, Imperial College London:

Guillermo Garcia-Hernando, Imperial College London:

Bjorn Stenger,:

Tae-Kyun Kim, Imperial College London:

Gyeongsik Moon, Seoul National University:

Ju Yong Chang, Kwangwoon University:

Kyoung Mu Lee,: http://cv.snu.ac.kr/kmlee/

Pavlo Molchanov, NVIDIA Research:

Liuhao Ge, NTU:

Junsong Yuan, Nanyang Technological University:

Xinghao Chen, Tsinghua University:

Guijin Wang, Tsinghua University:

Fan Yang, Nara institute of science and technology:

Kai Akiyama, Nara Institute of Science and Technology:

Yang Wu, Nara Institute of Science and Technology:

Qingfu Wan, Fudan University:

Meysam Madadi, Autonomus University of Barcelona and Computer Vision Center, Barcelona, Spain:

Sergio Escalera, University of Barcelona:

Shile Li, Technical University of Munich:

Dongheui Lee, Technical University of Munich:

Iason Oikonomidis, FORTH:

Antonis Argyros, FORTH:

【Motion estimation】Code SLAM — Learning a Compact, Optimisable Representation for Dense Visual SLAM

Michael Bloesch, Imperial College London:

Jan Czarnowski, Imperial College London:

Ronald Clark, Imperial College London:

Stefan Leutenegger, Imperial College London:

Andrew Davison, Imperial College London UK:

【Motion estimation】Self-calibrating polarising radiometric calibration

Daniel Teo, SUTD:

Boxin Shi, Peking University:

Yinqiang Zheng, National Institute of Informatics, Japan:

Sai-Kit Yeung,:

【Motion estimation】Estimation of Camera Locations in Highly Corrupted Scenarios: All About the Base, No Shape Trouble

Yunpeng Shi, University of Minnesota:

Gilad Lerman, University of Minnesota:

【Motion estimation】Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction

Shubham Tulsiani, UC Berkeley:

Alexei Efros, UC Berkeley: http://www.cs.cmu.edu/~efros/

Jitendra Malik,: http://www.cs.berkeley.edu/~malik/

【Motion estimation】Camera Pose Estimation with Unknown Principal Point

Viktor Larsson, Lund University:

Zuzana Kukelova, Czech Technical University in Prague:

Yinqiang Zheng, National Institute of Informatics, Japan:

【Motion estimation】A Fast Resection-Intersection Method for the Known Rotation Problem

Qianggong Zhang, The University of Adelaide:

Tat-Jun Chin,:

Huu Le, The University of Adelaide:

【Motion estimation】Structure from Recurrent Motion: From Rigidity to Recurrency

Xiu Li, Tsinghua University:

Hongdong Li, Australian National University:

Hanbyul Joo, CMU:

Yebin Liu, Tsinghua University: http://media.au.tsinghua.edu.cn/liuyebin.jsp

Yaser Sheikh,: http://www.cs.cmu.edu/~yaser/

【Motion estimation】Polarimetric Dense Monocular SLAM

Luwei Yang, Simon Farser University:

Feitong Tan, Simon Fraser University:

Ao Li, Simon Fraser University:

Zhaopeng Cui, Simon Fraser University:

Yasutaka Furukawa,:

Ping Tan,: http://www.ece.nus.edu.sg/stfpage/eletp/Index.htm

【Motion estimation】Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

Peter Anderson, Australian National University:

Qi Wu, University of Adelaide:

Damien Teney, Unversity of Adelaide:

Jake Bruce,:

Mark Johnson, Macquarie University:

Niko Sünderhauf, Queensland University of Technology:

Ian Reid,: http://www.robots.ox.ac.uk/~ian/

Stephen Gould, Australian National University: http://users.cecs.anu.edu.au/~sgould/index.html

Anton Van den Hengel, University of Adelaide:

【Motion estimation】Very Large-Scale Global Sf M by Distributed Motion Averaging

Siyu Zhu, HKUST:

Runze Zhang, HKUST:

Lei Zhou, HKUST:

Tianwei Shen, HKUST:

Tian Fang, HKUST:

Ping Tan,: http://www.ece.nus.edu.sg/stfpage/eletp/Index.htm

Long Quan, The Hong Kong University of Science and Technology, Hong Kong: http://visgraph.cs.ust.hk/index.html

【Motion estimation】Learning Less is More – 6D Camera Localization via 3D Surface Regression

Eric Brachmann, TU Dresden:

Carsten Rother, University of Heidelberg: http://www.inf.tu-dresden.de/index.php?node_id=3517&ln=de

【Motion estimation】Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images

Mahdi Rad, TUG:

Markus Oberweger,:

Vincent Lepetit, TU Graz: http://cvlabwww.epfl.ch/~lepetit/

【Motion estimation】Weakly Supervised Phrase Localization with Multi-Scale Anchored Transformer Network

Fang Zhao, National University of Singapore:

Jianshu Li, National University of Singapo:

Jian Zhao, NUS:

Jiashi Feng,:

【Motion estimation】Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations

Aditya Dhawale, Carnegie Mellon University:

Kumar Shaurya Shankar, Carnegie Mellon University:

Nathan Michael, Carnegie Mellon University:

【Motion estimation】Egocentric Basketball Motion Planning from a Single First-Person Image

Gedas Bertasius, University of Pennsylvania:

Aaron Chan, U. of Southern California:

Jianbo Shi, University of Pennsylvania, USA: http://www.cis.upenn.edu/~jshi/

【Motion estimation】Good Appearance Features for Multi-Target Multi-Camera Tracking

Ergys Ristani, Duke University:

Carlo Tomasi, Duke University:

【Motion estimation】Functional Map of the World

Gordon Christie, JHU/APL:

Neil Fendley, JHU/APL:

James Wilson, Digital Globe:

Ryan Mukherjee, JHU/APL:

【Motion estimation】Semantic Visual Localization

Johannes Schönberger, ETH Zurich:

Marc Pollefeys, ETH: http://www.inf.ethz.ch/personal/pomarc/

Andreas Geiger, MPI Tuebingen / ETH Zuerich:

Torsten Sattler, ETH Zurich:

【Motion estimation】In Loc: Indoor Visual Localization with Dense Matching and View Synthesis

Hajime Taira, Tokyo Institute of Technology:

Masatoshi Okutomi, Tokyo Institute of Technology:

Torsten Sattler, ETH Zurich:

Mircea Cimpoi, Czech Institute of Informatics:

Marc Pollefeys, ETH: http://www.inf.ethz.ch/personal/pomarc/

Josef Sivic,:

Tomas Pajdla,:

Akihiko Torii, Tokyo Institute of Technology:

【Motion estimation】Augmented Skeleton Space Transfer for Depth-based Hand Pose Estimation

Seungryul Baek, Imperial College London:

Kwang In Kim, University of Bath:

Tae-Kyun Kim, Imperial College London:

【Motion estimation】Map Net: An Allocentric Spatial Memory for Mapping Environments

Joao Henriques,:

Andrea Vedaldi, U Oxford: http://www.robots.ox.ac.uk/~vedaldi/index.html

【Motion estimation】Hand Point Net: 3D Hand Pose Estimation using Point Sets

Liuhao Ge, NTU:

Junwu Weng, Nanyang Technological Univ.:

Yujun Cai, NTU:

Junsong Yuan, Nanyang Technological University:

【Motion estimation】Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions

Torsten Sattler, ETH Zurich:

Will Maddern, University of Oxford:

Carl Toft, Chalmers University:

Akihiko Torii, Tokyo Institute of Technology:

Lars Hammarstrand, Chalmers university of technol:

Erik Stenborg, Chalmers University of Tech.:

Daniel Safari, DTU:

Marc Pollefeys, ETH: http://www.inf.ethz.ch/personal/pomarc/

Josef Sivic,:

Fredrik Kahl, Chalmers:

Tomas Pajdla,:

【Stereo matching】Single View Stereo Matching

Yue Luo, Sense Time:

Jimmy Ren, Sense Time Group Limited:

Mude Lin, Sun Yat-Sen University:

Jiahao Pang, Sense Time Group Limited:

Wenxiu Sun, Sense Time Group Limited:

Hongsheng Li,:

Liang Lin,: http://ss.sysu.edu.cn/~ll/index.html

【Stereo matching】Deep Depth Completion of a Single RGB-D Image

Yinda Zhang, Princeton:

Thomas Funkhouser, Princeton:

【Stereo matching】Geo Net: Geometric Neural Network for Joint Depth and Surface Normal Estimation

Xiaojuan Qi, CUHK:

Renjie Liao,:

Zhengzhe Liu, CUHK:

Raquel Urtasun, University of Toronto: http://www.cs.toronto.edu/~urtasun/

Jiaya Jia, Chinese University of Hong Kong: http://www.cse.cuhk.edu.hk/leojia/

【Stereo matching】Monocular Relative Depth Perception with Web Stereo Data Supervision

Ke Xian, Huazhong University of Science and Technology:

Chunhua Shen, University of Adelaide:

Zhiguo Cao, Huazhong University of Science and Technology:

Hao Lu, Huazhong University of Science and Technology:

yang xiao, Huazhong University of Science and Technology:

Ruibo Li, Huazhong University of Science and Technology:

Zhenbo Luo, Samsung Research Beijing:

【Stereo matching】Single-Image Depth Estimation Based on Fourier Domain Analysis

Jaehan Lee, Korea University:

Minhyeok Heo, Korea Unversity:

Kyung-Rae Kim, Korea University:

Chang-Su Kim,:

【Stereo matching】Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction

Huangying Zhan, The University of Adelaide:

Ravi Garg, The University of Adelaide:

Chamara Weerasekera, The University of Adelaide:

Kejie Li, The University of Adelaide:

Harsh Agarwal, Indian Institute of Technology (BHU):

Ian Reid,: http://www.robots.ox.ac.uk/~ian/

【Stereo matching】Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior

Daniel S. Jeon, KAIST:

Seung-Hwan Baek, KAIST:

Inchang Choi,:

Min H. Kim, KAIST:

【Stereo matching】Deep Material-aware Cross-spectral Stereo Matching

Tiancheng Zhi, Carnegie Mellon University:

Bernardo Pires, CMU:

Martial Hebert, Carnegie Mellon University: http://www.cs.cmu.edu/~hebert/

Srinivasa Narasimhan, Carnegie Mellon University:

【Stereo matching】Deep Ordinal Regression Network for Monocular Depth Estimation

Huan Fu, The University of Sydney:

Mingming Gong,:

Chaohui Wang, Université Paris-Est:

Kayhan Batmanghelich, University of Pittsburgh:

Dacheng Tao, University of Sydney:

【Stereo matching】Learning Depth from Monocular Videos using Direct Methods

Chaoyang Wang, Carnegie Mellon University:

Jose Buenaposada, Universidad Rey Juan Carlos:

Rui Zhu, Carnegie Mellon University:

Simon Lucey,:

【Stereo matching】Salience Guided Depth Calibration for Perceptually Optimized Compressive Light Field 3D Display

WENJUAN LIAO, NTU, Singapore:

【Stereo matching】Mega Depth: Learning Single-View Depth Prediction from Internet Photos

Zhengqi Li, Cornell University:

Noah Snavely, Cornell University / Google:

【Stereo matching】CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation

Konstantinos Batsos, Stevens Institute of Technolog:

Changjiang Cai,:

Philippos Mordohai, Stevens Institute of Technology:

【Stereo matching】Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains

Jiahao Pang, Sense Time Group Limited:

Wenxiu Sun, Sense Time Group Limited:

Chengxi Yang, Sense Time Group Limited:

Jimmy Ren, Sense Time Group Limited:

Ruichao Xiao,:

Jin Zeng, The Hong Kong University of Science and Technology:

Liang Lin,: http://ss.sysu.edu.cn/~ll/index.html

【Stereo matching】Ada Depth: Unsupervised Content Congruent Adaptation for Depth Estimation

Jogendra Kundu, Indian Institute of Science:

Phani Krishna Uppala, Indian Institute of Science:

Anuj Pahuja, Indian Institute of Science:

Venkatesh Babu Radhakrishnan, Indian Institute of Science:

【Stereo matching】Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer

Amir Atapour-Abarghouei, Durham University:

Toby Breckon, Durham University:

【Stereo matching】Deep MVS: Learning Multi-View Stereopsis

Po-Han Huang, University of Illinois, U-C:

Kevin Matzen, Facebook:

Johannes Kopf, Facebook:

Narendra Ahuja, University of Illinois at Urbana-Champaign, USA: http://vision.ai.illinois.edu/publications.htm

Jia-Bin Huang, Virginia Tech:

【Stereo matching】Uncalibrated Photometric Stereo under Natural Illumination

Zhipeng Mo,:

Boxin Shi, Peking University:

Feng Lu, U. Tokyo:

Sai-Kit Yeung,:

Yasuyuki Matsushita, Osaka University:

【Stereo matching】Robust Depth Estimation from Auto Bracketed Images

Sunghoon Im, KAIST:

Hae-Gon Jeon, KAIST:

In So Kweon, KAIST: http://rcv.kaist.ac.kr/

【Stereo matching】Spanning Patches: Deep Patch Selection for Fast Multi-View Stereo

Alex Poms, Carnegie Mellon University:

Shoou-I Yu, Oculus:

Chenglei Wu, Oculus:

Yaser Sheikh,: http://www.cs.cmu.edu/~yaser/

【Stereo matching】Left-Right Comparative Recurrent Model for Stereo Matching

Zequn Jie,:

Pengfei Wang, NUS:

Yonggen Ling, Tencent:

Bo Zhao,:

Jiashi Feng,:

Wei Liu,:

【Stereo matching】Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation

Dan Xu,:

Wei Wang, University of Trento:

Hao Tang, University of Trento:

Nicu Sebe, University of Trento:

Elisa Ricci, U. Perugia:

【Stereo matching】Trust your Model: Light Field Depth Estimation with inline Occlusion Handling

Hendrik Schilling, Universität Heidelberg:

Maximilian Diebold, Heidelberg University:

Carsten Rother, University of Heidelberg: http://www.inf.tu-dresden.de/index.php?node_id=3517&ln=de

Bernd Jähne, University of Heidelberg:

【Stereo matching】Alternating-Stereo VINS: Observability Analysis and Performance Evaluation

Mrinal Kanti Paul, Google:

Stergios Roumeliotis, Google:

【Stereo matching】EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry

Changha Shin, Yonsei Univ:

Hae-Gon Jeon, KAIST:

Youngjin Yoon ,:

In So Kweon,: http://rcv.kaist.ac.kr/

Seon Joo Kim, Yonsei University:

【Stereo matching】Time-resolved Light Transport Decomposition for Thermal Photometric Stereo

Nobuhiro Ikeya, NAIST:

Kenichiro Tanaka, NAIST:

Tsuyoshi Takatani, NAIST:

Hiroyuki Kubo,:

Takuya Funatomi, NAIST:

Yasuhiro Mukaigawa, NAIST:

【Stereo matching】Pyramid Stereo Matching Network

Jia-Ren Chang, National Chiao Tung University:

Yong-Sheng Chen, National Chiao Tung University:

【Stereo matching】Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints

Reza Mahjourian, University of Texas at Austin:

Martin Wicke, Google Brain:

Anelia Angelova, Google Brain:

【Stereo matching】Deep End-to-End Time-of-Flight Imaging

Shuochen Su, University of British Columbia:

Felix Heide, Stanford University:

Gordon Wetzstein,:

Wolfgang Heidrich,:

【Stereo matching】Aperture Supervision for Monocular Depth Estimation

Pratul Srinivasan, Berkeley:

Rahul Garg,:

Neal Wadhwa,:

Ren Ng, Berkeley:

Jonathan Barron, Google:

【Stereo matching】Photometric Stereo in Participating Media Considering Shape-Dependent Forward Scatter

Yuki Fujimura, Kyoto University:

Masaaki Iiyama, Kyoto University:

Atsushi Hashimoto, Kyoto University:

Michihiko Minoh, Kyoto University:

【Stereo matching】A Low Power, High Throughput, Fully Event-Based Stereo System

Alexander Andreopoulos, IBM Research:

Hirak Kashyap, UC Irvine and IBM:

Tapan Nayak, IBM:

Arnon Amir, IBM:

Myron Flickner, IBM:

【Optical flow】A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation

Guillermo Gallego, University of Zurich:

Henri Rebecq, University of Zurich:

Davide Scaramuzza, University of Zurich:

【Optical flow】Occlusion Aware Unsupervised Learning of Optical Flow

Yang Wang, Baidu USA:

Yi Yang,: http://www.cs.cmu.edu/~yiyang/

Zhenheng Yang,:

Liang Zhao, Baidu USA:

Wei Xu,:

【Optical flow】PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

Deqing Sun, NVIDIA: http://cs.brown.edu/~dqsun/index.html

Xiaodong Yang, NVIDIA:

Ming-Yu Liu, NVIDIA:

Jan Kautz, NVIDIA:

【Optical flow】Lite Flow Net: A Lightweight Convolutional Neural Network for Optical Flow Estimation

Tak-Wai Hui, The Chinese University of Hong Kong:

Chen-Change Loy, the Chinese University of Hong Kong:

Xiaoou Tang, Chinese University of Hong Kong: http://mmlab.ie.cuhk.edu.hk/

【Region matching】PPFNet: Global Context Aware Local Features for Robust 3D Point Matching

Haowen Deng, Technical University of Munich:

Tolga Birdal, Technical University of Munich:

Slobodan Ilic, Siemens AG:

【Region matching】Optimizing Local Feature Descriptors for Nearest Neighbor Matching

Kun He, Boston University:

Yan Lu,:

Stan Sclaroff, Boston University:

【Region matching】Multi-Image Semantic Matching by Mining Consistent Features

Qianqian Wang, Zhejiang University:

Xiaowei Zhou, Zhejiang University:

Kostas Daniilidis, University of Pennsylvania:

【Region matching】On the convergence of Patch Match and its variants

Thibaud EHRET, CMLA, ENS Cachan:

Pablo Arias, CMLA, ENS Cachan:

【Region matching】Co-Occurrence Template Matching

Shai Avidan,:

rotal kat, Tel-Aviv University:

roy jevnisek, Tel-Aviv University:

【Region matching】End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching

Georgios Georgakis, George Mason University:

Srikrishna Karanam, Siemens Corporate Technology:

Ziyan Wu, Siemens Corporation:

Jan Ernst, Siemens Corporation:

Jana Kosecka, George Mason Univiversity: http://cs.gmu.edu/~kosecka/

【Region matching】Distributable Consistent Multi-Graph Matching

Nan Hu, Stanford Unviversity:

Boris Thibert,:

Leonidas J. Guibas,:

【Region matching】Learning to Find Good Correspondences

Kwang Moo Yi, EPFL:

Eduard Trulls,:

Yuki Ono, Sony:

Vincent Lepetit, TU Graz: http://cvlabwww.epfl.ch/~lepetit/

Mathieu Salzmann, EPFL:

Pascal Fua,: http://cvlabwww.epfl.ch/~fua/

【Region matching】OATM: Occlusion Aware Template Matching by Consensus Set Maximization

Simon Korman, Weizmann Institute:

Mark Milam, NGC:

Stefano Soatto, UCLA: http://vision.ucla.edu/projects.html

【Region matching】Deep Learning of Graph Matching

Andrei Zanfir, IMAR and Lund University:

Cristian Sminchisescu,:

【Region matching】Learning Deep Correspondence through Prior and Posterior Feature Constancy

Zhengfa Liang, NUDT:

Yiliu Feng, NUDT:

Yulan Guo, NUDT:

Hengzhu Liu, NUDT:

Wei Chen,:

Linbo Qiao,:

Li Zhou, NUDT:

Jianfeng Zhang, NUDT:

【Region matching】4D Human Body Correspondences from Panoramic Depth Maps

Zhong Li, University of Delaware:

Minye Wu, Shanghai Tech:

Wangyiteng Zhou, Shanghai Tech University:

Jingyi Yu, University of Delaware, USA:

【Region matching】End-to-end weakly-supervised semantic alignment

Ignacio ROCCO, Inria:

Relja Arandjelovic, Deep Mind:

Josef Sivic,:

【Region matching】CVM-Net: Cross-View Matching Network for Image-Based Ground-to-Aerial Geo-Localization

Sixing Hu, NUS:

Mengdan Feng, NUS:

Rang Nguyen, National Uni. of Singapore:

Gim Hee Lee, National University of SIngapore:

【Region matching】Consensus Maximization for Semantic Region Correspondences

Pablo Speciale, ETH:

Danda Paudel,:

Martin Oswald, ETH Zurich:

Hayko Riemenschneider, Computer Vision Lab, ETH Zurich:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

Marc Pollefeys, ETH: http://www.inf.ethz.ch/personal/pomarc/

【Region matching】LSTM stack-based Neural Multi-sequence Alignment Te CHnique

Pelin Dogan, ETH Zurich:

Albert Li, Disney Research:

Leonid Sigal, University of British Columbia:

Markus Gross,:

【Region matching】CNN Driven Sparse Multi-Level B-spline Image Registration

Pingge Jiang, Drexel University:

James Shackleford, Drexel University:

【Image editing】Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading

Bjoern Haefner, TU Munich:

Yvain Queau, Technical University Munich:

Thomas Möllenhoff, Technical University of Munich:

Daniel Cremers,: http://vision.in.tum.de/

【Image editing】Imagination-IQA: No-reference Image Quality Assessment via Adversarial Learning

Kwan-Yee Lin, Peking University:

【Image editing】Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

Jifeng Wang, NJUST:

Xiang Li, NJUST:

Jian Yang, Nanjing University of Science and Technology:

【Image editing】Image Correction via Deep Reciprocating HDR Transformation

Xin Yang, Dalian University of Technology, City University of Hong Kong:

Ke Xu, Dalian University of Technology:

City University of Hong Kong:

Yibing Song, Tencent AI Lab:

Qiang Zhang, Dalian University of Technology:

Xiaopeng Wei, Dalian University of Technology:

Rynson Lau, City University of Hong Kong:

【Image editing】Fast End-to-End Trainable Guided Filter

Huikai Wu, CASIA:

Shuai Zheng, EBay:

Junge Zhang,:

Kaiqi Huang, National Laboratory of Pattern Recognition:

【Image editing】Disentangling Structure and Aesthetics for Content-aware Image Completion

Andrew Gilbert, University of Surrey:

John Collomosse, University of Surrey, UK.:

Hailin Jin,: http://vision.ucla.edu/~hljin/

Brian Price,:

【Image editing】Document Enhancement using Visibility Detection

Nati Kligler, Technion:

Sagi Katz, Technion:

Ayellet Tal, Technion: http://webee.technion.ac.il/labs/cgm/

【Image editing】Learning a Toolchain for Image Restoration

Ke Yu, CUHK:

Chao Dong, Sensetime Co. Ltd:

Chen-Change Loy, the Chinese University of Hong Kong:

【Image editing】FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors

Yu Chen, NUST:

Ying Tai, Tencent:

Xiaoming Liu, Michigan State University:

Chunhua Shen, University of Adelaide:

Jian Yang, Nanjing University of Science and Technology:

【Image editing】Learning Dual Convolutional Neural Networks for Low-Level Vision

Jinshan Pan, UC Merced:

Sifei Liu,:

Deqing Sun, NVIDIA: http://cs.brown.edu/~dqsun/index.html

Jiawei Zhang, City University of Hong Kong:

Yang Liu, DUT:

Jimmy Ren, Sense Time Group Limited:

Zechao Li, Nanjing University of Science and Technology:

Jinhui Tang,:

Huchuan Lu, Dalian University of Technology: http://ice.dlut.edu.cn/lu/index.html

Yu-Wing Tai, Tencent You Tu:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Image editing】Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network

Wenda Zhao, Dalian University of Technolog:

Dong Wang, DUT:

Huchuan Lu, Dalian University of Technology: http://ice.dlut.edu.cn/lu/index.html

【Image editing】Learning Convolutional Networks for Content-weighted Image Compression

Mu LI, Poly U:

Wangmeng Zuo, Harbin Institute of Technology:

Shuhang Gu,:

debin Zhao,:

David Zhang, Hong Kong Polytechnic University: http://www4.comp.polyu.edu.hk/~csdzhang/

【Image editing】Sparse, Smart Contours to Represent and Edit Images

Tali Dekel, Google:

Dilip Krishnan, Google:

Chuang Gan, Tsinghua University:

Ce Liu, Google, Cambridge, USA: http://people.csail.mit.edu/celiu/

William Freeman, Google: http://people.csail.mit.edu/billf/

【Image editing】Doc UNet: Document Image Unwarping via A Stacked U-Net

Ke Ma, Stony Brook University:

Zhixin Shu, Stony Brook University:

Xue Bai, Megvii Inc:

Jue Wang, Megvii: http://www.juew.org/

Dimitris Samaras,:

【Image editing】Encoder-Decoder Alignment for Zero-Pair Image-to-Image Translation

Yaxing Wang, Computer vision center:

Joost van de Weijer, Computer Vision Center Barcelona:

Luis Herranz, Computer Vision Center:

【Image editing】Generative Image Inpainting with Contextual Attention

Jiahui Yu, UIUC:

Zhe Lin, Adobe Systems, Inc.: http://www.adobe.com/technology/people/san-jose/zhe-lin.html

Jimei Yang,: https://eng.ucmerced.edu/people/jyang44

Xiaohui Shen, Adobe Research:

Xin Lu,:

Thomas Huang,:

【Image editing】Conditional Image-to-Image Translation

Jianxin Lin, USTC:

Yingce Xia,:

Tao Qin,:

Zhibo Chen,:

Tie-Yan Liu,:

【Image editing】Analyzing Filters Toward Efficient Conv Net

Takumi Kobayashi,: https://staff.aist.go.jp/takumi.kobayashi/index.html

【Image editing】Deep Photo Enhancer: Unsupervised Learning of Image Enhancement from Photographs with GANs

Yu-Sheng Chen, National Taiwan University:

Yu-Ching Wang, National Taiwan University:

Man-Hsin Kao, National Taiwan University:

Yung-Yu Chuang, National Taiwan University:

【Image editing】Multispectral Image Intrinsic Decomposition via Low Rank Constraint

Qian Huang, Nanjing University:

Zhu Weixin, Nanjing university:

Yang Zhao, Nanjing University:

Linsen Chen, Nanjing University:

yao wang, new york university:

Tao Yue, Nanjing Univ.:

Xun Cao, EE Department, Nanjing Univ:

【Image editing】Learning to Understand Image Blur

Shanghang Zhang,:

Xiaohui Shen, Adobe Research:

Zhe Lin, Adobe Systems, Inc.: http://www.adobe.com/technology/people/san-jose/zhe-lin.html

Radomír Mech,:

João Costeira,:

Jose Moura, Carnegie Mellon University:

【Image editing】Video Rain Removal By Multiscale Convolutional Sparse Coding

Li Minghan, Xi’an Jiaotong University:

Qi Xie,:

Qian Zhao,:

Wei Wei, Xi’an Jiaotong University:

Shuhang Gu,:

Jing Tao,:

Deyu Meng, Xi’an Jiaotong University:

【Image editing】Multi-Frame Quality Enhancement for Compressed Video

Ren Yang, Beihang University:

Mai Xu, Beihang University:

Zulin Wang, Beihang University:

Tianyi Li, Beihang University:

【Image editing】CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition

Anil Baslamisli, University of Amsterdam:

Hoang-An Le, University of Amsterdam:

Theo Gevers, University of Amsterdam:

【Image editing】Image Restoration by Estimating Frequency Distribution of Local Patches

Jaeyoung Yoo, Seoul National University:

Sang ho Lee, Seoul National University:

Nojun Kwak, Seoul National University:

【Image editing】Eye In-Painting with Exemplar Generative Adversarial Networks

Brian Dolhansky, Facebook:

Cristian Canton Ferrer, Facebook:

【Image editing】Language-Based Image Editing with Recurrent attentive Models

Yelong Shen, Microsoft:

Jianbo Chen, UC Berkeley:

Jianfeng Gao,:

Jing Jing Liu, Microsoft:

Xiaodong Liu, Microsoft:

【Image editing】Learning Intrinsic Image Decomposition from Watching the World

Zhengqi Li, Cornell University:

Noah Snavely, Cornell University / Google:

【Image editing】ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing

Chen-Hsuan Lin, CMU:

Ersin Yumer, Argo AI:

Oliver Wang, Adobe:

Eli Shechtman, Adobe Research: http://www.adobe.com/technology/people/seattle/eli-shechtman.html

Simon Lucey,:

【Computational photography】Learning Pose Specific Representations by Predicting different Views

Georg Poier, Graz University of Technology:

David Schinagl,:

Horst Bischof: http://www.icg.tugraz.at/Members/bischof

【Computational photography】Disentangled Person Image Generation

Liqian Ma, KU Leuven:

Qianru Sun, MPI for Informatics:

Stamatios Georgoulis, KU Leuven:

Mario Fritz, MPI, Saarbrucken, Germany: https://scalable.mpi-inf.mpg.de/

Bernt Schiele, MPI Informatics Germany: http://www.d2.mpi-inf.mpg.de/schiele/

Luc Van Gool, KU Leuven: http://www.vision.ee.ethz.ch/

【Computational photography】Multistage Adversarial Losses for Pose-Based Human Image Synthesis

Chenyang Si, Institute of Automation, Chine:

Wei Wang,:

Liang Wang, unknown:

Tieniu Tan, NLPR China: http://lab.datatang.com/1984DA173065/Default.aspx

【Computational photography】Makeup GAN: Makeup Transfer via Cycle-Consistent Adversarial Networks

Huiwen Chang,:

Jingwan Lu, Adobe Research:

Fisher Yu, UC Berkeley:

Adam Finkelstein, Princeton University:

【Computational photography】Deep Phase Net for Video Frame Interpolation

Simone Meyer, ETH Zurich:

Abdelaziz Djelouah, The Walt Disney Company:

Christopher Schroers, Disney Research Zurich:

Brian Mc Williams,:

Alexander Sorkine-Hornung,:

Markus Gross,:

【Computational photography】Occlusion-Aware Rolling Shutter Rectification of 3D Scenes

Subeesh Vasu, IIT Madras:

Mahesh Mohan M R, IIT Madras:

A.N. Rajagopalan, IIT Madras:

【Computational photography】Intrinsic Image Transformation via Scale Space Decomposition

Lechao Cheng,:

Chengyi Zhang, Zhejiang University:

Zicheng Liao,:

【Computational photography】Density-aware Single Image De-raining using a Multi-stream Dense Network

He Zhang, Rutgers:

Vishal Patel:

【Computational photography】Fast and Accurate Single Image Super-Resolution via Information Distillation Network

Zheng Hui, Xidian university:

Xiumei Wang, Xidian university:

Xinbo Gao,:

【Computational photography】ID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis

Yujun Shen, Dept. of IE, CUHK:

Ping Luo, The Chinese University of Hong Kong:

Junjie Yan,:

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

Xiaoou Tang, Chinese University of Hong Kong: http://mmlab.ie.cuhk.edu.hk/

【Computational photography】Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes

Xin Yu, Australian National University:

Basura Fernando, ANU Canberra Australia:

Richard Hartley, Australian National University Australia: http://users.cecs.anu.edu.au/~hartley/

Fatih Porikli, NICTA, Australia: http://www.porikli.com/

【Computational photography】Imagine it for me: Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts

Yizhe Zhu,:

Mohamed Elhoseiny, FAIR:

Bingchen Liu, Rutgers:

Ahmed Elgammal,:

【Computational photography】Image Generation from Scene Graphs

Justin Johnson, Stanford University:

Agrim Gupta, Stanford University:

Fei-Fei Li, Stanford University: http://vision.stanford.edu/resources_links.html

【Computational photography】Attn GAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

Tao Xu, Lehigh University:

Pengchuan Zhang,:

Qiuyuan Huang,:

Han Zhang, Rutgers:

Zhe Gan,:

Xiaolei Huang, Lehigh: http://www.cse.lehigh.edu/~huang/

Xiaodong He,:

【Computational photography】Mo Co GAN: Decomposing Motion and Content for Video Generation

Sergey Tulyakov,:

Ming-Yu Liu, NVIDIA:

Xiaodong Yang, NVIDIA:

Jan Kautz, NVIDIA:

【Computational photography】Image Super-resolution via Dual-state Recurrent Neural Networks

Wei Han, UIUC:

Shiyu Chang,:

Ding Liu, UIUC:

Michael Witbrock,:

Thomas Huang,:

【Computational photography】Deep Back-Projection Networks For Super-Resolution

Muhammad Haris, Toyota Technological Institute:

Greg Shakhnarovich,:

Norimichi Ukita, NAIST:

【Computational photography】A High-Quality Denoising Dataset for Smartphone Cameras

Abdelrahman Abdelhamed, York University:

Stephen Lin, Microsoft Research Asia, China:

Michael Brown, York University: http://www.comp.nus.edu.sg/~brown/

【Computational photography】Context-aware Synthesis for Video Frame Interpolation

Simon Niklaus, Portland State University:

Feng Liu, Portland State University: http://web.cecs.pdx.edu/~fliu/

【Computational photography】Jerk-Aware Video Acceleration Magnification

Shoichiro Takeda, NTT Media Intelligence Lab.:

Kazuki Okami, NTT Media Intelligence Lab.:

Dan Mikami, NTT Media Intelligence Lab.:

Megumi Isogai, NTT Media Intelligence Lab.:

Hideaki Kimata, NTT Media Intelligence Lab.:

【Computational photography】Defense against adversarial attacks using guided denoiser

Fangzhou Liao, Tsinghua University:

Ming Liang,:

Yinpeng Dong, Tsinghua Univeristy:

Tianyu Pang, Tsinghua University:

Jun Zhu, Tsinghua University:

Xiaolin Hu, Tsinghua University:

【Computational photography】ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

Jian Zhang, KAUST:

Bernard Ghanem,:

【Computational photography】Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks

Wei Xiong, University of Rochester:

Wenhan Luo, Tencent AI Lab:

Lin Ma, Tencent AI Lab:

Wei Liu,:

Jiebo Luo, University of Rochester: http://www.cs.rochester.edu/u/jluo/

【Computational photography】A Weighted Sparse Sampling and Smoothing Frame Transition Approach for Semantic Fast-Forward First-Person Videos

Michel Silva, Universidade de Minas Gerais:

Washington Luis Ramos, Universidade Federal de Minas Gerais:

João Pedro Ferreira, Universidade Federal de Minas Gerais:

Felipe Chamone, Universidade Federal de Minas Gerais:

Mario F Campos, Universidade Federal de Minas Gerais:

Erickson Nascimento, Universidade Federal de Minas Gerais:

【Computational photography】x Unit: Learning a Spatial Activation Function for Efficient Image Restoration

Idan Kligvasser, Technion:

Tamar Rott Shaham, Technion:

Tomer Michaeli, Technion:

【Computational photography】Deformation Aware Image Compression

Tamar Rott Shaham, Technion:

Tomer Michaeli, Technion:

【Computational photography】Residual Dense Network for Image Super-Resolution

Yulun Zhang, Northeastern University:

Yapeng Tian, University of rochester:

Yu Kong, Northeastern University:

Bineng Zhong, Huaqiao University:

Yun Fu, Northeastern University:

【Computational photography】Attentive Generative Adversarial Network for Raindrop Removal from A Single Image

Rui Qian, Peking University:

Robby Tan, Yale-NUS College Also, Electrical and Computer Engineering, NUS:

Wenhan Yang, Peking University:

Jiajun Su, Peking University:

Jiaying Liu, Peking University:

【Computational photography】Burst Denoising with Kernel Prediction Networks

Ben Mildenhall, UC Berkeley:

Jiawen Chen, Google:

Jonathan Barron, Google:

Robert Carroll, Google:

Dillon Sharlet,:

Ren Ng, Berkeley:

【Computational photography】Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution

Ying Qu, The University of Tennessee:

Hairong Qi, University of Tennessee:

Chiman Kwan,:

【Computational photography】Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks

Jiawei Zhang, City University of Hong Kong:

Jinshan Pan, UC Merced:

Jimmy Ren, Sense Time Group Limited:

Yibing Song, Tencent AI Lab:

Linchao Bao, Tencent AI Lab:

Rynson Lau, City University of Hong Kong:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Computational photography】Unsupervised Person Image Synthesis in Arbitrary Poses

Albert Pumarola, IRI (CSIC-UPC):

Antonio Agudo, IRI (CSIC-UPC):

Alberto Sanfeliu, IRI (CSIC-UPC):

Francesc Moreno-Noguer, Institut de Robotica i Informatica Industrial (UPC/CSIC):

【Computational photography】Probabilistic Plant Modeling via Multi-View Image-to-Image Translation

Takahiro Isokane, Osaka university:

Fumio Okura, Osaka University:

Ayaka Ide, Osaka University:

Yasuyuki Matsushita, Osaka University:

Yasushi Yagi, Osaka University:

【Computational photography】Free supervision from video games

Philipp Krahenbuhl,:

【Computational photography】Zero-Shot Super-Resolution using Deep Internal Learning

Assaf Shocher, Weizmann institut of Science:

Michal Irani, Weizmann Institute of Science: http://www.wisdom.weizmann.ac.il/~irani/

Nadav Cohen, Institute for Advanced Study:

【Computational photography】Image Blind Denoising With Generative Adversarial Network Based Noise Modeling

Jingwen Chen, Sun Yat-sen University:

Jiawei Chen, Sun Yat-sen University:

Hongyang Chao, Sun Yat-sen University:

Ming Yang,:

【Computational photography】Multi-Scale Weighted Nuclear Norm Image Restoration

Noam Yair, Technion:

Tomer Michaeli, Technion:

【Computational photography】Densely Connected Pyramid Dehazing Network

He Zhang, Rutgers:

Vishal Patel,:

【Computational photography】Universal Denoising Networks : A Novel CNN-based Network Architecture for Image Denoising

Stamatios Lefkimmiatis, Skolkovo Institute of Science:

【Computational photography】Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation

Younghyun Jo, Yonsei University:

Seoung Wug Oh, Yonsei Univeristy:

Jae Yeon Kang, Yonsei Univ.:

Seon Joo Kim, Yonsei University:

【Computational photography】Erase or Fill? Deep Joint Recurrent Rain Removal and Reconstruction in Videos

Jiaying Liu, Peking University:

Wenhan Yang, Peking University:

Shuai Yang, Peking University:

Zongming Guo,:

【Computational photography】Gated Fusion Network for Single Image Dehazing

Wenqi Ren, Chinese Academy of Sciences:

Lin Ma, Tencent AI Lab:

Jiawei Zhang, City University of Hong Kong:

Jinshan Pan, UC Merced:

Xiaochun Cao, Chinese Academy of Sciences:

Wei Liu,:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Computational photography】Learning a Single Convolutional Super-Resolution Network for Multiple Degradations

Kai Zhang, Harbin Institute of Technology:

Wangmeng Zuo, Harbin Institute of Technology:

Lei Zhang, The Hong Kong Polytechnic University: http://www4.comp.polyu.edu.hk/~cslzhang/

【Computational photography】Non-blind Deblurring: Handling Kernel Uncertainty with CNNs

Subeesh Vasu, IIT Madras:

Venkatesh Reddy Maligireddy, IIT Madras:

A.N. Rajagopalan, IIT Madras:

【Computational photography】Learning to See in the Dark

Chen Chen, UIUC:

Qifeng Chen, Intel Labs:

Jia Xu, Tencent AI Lab:

Vladlen Koltun, Intel Labs: http://vladlen.info/publications/

【Computational photography】Deformable GANs for Pose-based Human Image Generation

Aliaksandr Siarohin , DISI, University of Trento:

Enver Sangineto, University of Trento:

Stéphane Lathuilière, Inria:

Nicu Sebe, University of Trento:

【Computational photography】Cross-View Image Synthesis using Conditional Generative Adversarial Nets

Krishna Regmi, Ucf:

Ali Borji, UCF: http://ilab.usc.edu/borji/

【Computational photography】Visual to Sound: Generating Natural Sound for Videos in the Wild

Yipin Zhou, UNC-Chapel Hill:

Zhaowen Wang, Adobe:

Chen Fang, Adobe Research:

Trung Bui,:

Tamara Berg, University on North carolina:

【Computational photography】Feature Super-Resolution: Make Machine See More Clearly

Weimin Tan, Fudan University:

Bo Yan, Fudan University:

Bahetiyaer Bare, Fudan University:

【Computational photography】Classification Driven Dynamic Image Enhancement

Vivek Sharma, Karlsruhe Institute of Technology:

Ali Diba,:

Davy Neven, KU Leuven:

Michael Brown, York University: http://www.comp.nus.edu.sg/~brown/

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

Rainer Stiefelhagen, Karlsruhe Institute of Technology:

【Computational photography】Image to Image Translation for Domain Adaptation

Zak Murez, UCSD:

Soheil Kolouri, HRL Laboratories, LLC:

David Kriegman, University of California at San Diego:

Ravi Ramamoorthi, University of California, San Diego:

Kyungnam Kim, HRL Laboratories:

【Computational photography】Composing Two Objects of Interest for Flying Camera Photography

ZIQUAN LAN, NUS:

David Hsu, NUS:

Gim Hee Lee, National University of SIngapore:

【Computational photography】Reflection Removal for Large-Scale 3D Point Clouds

Jae-Seong Yun, UNIST:

Jae-Young Sim, UNIST:

【Computational photography】Geometry-aware Deep Network for Single-Image Novel View Synthesis

Miaomiao Liu, Data61,CSIRO:

Xuming He, Shanghai Tech: http://users.cecs.anu.edu.au/~hexm/

Mathieu Salzmann, EPFL:

【Computational photography】Inverse Face Net: Deep Monocular Inverse Face Rendering at over 250 Hz

Hyeongwoo Kim, MPII:

Michael Zollhöfer, MPI Informatics:

Ayush Tewari, MPI Informatics:

Justus Thies, Technical University of Munich:

Christian Richardt, University of Bath:

Christian Theobalt, MPI Informatics:

【Computational photography】A Hybrid L1-L0 Layer Decomposition Model for Tone Mapping

Zhetong Liang, Poly U:

Jun Xu, Hong Kong Polytechnic U:

David Zhang, Hong Kong Polytechnic University: http://www4.comp.polyu.edu.hk/~csdzhang/

Zisheng Cao,:

Lei Zhang, The Hong Kong Polytechnic University: http://www4.comp.polyu.edu.hk/~cslzhang/

【Computational photography】CRRN: Multi-Scale Guided Concurrent Reflection Removal Network

Renjie Wan, Nanyang Technological Universi:

Boxin Shi, Peking University:

Ling-Yu Duan,:

Ah-Hwee Tan,:

Alex Kot,:

【Computational photography】Single Image Reflection Separation with Perceptual Losses

Xuaner Zhang, UC Berkeley:

Qifeng Chen, Intel Labs:

【Computational photography】A Robust Method for Strong Rolling Shutter Effects Correction Using Lines with Automatic Feature Selection

Yizhen Lao, Institut Pascal:

Omar Ait-Aider, Institut Pascal:

【Computational photography】Natural and Effective Obfuscation by Head Inpainting

Qianru Sun, MPI for Informatics:

Liqian Ma, KU Leuven:

Seong Joon Oh, MPI-INF:

Mario Fritz, MPI, Saarbrucken, Germany: https://scalable.mpi-inf.mpg.de/

Luc Van Gool, KU Leuven: http://www.vision.ee.ethz.ch/

Bernt Schiele, MPI Informatics Germany: http://www.d2.mpi-inf.mpg.de/schiele/

【Computational photography】Good View Hunting: Learning Photo Composition from 1 Million View Pairs

Zijun Wei, Stony Brook University:

Jianming Zhang, Adobe Research:

Minh Hoai, Stony Brook University:

Xiaohui Shen, Adobe Research:

Zhe Lin, Adobe Systems, Inc.: http://www.adobe.com/technology/people/san-jose/zhe-lin.html

Radomír Mech,:

Dimitris Samaras,:

【Computational photography】DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Network

Shuang Ma, SUNY Buffalo:

Jianlong Fu,:

Chang Chen,:

Tao Mei, Microsoft Research Asia:

【Computational photography】Globally Optimal Inlier Set Maximization for Atlanta Frame Estimation

Kyungdon Joo,:

Tae-Hyun Oh, MIT:

In So Kweon, KAIST: http://rcv.kaist.ac.kr/

Jean-Charles Bazin, KAIST:

【Computational photography】Logo Synthesis and Manipulation with Clustered Generative Adversarial Networks

Alexander Sage, ETH Zurich:

Eirikur Agustsson, ETH Zurich:

Radu Timofte, ETH Zurich:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

【Computational photography】Human-centric Indoor Scene Synthesis Using Stochastic Grammar

Siyuan Qi, UCLA:

Yixin Zhu, UCLA:

Siyuan Huang, UCLA:

Chenfanfu Jiang,:

Song-Chun Zhu,: http://www.stat.ucla.edu/~sczhu/

【Computational photography】Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning

Jongchan Park, KAIST:

Joon-Young Lee,:

Donggeun Yoo, Lunit:

In So Kweon, KAIST: http://rcv.kaist.ac.kr/

【Computational photography】Illuminant Spectra-based Source Separation Using Flash Photography

Zhuo Hui, Carnegie Mellon University:

Kalyan Sunkavalli, Adobe Systems Inc.:

Sunil Hadap,:

Aswin Sankaranarayanan, Carnegie Mellon University:

【Computational photography】Trapping Light for Time of Flight

Ruilin Xu, Columbia University:

Mohit Gupta, Wisconsin:

Shree Nayar, Columbia University:

【Computational photography】Se GAN: Segmenting and Generating the Invisible

KIANA EHSANI, 1993:

Roozbeh Mottaghi, Allen Institute for Artificial Intelligence: http://www.cs.stanford.edu/~roozbeh/

Ali Farhadi,: http://homes.cs.washington.edu/~ali/index.html

【Computational photography】Label Denoising Adversarial Network

Hao Zhou, UMD:

Jin Sun, University of Maryland:

Yaser Yacoob, Univ of Maryland:

David Jacobs, University of Maryland:

【Computational photography】Optimal Structured Light a la Carte

Parsa Mirdehghan, University of Toronto:

Wenzheng Chen, Uof T:

Kyros Kutulakos,:

【Computational photography】Inferring Light Fields from Shadows

Manel Baradad, MIT:

Vickie Ye, MIT:

Adam Yedida, MIT:

Fredo Durand,: http://people.csail.mit.edu/fredo/

William Freeman, MIT/Google: http://people.csail.mit.edu/billf/

Gregory Wornell,:

Antonio Torralba, MIT: http://web.mit.edu/torralba/www/

【Computational photography】Modifying Non-Local Variations Across Multiple Views

Tal Tlusty, Technion:

Tomer Michaeli, Technion:

Tali Dekel, Google:

Lihi Zelnik-Manor,: http://lihi.eew.technion.ac.il/

【Computational photography】Robust Video Content Alignment and Compensation for Rain Removal in a CNN Framework

Jie Chen, Nanyang Technological University:

Cheen-Hau Tan,:

Junhui Hou, City University of Hong Kong:

Lap-Pui Chau, Nanyang Technological University:

He Li,:

【Computational photography】Sf SNet : Learning Shape, Reflectance and Illuminance of Faces `in the wild’

Soumyadip Sengupta, University of Maryland:

Angjoo Kanazawa, University of Maryland:

Carlos Castillo,:

David Jacobs, University of Maryland:

【Computational photography】Learning to Extract a Video Sequence from a Single Motion-Blurred Image

Meiguang Jin, University of Bern, Switzerlan:

Givi Meishvili, University of Bern, Switzerland:

Paolo Favaro, Bern University, Switzerland:

【Computational photography】Blind Predicting Similar Quality Map for Image Quality Assessment

Da Pan, Communication University of CN:

Ping Shi,:

Ming Hou,:

Zefeng Ying,:

Sizhe Fu,:

Yuan Zhang,:

【Computational photography】Seeing Temporal Modulation of Lights from Standard Cameras

Naoki Sakakibara, Nagoya Institute of Technology:

Fumihiko Sakaue, Nagoya Institute of Technology:

JUN SATO, Nagoya Institute of Technology:

【Computational photography】Divide and Conquer for Full-Resolution Light Field Deblurring

Mahesh Mohan M R, IIT Madras:

A.N. Rajagopalan, IIT Madras:

【Computational photography】Improving Color Reproduction Accuracy in the Camera Imaging Pipeline

Hakki Karaimer, York University:

Michael Brown, York University: http://www.comp.nus.edu.sg/~brown/

【Computational photography】Depth-Aware Stereo Video Retargeting

Bing Li, University of Southern Califor:

Chia-Wen Lin,:

Tiejun Huang,: http://www.jdl.ac.cn/~tjhuang/index-en.html

Boxin Shi, Peking University:

Wen Gao,: http://www.jdl.ac.cn/

C.-C. Jay Kuo, University of Southern California:

【Computational photography】Generative Adversarial Image Synthesis with Decision Tree Latent Controller

Takuhiro Kaneko, NTT Corporation:

Kaoru Hiramatsu, NTT Corporation:

Kunio Kashino, NTT:

【Computational photography】Learning a Discriminative Prior for Blind Image Deblurring

Lerenhan Li, HUST:

Jinshan Pan, UC Merced:

Wei-Sheng Lai, University of California, Merced:

Changxin Gao, HUST:

Nong Sang,:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Computational photography】Frame-Recurrent Video Super-Resolution

Mehdi S. M. Sajjadi, Max Planck Institute for Intel:

Raviteja Vemulapalli, Google:

Matthew Brown,:

【Computational photography】Discovering Point Lights with Intensity Distance Fields

Edward Zhang, University of Washington:

MIchael Cohen,:

Brian Curless, Washington: http://homes.cs.washington.edu/~curless/

【Computational photography】Stereoscopic Neural Style Transfer

Dongdong Chen,:

Lu Yuan, Microsoft Research Asia: http://research.microsoft.com/en-us/um/people/luyuan/index.htm

Jing Liao,:

Nenghai Yu,:

Gang Hua, Microsoft Research: http://www.cs.stevens.edu/~ghua/

【Computational photography】Creating Capsule Wardrobes from Fashion Images

Wei-Lin Hsiao, UT-Austin:

Kristen Grauman,: http://www.cs.utexas.edu/~grauman/

【Computational photography】Multi-Content GAN for Few-Shot Font Style Transfer

Samaneh Azadi, UC Berkeley:

Matthew Fisher, Adobe:

Vladimir Kim, Adobe Research:

Zhaowen Wang, Adobe:

Eli Shechtman, Adobe Research: http://www.adobe.com/technology/people/seattle/eli-shechtman.html

Trevor Darrell, UC Berkeley, USA: http://www.eecs.berkeley.edu/~trevor/

【Computational photography】Representing and Learning High Dimensional Data with the Optimal Transport Map from a Probabilistic Viewpoint

Serim Park, Oath:

Matthew Thorpe,:

【Computational photography】Face Aging with Identity-Preserved Conditional Generative Adversarial Networks

Zongwei WANG,:

Xu Tang,:

Weixin Luo, Shanghaitech University:

Shenghua Gao, Shanghai Tech University:

【Computational photography】Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis

Seunghoon Hong, POSTECH:

Dingdong Yang, University of Michigan:

Jongwook Choi, University of Michigan:

Honglak Lee, University of Michigan, USA: http://web.eecs.umich.edu/~honglak/

【Computational photography】Regularizing RNNs for Caption Generation by Reconstructing The Past with The Present

Xinpeng Chen, Wuhan University:

Lin Ma, Tencent AI Lab:

Wenhao Jiang, Tencent AI Lab:

Jian Yao,:

Wei Liu,:

【Computational photography】Learning and Using the Arrow of Time

Donglai Wei, MIT:

Andrew Zisserman, Oxford: http://www.robots.ox.ac.uk/~vgg/

William Freeman, MIT/Google: http://people.csail.mit.edu/billf/

Joseph Lim, University of Southern California:

【Computational photography】Neural Style Transfer via Meta Networks

Falong Shen, Peking University:

Shuicheng Yan,: http://www.lv-nus.org/index.html

Gang Zeng, Peking University:

【Computational photography】From source to target and back: Symmetric Bi-Directional Adaptive GAN

Paolo Russo, University of Rome La Sapienza:

Fabio Carlucci, University of Rome La Sapienza:

Tatiana Tommasi, Italian Institute of Tecnology:

Barbara Caputo, University of Rome La Sapienza, Italy:

【Computational photography】Efficient Subpixel Refinement with Symbolic Linear Predictors

Vincent Lui, Monash University:

Jonathon Geeves, Monash University:

Winston Yii, Monash University:

Tom Drummond, Monash:

【Computational photography】Scale-recurrent Network for Deep Image Deblurring

Xin Tao, CUHK:

Hongyun Gao,:

Yi Wang, The Chinese University of HK:

Xiaoyong Shen, CUHK:

Jue Wang, Megvii: http://www.juew.org/

Jiaya Jia, Chinese University of Hong Kong: http://www.cse.cuhk.edu.hk/leojia/

【Computational photography】Deblur GAN: Blind Motion Deblurring Using Conditional Adversarial Networks

Orest Kupyn, Ukrainian Catholic University:

Volodymyr Budzan, Ukrainian Catholic University:

Mykola Mykhailych, UCU:

Dmytro Mishkin, Czech Technical University:

Jiri Matas,:

【Computational photography】A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

Debang Li, CASIA:

Huikai Wu, CASIA:

Junge Zhang,:

Kaiqi Huang,:

【Computational photography】Single Image Dehazing via Conditional Generative Adversarial Network

Runde Li, NJUST:

Jinshan Pan, UC Merced:

Zechao Li, Nanjing University of Science and Technology:

Jinhui Tang,:

【Computational photography】On the Duality Between Retinex and Image Dehazing

Adrian Galdran, INESC TEC Porto:

Aitor Alvarez-Gila, Tecnalia / CVC-Universitat Autonoma de Barcelona:

Alessandro Bria, University of Cassino and L.M.:

Javier Vazquez-Corral, Universitat Pompeu Fabra:

Marcelo Bertalmio,:

【Computational photography】Arbitrary Style Transfer with Deep Feature Reshuffle

Shuyang Gu, USTC:

Congliang Chen, Peking University:

Jing Liao,:

Lu Yuan, Microsoft Research Asia: http://research.microsoft.com/en-us/um/people/luyuan/index.htm

【Computational photography】Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration

Xinyuan Zhang, Duke University:

Xin Yuan, Nokia Bell Labs:

Lawrence Carin,:

【Computational photography】Deep Semantic Face Deblurring

Ziyi Shen, Beijing Institute of Technology:

Wei-Sheng Lai, University of California, Merced:

Tingfa Xu, Beijing Institute of Technology:

Jan Kautz, NVIDIA:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

【Computational photography】Synthesizing Images of Humans in Unseen Poses

Guha Balakrishnan, MIT:

Adrian Dalca,:

Amy Zhao, MIT:

Fredo Durand,: http://people.csail.mit.edu/fredo/

John Guttag,:

【Computational photography】Revisiting Deep Intrinsic Image Decompositions

Qingnan Fan, Shandong University:

David Wipf, Microsoft Research Asia:

Jiaolong Yang, Microsoft Research Asia:

Gang Hua, Microsoft Research: http://www.cs.stevens.edu/~ghua/

Baoquan Chen,:

【Computational photography】Star GAN: Unified Generative Adversarial Networks for Controllable Multi-Domain Image-to-Image Translation

Jaegul Choo, Korea University:

Jung-Woo Ha, NAVER Corp:

Munyoung Kim, The College of New Jersey:

Yunjey Choi, Korea University:

Minje Choi, Korea University:

Sunghun Kim, HKUST:

【Computational photography】High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

Ting-Chun Wang, NVIDIA:

Ming-Yu Liu, NVIDIA:

Jun-Yan Zhu, UC Berkeley:

Andrew Tao, NVIDIA:

Bryan Catanzaro, NVIDIA:

Jan Kautz, NVIDIA:

【Computational photography】Semi-parametric Image Synthesis

Xiaojuan Qi, CUHK:

Qifeng Chen, Intel Labs:

Jiaya Jia, Chinese University of Hong Kong: http://www.cse.cuhk.edu.hk/leojia/

Vladlen Koltun, Intel Labs: http://vladlen.info/publications/

【Computational photography】Neural Inverse Kinematics for Unsupervised Motion Retargetting

Ruben Villegas, University of Michigan:

Jimei Yang,: https://eng.ucmerced.edu/people/jyang44

Duygu Ceylan,:

Honglak Lee, University of Michigan, USA: http://web.eecs.umich.edu/~honglak/

【Computational photography】Separating Style and Content for Generalized Style Transfer

Yexun Zhang, Shanghai Jiao Tong University:

Ya Zhang,:

Wenbin Cai,:

【Computational photography】Texture GAN: Controlling Deep Image Synthesis with Texture Patches

Wenqi Xian,:

Patsorn Sangkloy, Georgia Institute of Technology:

Varun Agrawal,:

Amit Raj, Georgia Institute of Technolog:

Jingwan Lu, Adobe Research:

Chen Fang, Adobe Research:

Fisher Yu, UC Berkeley:

James Hays, Georgia Tech: http://www.cs.brown.edu/~hays/

【Computational photography】Super Slo Mo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation

Huaizu Jiang, UMass Amherst:

Deqing Sun, NVIDIA: http://cs.brown.edu/~dqsun/index.html

Varun Jampani, NVIDIA Research:

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

Erik Miller,:

Jan Kautz, NVIDIA:

【Computational photography】Generating Synthetic X-ray Images of a Person from the Surface Geometry

Brian Teixeira, Siemens Healthineers:

Vivek Singh, Siemens Healthineers:

Kai Ma, Siemens Healthineers:

Birgi Tamersoy, Siemens Healthineers:

Terrence Chen, Siemens Healthineers:

Yifan Wu, Temple University:

Elena Balashova, Princeton University:

Dorin Comaniciu, Siemens Healthineers: http://coewww.rutgers.edu/riul/FORMER/comanici/

【Computational photography】Light field intrinsics with a deep encoder-decoder network

Anna Alperovich, University of Konstanz:

Ole Johannsen, University of Konstanz:

Michael Strecke, University of Konstanz:

Bastian Goldluecke,:

【Computational photography】Sketchy GAN: Towards Diverse and Realistic Sketch to Image Synthesis

Wengling Chen, Georgia Institute of Technolog:

James Hays, Georgia Tech: http://www.cs.brown.edu/~hays/

【Computational photography】Cartoon GAN: Generative Adversarial Networks for Photo Cartoonization

Yang Chen, Tsinghua University:

Yu-Kun Lai, Cardiff University:

Yong-Jin Liu:

【Texture analysis】Recovering Realistic Texture in Image Super-resolution by Spatial Feature Modulation

Xintao Wang, CUHK University:

Ke Yu, CUHK:

Chao Dong, Sensetime Co. Ltd:

Chen-Change Loy, the Chinese University of Hong Kong:

【Texture analysis】LIME: Live Intrinsic Material Estimation

Abhimitra Meka, Max Planck Institute for Infor:

Maxim Maximov, Graduate School of Computer Science, Saarland University:

Michael Zollhöfer, MPI Informatics:

Avishek Chatterjee, Max Planck Institute for Informatics:

Hans-Peter Seidel, Max Planck Institute for Informatics:

Christian Richardt, University of Bath:

Christian Theobalt, MPI Informatics:

【Texture analysis】Learning to Detect Features in Texture Images

Linguang Zhang, Princeton University:

Szymon Rusinkiewicz, Princeton University:

【Texture analysis】A Common Framework for Interactive Texture Transfer

Yifang Men, Peking University:

Zhouhui Lian,:

Jianguo Xiao, Peking University:

【Texture analysis】Two-Stream Convolutional Networks for Dynamic Texture Synthesis

Matthew Tesfaldet, York University:

Marcus Brubaker, York University:

Konstantinos Derpanis, Ryerson University:

【Medical image】Clinical Skin Lesion Diagnosis using Representations Inspired by Dermatologist Criteria

Jufeng Yang, Nankai University:

Xiaoxiao Sun,:

Jie Liang,:

Paul Rosin,:

【Medical image】Thoracic Disease Identification and Localization with Limited Supervision

Zhe Li, Syracuse University:

Chong Wang, Google Inc:

Mei Han, Google Inc: http://research.google.com/pubs/author13553.html

Yuan Xue, Google:

Wei Wei, Google Inc.:

Li-jia Li, Google Inc:

Fei-Fei Li, Google Inc.: http://vision.stanford.edu/resources_links.html

【Medical image】Seeing Voices and Hearing Faces: Cross-modal biometric matching

Arsha Nagrani, University of Oxford:

Samuel Albanie, Oxford University:

Andrew Zisserman, Oxford: http://www.robots.ox.ac.uk/~vgg/

【Medical image】Tie Net: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays

Xiaosong Wang, NIH:

Yifan Peng, NIH NLM:

Le Lu, Nvidia Corp:

Zhiyong Lu,:

Ronald Summers,:

【Medical image】Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network

Zizhao Zhang, University of Florida:

Lin Yang,:

Yefeng Zheng, Simens:

【Medical image】An Unsupervised Learning Model for Deformable Medical Image Registration

Guha Balakrishnan, MIT:

Adrian Dalca,:

Amy Zhao, MIT:

Mert Sabuncu, Cornell:

John Guttag,:

【Medical image】Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

Adrian Dalca,:

John Guttag,:

Mert Sabuncu, Cornell:

【Medical image】Learning Representations for Single Cells in Microscopy Images

Juan Caicedo, Broad Institute of Harvard and:

Claire Mcquin, Broad Institute of Harvard and MIT:

Allen Goodman, Broad Institute of Harvard and MIT:

Shantanu Singh, Broad Institute of Harvard and MIT:

Anne Carpenter, Broad Institute of Harvard and MIT:

【Data clustering】Deep Adversarial Subspace Clustering

Pan Zhou, National university of singapo:

Yunqing Hou, NUS:

Jiashi Feng,:

【Data clustering】Local and Global Optimization Techniques in Graph-based Clustering

Daiki Ikami, The University of Tokyo:

Toshihiko Yamasaki, The University of Tokyo:

Kiyoharu Aizawa,:

【Data clustering】So S-RSC: A Sum-of-Squares Polynomial Approach to Robustifying Subspace Clustering Algorithms

Octavia Camps, Northeastern University, USA:

Mario Sznaier,:

【Data clustering】Deep Density Clustering of Unconstrained Faces

Wei-An Lin, UMD:

Jun-Cheng Chen,:

Carlos Castillo,:

Rama Chellappa, University of Maryland, USA:

【Machine learning】Rotation Averaging and Strong Duality

Anders Eriksson,:

Fredrik Kahl, Chalmers:

Carl Olsson, Lund University:

Tat-Jun Chin,:

【Machine learning】Joint Cuts and Matching of Partitions in One Graph

Tianshu Yu, Arizona State University:

Junchi Yan, Shanghai Jiao Tong University:

Jieyi Zhao, University of Texas Health Science Center at Houston:

Baoxin Li, Arizona State University:

【Machine learning】Cross-Domain Self-supervised Multi-task Feature Learning Using Synthetic Game Imagery

Zhongzheng Ren, UC Davis:

Yong Jae Lee, UC Davis:

【Machine learning】A Two-Step Disentanglement Method

Naama Hadad, Tel Aviv University:

Lior Wolf, Tel Aviv University, Israel:

Moni Shahar, Tel Aviv University:

【Machine learning】Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization

Peihua Li,:

Jiangtao Xie,:

Qilong Wang,:

Zilin Gao, Dalian University of Technology:

【Machine learning】Visual Relationship Learning with a Factorization-based Prior

SEONG JAE HWANG, University of Wisconsin – Madison:

Zirui Tao , University of Wisconsin – Madi:

Vikas Singh, University of Wisconsin-Madison: http://www.biostat.wisc.edu/~vsingh/

Hyunwoo Kim, Amazon Lab 126:

Sathya Ravi, University of Wisconsin-Madison:

Maxwell Collins,:

【Machine learning】Transductive Unbiased Embedding for Zero-Shot Learning

Jie Song, Zhejiang University:

Chengchao Shen, Zhejiang University:

Yezhou Yang, Arizona State University: http://www.umiacs.umd.edu/~yzyang/

Yang Liu,:

Mingli Song, Zhejiang University:

【Machine learning】Learning to Compare: Relation Network for Few-Shot Learning

Flood Sung, Independent Researcher:

Yongxin Yang, Queen Mary University of London:

Li Zhang, Queen Mary University of London: http://pages.cs.wisc.edu/~lizhang/

Tao Xiang, Queen Mary University of London:

Phil Torr, Oxford:

Timothy Hospedales, University of Edinburgh:

【Machine learning】Compare and Contrast: Learning Prominent Visual Differences

Steven Chen, University of Texas at Austin:

Kristen Grauman,: http://www.cs.utexas.edu/~grauman/

【Machine learning】Scalable and Effective Deep CCA via Soft Decorrelation

Xiaobin Chang, Queen Mary Univ. of London:

Tao Xiang, Queen Mary University of London:

Timothy Hospedales, University of Edinburgh:

【Machine learning】Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data

Arghya Pal, Indian Institute of Technology:

Vineeth Balasubramanian, IIT Hyderabad:

【Machine learning】Stochastic Variational Inference with Gradient Linearization

Tobias Plötz, TU Darmstadt:

Anne Wannenwetsch, TU Darmstadt:

Stefan Roth,: http://www.igp.ethz.ch/photogrammetry/

【Machine learning】Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

Chung-Wei Lee, National Taiwan University:

Wei Fang, National Taiwan University:

Chih-Kuan Yeh, Carnegie Mellon University:

Yu-Chiang Frank Wang, Academia Sinica: http://www.citi.sinica.edu.tw/pages/ycwang/index_en.html

【Machine learning】Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs

Emanuel Laude, TUM:

Jan-Hendrik Lange,:

Jonas Schuepfer,:

Csaba Domokos,:

Laura Leal-Taixe, Technical University of Munich:

Frank Schmidt, BCAI:

Bjoern Andres,:

Daniel Cremers,: http://vision.in.tum.de/

【Machine learning】Pie APP: Perceptual Image-Error Assessment through Pairwise Preference

Ekta Prashnani, UCSB:

Hong Cai, University of California, Santa Barbara:

Yasamin Mostofi, UCSB:

Pradeep Sen, University of California, Santa Barbara:

【Machine learning】Radially-Distorted Conjugate Translations

James Pritts, Czech Technical University:

Zuzana Kukelova, Czech Technical University in Prague:

Viktor Larsson, Lund University:

Ondrej Chum, Czech Technical University in Prague:

【Machine learning】Self-Supervised Feature Learning by Learning to Spot Artifacts

Simon Jenni, Universität Bern:

Paolo Favaro, Bern University, Switzerland:

【Machine learning】Learning Deep Descriptors with Scale-Aware Triplet Networks

Michel Keller, ETH Zürich:

Zetao Chen, ETH Zurich:

Fabiola Maffra, ETH Zürich:

Patrik Schmuck, ETH Zurich:

Margarita Chli, ETH Zurich:

【Machine learning】Efficient, sparse representation of manifold distance matrices for classical scaling

Alexander Huth, University of Texas at Austin:

Javier Turek, Intel Corporation:

【Machine learning】Beyond the Pixel-Wise Loss for Topology-Aware Delineation

Agata Mosinska, EPFL:

Pablo Marquez Neila, EPFL:

Mateusz Kozinski,:

Pascal Fua,: http://cvlabwww.epfl.ch/~fua/

【Machine learning】KIPPI: KInetic Polygonal Partitioning of Images

Jean-Philippe Bauchet, Inria:

Florent Lafarge,:

【Machine learning】Unsupervised CCA

Yedid Hoshen, Facebook AI Research (FAIR):

Lior Wolf, Tel Aviv University, Israel:

【Machine learning】A Biresolution Spectral framework for Product Quantization

Lopamudra Mukherjee, University of Wisc Whitewater:

Sathya Ravi, University of Wisconsin-Madison:

Jiming Peng, University of Houston:

Vikas Singh, University of Wisconsin-Madison: http://www.biostat.wisc.edu/~vsingh/

【Machine learning】Low-shot learning with large-scale diffusion

Matthijs Douze,:

Arthur Szlam, Facebook AI Research:

Bharath Hariharan, Cornell University:

Herve Jegou, Facebook AI Research:

【Machine learning】Disentangling Factors of Variation by Mixing Them

Qiyang HU, University of bern:

Attila Szabo, University of Bern:

Tiziano Portenier,:

Matthias Zwicker,:

Paolo Favaro, Bern University, Switzerland:

【Machine learning】Sliced Wasserstein Distance for Learning Gaussian Mixture Models

Soheil Kolouri, HRL Laboratories, LLC:

Gustavo Rohde, University Virginia:

Heiko Hoffmann, HRL Laboratories, LLC:

【Machine learning】Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation

Zhen Zhang, WASHINGTON UNIVERSITY IN ST.LO:

Mianzhi Wang, WASHINGTON UNIVERSITY IN ST.LOUIS:

Yan Huang,:

Arye Nehorai, WASHINGTON UNIVERSITY IN ST.LOUIS:

【Machine learning】Multi-task Learning by Maximizing Statistical Dependence

Youssef Alami Mejjati, University of Bath:

Darren Cosker, University of Bath:

Kwang In Kim, University of Bath:

【Machine learning】Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB

Tomoyuki Suzuki, Keio University:

Hirokatsu Kataoka, AIST:

Yoshimitsu Aoki, Keio University:

Yutaka Satoh, AIST:

【Machine learning】Efficient Optimization for Rank-based Loss Functions

Pritish Mohapatra, IIIT Hyderabad:

Michal Rolinek, Max Planck Institute for Intelligent Systems, Tuebingen:

C.V. Jawahar, IIIT Hyderabad:

Vladimir Kolmogorov, Institute of Science and Technology, Austria: http://pub.ist.ac.at/~vnk/

  1. Pawan Kumar,:

【Machine learning】Taskonomy: Disentangling Task Transfer Learning

Alexander Sax, Stanford University:

William Shen,:

Amir Zamir, Stanford, UC Berkeley:

Jitendra Malik,: http://www.cs.berkeley.edu/~malik/

Silvio Savarese,: http://cvgl.stanford.edu/silvio/

Leonidas J. Guibas,:

【Machine learning】Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

Kuniaki Saito, The University of Tokyo:

Kohei Watanabe,:

Yoshitaka Ushiku,:

Tatsuya Harada, University of Tokyo:

【Machine learning】Link and code: Fast indexing with graphs and compact regression codes

Matthijs Douze,:

Herve Jegou, Facebook AI Research:

【Machine learning】Boosting Domain Adaptation by Discovering Latent Domains

Massimiliano Mancini, Sapienza University of Rome:

Lorenzo Porzi, Mapillary Research:

Samuel Rota Bulò, Mapillary Research:

Barbara Caputo, University of Rome La Sapienza, Italy:

Elisa Ricci, U. Perugia:

【Machine learning】Collaborative and Adversarial Network for Unsupervised domain adaptation

Weichen Zhang, The University of Sydney:

Wanli Ouyang, The University of Sydney: http://www.ee.cuhk.edu.hk/~wlouyang/

Dong Xu,: http://www.ntu.edu.sg/home/dongxu/

Wen Li, ETH:

【Machine learning】Beyond Gröbner Bases: Basis Selection for Minimal Solvers

Viktor Larsson, Lund University:

Magnus Oskarsson, Lund University Sweden:

Kalle Astroem, Lund University:

Alge Wallis,:

Zuzana Kukelova, Czech Technical University in Prague:

Tomas Pajdla,:

【Machine learning】Deep Cocktail Networks: Multi-source Unsupervised Domain Adaptation with Category Shift

Ruijia Xu, Sun Yat-sen University:

Ziliang Chen, Sun Yat-sen University:

Wangmeng Zuo, Harbin Institute of Technology:

Junjie Yan,:

Liang Lin,: http://ss.sysu.edu.cn/~ll/index.html

【Machine learning】Knowledge Aided Consistency for Weakly Supervised Phrase Grounding

Kan Chen, Univ. of Southern California:

Jiyang Gao,:

Ram Nevatia,: http://iris.usc.edu/USC-Computer-Vision.html

【Machine learning】Large Scale Fine-Grained Categorization and the Effectiveness of Domain-Specific Transfer Learning

Yin Cui, Cornell Tech:

Yang Song, Google: http://research.google.com/pubs/author38270.html

Chen Sun, Google:

Andrew Howard, Google:

Serge Belongie,: http://vision.ucsd.edu/person/serge-belongie

【Machine learning】Data Distillation: Towards Omni-Supervised Learning

Ilija Radosavovic, Facebook AI Research:

Piotr Dollar, Facebook AI Research, Menlo Park, USA: http://vision.ucsd.edu/~pdollar/

Ross Girshick,: http://www.cs.berkeley.edu/~rbg/

Georgia Gkioxari, Facebook:

Kaiming He,: http://research.microsoft.com/en-us/um/people/kahe/

【Machine learning】Grounding Referring Expressions in Images by Variational Context

Hanwang Zhang, Columbia University:

Yulei Niu, Renmin University of China:

Shih-Fu Chang,: http://www.ee.columbia.edu/ln/dvmm/

【Machine learning】A Robust Generative Framework for Generalized Zero-Shot Learning

Vinay Verma, IIT Kanpur:

Gundeep Arora, IIT Kanpur:

Ashish Mishra, IIT MADRAS:

Piyush Rai, IIT Kanpur:

【Machine learning】DS*: Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems

Florian Bernard,:

Christian Theobalt, MPI Informatics:

Michael Moeller, University of Siegen:

【Machine learning】Deep Mutual Learning

Ying Zhang, QMUL:

Tao Xiang, Queen Mary University of London:

Timothy Hospedales, University of Edinburgh:

Huchuan Lu, Dalian University of Technology: http://ice.dlut.edu.cn/lu/index.html

【Machine learning】Coupled End-to-end Transfer Learning with Generalized Fisher Information

Shixing Chen, Wayne State University:

Caojin Zhang, Wayne State University:

Ming Dong,:

【Machine learning】Residual Parameter Transfer for Deep Domain Adaptation

Artem Rozantsev, EPFL:

Mathieu Salzmann, EPFL:

Pascal Fua,: http://cvlabwww.epfl.ch/~fua/

【Machine learning】An Efficient and Provable Approach for Mixture Proportion Estimation Using Linear Independence Assumption

Xiyu Yu, The University of Sydney:

Tongliang Liu, The University of Sydney:

Mingming Gong,:

Kayhan Batmanghelich, University of Pittsburgh:

Dacheng Tao, University of Sydney:

【Machine learning】Baseline Desensitizing In Translation Averaging

Bingbing Zhuang, National University of Singapore:

Loong Fah Cheong, National University of Singapore:

Gim Hee Lee, National University of SIngapore:

【Machine learning】Multimodal Visual Concept Learning with Weakly Supervised Techniques

Giorgos Bouritsas, NTUA:

Petros Koutras, NTUA:

Athanasia Zlatintsi, NTUA:

Petros Maragos, NTUA: http://cvsp.cs.ntua.gr/index.shtm

【Machine learning】Efficient Large-scale Approximate Nearest Neighbor Search on Open CL FPGA

Jialiang Zhang, University of Wisconsin-Madiso:

Soroosh Khoram, UW-Madison:

Jing Li, University of Wisconsin-Madison:

【Machine learning】Structured Uncertainty Prediction Networks

Garoe Dorta, University of Bath:

Sara Vicente, Anthropics Technology Ltd:

Lourdes Agapito, University College London:

Neill Campbell, University of bath:

Ivor Simpson, Anthropics Technology Ltd:

【Machine learning】Adversarial Feature Augmentation for Unsupervised Domain Adaptation

Riccardo Volpi, IIT (Italy):

Pietro Morerio, Istituto Italiano di Tecnologi:

Silvio Savarese,: http://cvgl.stanford.edu/silvio/

Vittorio Murino, Istituto Italiano di Tecnologia:

【Machine learning】Tight Nonconvex Relaxation of MAP Inference

  1. Khuê Lê-Huu, Inria & Centrale Supélec, Université Paris-Saclay:

Nikos Paragios, Ecole Centrale de Paris:

【Machine learning】Feature Generating Networks for Zero-Shot Learning

Yongqin Xian, Max Planck Institute:

Tobias Lorenz, Max Planck Institute for Informatics:

Bernt Schiele, MPI Informatics Germany: http://www.d2.mpi-inf.mpg.de/schiele/

Zeynep Akata, University of Amsterdam:

【Machine learning】Joint Optimization Framework for Learning with Noisy Labels

Daiki Tanaka, The University of Tokyo:

Daiki Ikami, The University of Tokyo:

Toshihiko Yamasaki, The University of Tokyo:

Kiyoharu Aizawa,:

【Machine learning】Wrapped Gaussian Process Regression on Riemannian Manifolds

Anton Mallasto, University of Copenhagen:

Aasa Feragen, University of Copenhagen:

【Machine learning】Deeper Look at Power Normalizations.

Piotr Koniusz, Data61/CSIRO:

Hongguang Zhang, Data61:

Fatih Porikli, NICTA, Australia: http://www.porikli.com/

【Machine learning】The Perception-Distortion Tradeoff

Yochai Blau, Technion:

Tomer Michaeli, Technion:

【Machine learning】Statistical Tomography of Microscopic Life

Aviad Levis, Technion Institute of Technology:

Ronen Talmon, Technion – Israel Institute of Technology:

Yoav Schechner, Technion Haifa, Israel:

【Machine learning】Latent RANSAC

Simon Korman, Weizmann Institute:

Roee Litman, Tel-Aviv University:

【Machine learning】Learning from the Deep: A Revised Underwater Image Formation Model

Derya Akkaynak, University of Haifa:

Tali Treibitz, University of Haifa:

【Machine learning】Graph-Cut RANSAC

Daniel Barath, MTA SZTAKI:

Jiri Matas,:

【Machine learning】Referring Relationships

Ranjay Krishna, Stanford University:

Ines Chami, Stanford University:

Michael Bernstein, Stanford University:

Fei-Fei Li, Stanford University: http://vision.stanford.edu/resources_links.html

【Machine learning】Reward Learning by Instruction

Hsiao-Yu Tung, Carnegie Mellon University:

Adam Harley, Carnegie Mellon University:

Katerina Fragkiadaki, Carnegie Mellon University:

【Machine learning】Low-shot Learning from Imaginary Data

Yu-Xiong Wang, Carnegie Mellon University:

Ross Girshick,: http://www.cs.berkeley.edu/~rbg/

Martial Hebert,: http://www.cs.cmu.edu/~hebert/

Bharath Hariharan, Cornell University:

【Machine learning】Mining on Manifolds: Metric Learning without Labels

Ahmet Iscen, Inria:

Giorgos Tolias, Czech Technical University in Prague:

Yannis Avrithis, Inria:

Ondrej Chum, Czech Technical University in Prague:

【Machine learning】Zero-Shot Kernel Learning.

Hongguang Zhang, Data61:

Piotr Koniusz, Data61/CSIRO:

【Machine learning】Bidirecional Retrieval Made Simple

Jônatas Wehrmann, PUCRS:

Rodrigo Barros, PUCRS:

【Machine learning】Learning Multi-Instance Enriched Image Representation via Non-Greedy Simultaneous L1 -Norm Minimization and Maximization

Hua Wang, Colorado School of Mines:

【Machine learning】Inference in Higher Order MRF-MAP Problems with Small and Large Cliques

Ishant Shanu, Iiit delhi:

Chetan Arora, Indraprastha Institute of Information Technology Delhi:

S.N. Maheshwari, IIT Delhi:

【Machine learning】Unsupervised Domain Adaptation with Similarity-Based Classifier

Pedro Pinheiro, EPFL:

【Machine learning】Hydra Nets: Specialized Dynamic Architectures for Efficient Inference

Ravi Teja Mullapudi, Carnegie Mellon University:

Noam Shazeer, Google:

William Mark, Google:

Kayvon Fatahalian, Stanford:

【Machine learning】Geometric Multi-Model Fitting with a Convex Relaxation Algorithm

Paul Amayo, Oxford:

Pedro Pinies, University of Oxford:

Lina Paz, University of Oxford:

Paul Newman, University of Oxford:

【Machine learning】Fast and Robust Estimation for Unit-Norm Constrained Linear Fitting Problems

Daiki Ikami, The University of Tokyo:

Toshihiko Yamasaki, The University of Tokyo:

Kiyoharu Aizawa,:

【Machine learning】Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration

Lu Sheng, The Chinese University of HK:

Jing Shao, The Sensetime Group Limited:

Ziyi Lin, Sense Time Co. Ltd.:

Xiaogang Wang, Chinese University of Hong Kong: http://www.ee.cuhk.edu.hk/~xgwang/

【Machine learning】Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space

Tatsuya Yokota, Nagoya Institute of Technology:

Burak Erem,:

Seyhmus Guler,:

Simon Warfield, Harvard Medical School:

Hidekata Hontani,:

【Machine learning】Graph Bit: Bitwise Interaction Mining via Deep Reinforcement Learning

Yueqi Duan, Tsinghua University:

Ziwei Wang, Tsinghua University:

Jiwen Lu, Tsinghua University:

Xudong Lin, Tsinghua University:

Jie Zhou,:

【Machine learning】Feature Space Transfer for Data Augmentation

Bo Liu, UCSD:

Xudong Wang, UCSD:

Mandar Dixit, UC San Diego:

Roland Kwitt,:

Nuno Vasconcelos, UCSD, USA: http://www.svcl.ucsd.edu/

【Machine learning】Analytic Expressions for Probabilistic Moments of PL-DNN with Gaussian Input

Adel Bibi, KAUST:

Modar Alfadly, King Abdullah University of Science and Technology:

Bernard Ghanem,:

【Machine learning】The Easy, The Medium and The Hard: Adapting Across Varied Domain Shifts

Swami Sankaranarayanan, University of Maryland:

Yogesh Balaji, University of Maryland:

Carlos Castillo,:

Rama Chellappa, University of Maryland, USA:

【Machine learning】Learning Compositional Visual Concepts with Mutual Consistency

Yunye Gong, Cornell University:

Srikrishna Karanam, Siemens Corporate Technology:

Ziyan Wu, Siemens Corporation:

Kuan-Chuan Peng, Siemens Corporation:

Jan Ernst, Siemens Corporation:

Peter Doerschuk, Cornell University:

【Machine learning】Iterative Learning with Open-set Noisy Labels

Yisen Wang, Tsinghua University:

Xingjun Ma, The University of Melbourne:

Weiyang Liu, Georgia Tech:

James Bailey, The University of Melbourne:

Hongyuan Zha, Georgia Institute of Technology:

Le Song, Georgia Institute of Technology:

Shu-Tao Xia, Tsinghua University:

【Machine learning】Multimodal Explanations: Justifying Decisions and Pointing to the Evidence

Lisa Anne Hendricks, UC Berkeley:

Trevor Darrell, UC Berkeley, USA: http://www.eecs.berkeley.edu/~trevor/

Anna Rohrbach, UC Berkeley:

Zeynep Akata, University of Amsterdam:

Bernt Schiele, MPI Informatics Germany: http://www.d2.mpi-inf.mpg.de/schiele/

Marcus Rohrbach, UC Berkeley:

Dong Huk Park, UC Berkeley:

【Machine learning】Deep Cross-media Knowledge Transfer

Xin Huang, Peking University:

Yuxin Peng, Peking University:

【Machine learning】Smooth Neighbors on Teacher Graphs for Semi-supervised Learning

Yucen Luo, Tsinghua University:

Jun Zhu, Tsinghua University:

Mengxi Li, Tsinghua University:

Yong Ren, Tsinghua University:

Bo Zhang,:

【Machine learning】Manifold Learning in Quotient Spaces

Éloi Mehr, LIP6:

André Lieutier,:

Fernando Sanchez Bermudez,:

Vincent Guitteny,:

Nicolas Thome, Conservatoire national des arts et métiers:

Matthieu Cord,:

【Machine learning】Improvements to context based self-supervised learning

Terrell Mundhenk, LLNL:

Daniel Ho, LLNL:

Barry Chen, LLNL:

【Machine learning】Boosting Self-Supervised Learning via Knowledge Transfer

Mehdi Noroozi, University of Bern:

Ananthachari Kavalkazhani Vinjimoor, UMBC:

Hamed Pirsiavash,:

Paolo Favaro, Bern University, Switzerland:

【Deep learning】The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

Richard Zhang, UC Berkeley:

Phillip Isola, UC Berkeley:

Alexei Efros, UC Berkeley: http://www.cs.cmu.edu/~efros/

Eli Shechtman, Adobe Research: http://www.adobe.com/technology/people/seattle/eli-shechtman.html

Oliver Wang, Adobe:

【Deep learning】NAG: Network for Adversary Generation

Konda Reddy Mopuri, Indian Institute of Science:

Utkarsh Ojha, MNNIT Allahabad:

Utsav Garg, Nanyang Technological University:

Venkatesh Babu Radhakrishnan, Indian Institute of Science:

【Deep learning】Decorrelated Batch Normalization

Lei Huang, Bei Hang university:

Dawei Yang, University of Michigan:

Bo Lang, Beihang University:

Jia Deng,:

【Deep learning】Towards a Mathematical Understanding of the Difficulty in Learning with Feedforward Neural Networks

Hao Shen, Fortiss Gmb H:

【Deep learning】A Constrained Deep Neural Network for Ordinal Regression

Yanzhu Liu, Nanyang Technological Universi:

Adams Kong, NTU Singapore:

Chi Keong Goh, Rolls-Royce Advanced Technology Centre:

【Deep learning】Modulated Convolutional Networks

Xiaodi Wang, Beihang University:

Baochang Zhang,:

Ce Li, CUMTB:

Rongrong Ji,:

jungong han,:

Xianbin Cao, Beihang University:

jianzhuang liu,:

【Deep learning】Learning Steerable Filters for Rotation Equivariant CNNs

Maurice Weiler, Heidelberg University:

Fred Hamprecht, Heidelberg University, Germany:

Martin Storath,:

【Deep learning】Spline CNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

Matthias Fey, TU Dortmund:

Jan Lenssen, TU Dortmund:

Frank Weichert, TU Dortmund:

Heinrich Müller, TU Dortmund:

【Deep learning】GAGAN: Geometry Aware Generative Adverserial Networks

Jean Kossaifi, Imperial College London:

Linh Tran, Imperial College London:

Yannis Panagakis,:

Maja Pantic, Imperial College London, UK: http://ibug.doc.ic.ac.uk/research

【Deep learning】Feedback-prop: Convolutional Neural Network Inference under Partial Evidence

Tianlu Wang, 1994:

Kota Yamaguchi, Cyber Agent, Inc.: http://vision.is.tohoku.ac.jp/~kyamagu/

Vicente Ordonez, University of Virginia:

【Deep learning】Point-wise Convolutional Neural Networks

Binh-Son Hua, SUTD:

Khoi Tran, SUTD:

Sai-Kit Yeung,:

【Deep learning】Fully Convolutional Attention Network for Multimodal Reasoning

Haoqi Fan, Carnegie Mellon University:

Jiatong Zhou:

【Deep learning】Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons

Edward Kim,:

Darryl Hannan,:

Garrett Kenyon,:

【Deep learning】Mat Net: Modular Attention Network for Referring Expression Comprehension

Licheng Yu, UNC Chapel Hill:

Zhe Lin, Adobe Systems, Inc.: http://www.adobe.com/technology/people/san-jose/zhe-lin.html

Xiaohui Shen, Adobe Research:

Jimei Yang,: https://eng.ucmerced.edu/people/jyang44

Xin Lu,:

Mohit Bansal, UNC Chapel Hill:

Tamara Berg, University on North carolina:

【Deep learning】Conditional Generative Adversarial Network for Structured Domain Adaptation

Weixiang Hong, Nanyang Technological Universi:

Zhenzhen Wang, Nanyang Technological University:

Ming Yang, Horizon Robotics Inc.:

Junsong Yuan, Nanyang Technological University:

【Deep learning】Excitation Backprop for RNNs

Sarah Bargal, Boston University:

Andrea Zunino, Istituto Italiano di Tecnologia:

Donghyun Kim, Boston University:

Jianming Zhang, Adobe Research:

Vittorio Murino, Istituto Italiano di Tecnologia:

Stan Sclaroff, Boston University:

【Deep learning】Duplex Generative Adversarial Network for Unsupervised Domain Adaptation

Lanqing Hu, ICT, CAS:

Meina Kan,:

Shiguang Shan, Chinese Academy of Sciences: http://vipl.ict.ac.cn/members/sgshan

Xilin Chen,:

【Deep learning】Global versus Localized Generative Adversarial Nets

Guo-Jun Qi, University of Central Florida:

Liheng Zhang, University of Central Florida:

Hao Hu, University of Central Florida:

【Deep learning】Fast, Simple, and Effective Resource-Constrained Structure Learning of Deep Networks

Ariel Gordon, Google:

Elad Eban, Google:

Bo Chen, Google:

ofir Nachum, Google:

Tien-Ju Yang, Massachusetts Institute of Technology:

Edward Choi, Georgia Institute of Technology:

【Deep learning】Deep Parametric Continuous Convolutional Neural Networks

Shenlong Wang,:

Shun Da Suo,:

Wei-Chiu Ma, MIT:

Raquel Urtasun, University of Toronto: http://www.cs.toronto.edu/~urtasun/

【Deep learning】Fea St Net: Feature-Steered Graph Convolutions for 3D Shape Analysis

Nitika Verma, INRIA:

Edmond Boyer,:

Jakob Verbeek,: http://lear.inrialpes.fr/~verbeek/

【Deep learning】Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Benoit Jacob, Google:

Skirmantas Kligys, Google:

Bo Chen, Google:

Matthew Tang, Google:

Menglong Zhu,:

Andrew Howard, Google:

Dmitry Kalenichenko, Google:

Hartwig Adam, Google:

【Deep learning】Partial Transfer Learning with Selective Adversarial Networks

Zhangjie Cao, Tsinghua University:

Mingsheng Long, Tsinghua University:

Jianmin Wang,:

【Deep learning】LDMNet: Low Dimensional Manifold Regularized Neural Networks

Wei Zhu, Duke University:

Qiang Qiu,:

Jiaji Huang, Baidu Silicon Valley AI Lab:

Robert Calderbank, Duke University:

Guillermo Sapiro, Duke:

Ingrid Daubechies, Duke University:

【Deep learning】Condense Net: An Efficient Dense Net using Learned Group Convolutions

Gao Huang,:

Shichen Liu, Tsinghua University:

Laurens van der Maaten, Facebook:

Kilian Weinberger, Cornell University:

【Deep learning】Decoupled Networks

Weiyang Liu, Georgia Tech:

Zhen Liu,:

Zhiding Yu, Carnegie Mellon University:

Bo Dai,:

Yisen Wang, Tsinghua University:

Thomas Breuel,:

James Rehg, Georgia Institute of Technology: http://www.cc.gatech.edu/~rehg/

Jan Kautz, NVIDIA:

Le Song, Georgia Institute of Technology:

【Deep learning】Deep Adversarial Metric Learning

Yueqi Duan, Tsinghua University:

Wenzhao Zheng, Tsinghua University:

Xudong Lin, Tsinghua University:

Jiwen Lu, Tsinghua University:

Jie Zhou,:

【Deep learning】Deep Layer Aggregation

Fisher Yu, UC Berkeley:

Dequan Wang, UC Berkeley:

Evan Shelhamer, UC Berkeley:

Trevor Darrell, UC Berkeley, USA: http://www.eecs.berkeley.edu/~trevor/

【Deep learning】Convolutional Neural Networks with Alternately Updated Clique

Yibo Yang, Peking Univ.:

Zhisheng Zhong,:

Tiancheng Shen,:

Zhouchen Lin, Peking University, China: http://www.cis.pku.edu.cn/faculty/vision/zlin/zlin.htm

【Deep learning】Practical Block-wise Neural Network Architecture Generation

Zhao Zhong, Institute of Automation,CAS:

Junjie Yan,:

Wei Wu,:

Jing Shao, The Sensetime Group Limited:

cheng-lin Liu,:

【Deep learning】Surface Networks

Ilya Kostrikov, NYU:

Joan Bruna, New York University:

Daniele Panozzo, NYU:

Denis Zorin, NYU:

【Deep learning】What do Deep Networks Like to See?

Sebastian Palacio, DFKI:

Joachim Folz, DFKI:

Andreas Dengel, DFKI:

Jörn Hees, DFKI:

Federico Raue, DFKI:

【Deep learning】Mo Net: Moments Embedding Network

Mengran Gou, Northeastern University:

Fei Xiong, University of Southern California:

Octavia Camps, Northeastern University, USA:

Mario Sznaier,:

【Deep learning】BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning

Ziming Zhang, MERL:

Yuanwei Wu, University of Kansas:

Guanghui Wang, University of Kansas:

【Deep learning】Perturbative Neural Networks: Rethinking Convolution in CNNs

Felix Juefei-Xu, Carnegie Mellon University:

Vishnu Naresh Boddeti, Michigan State University:

Marios Savvides, Carnegie Mellon University:

【Deep learning】Lightweight Probabilistic Deep Networks

Jochen Gast, TU Darmstadt:

Stefan Roth,: http://www.igp.ethz.ch/photogrammetry/

【Deep learning】Defense against Universal Adversarial Perturbations

NAVEED AKHTAR, UNIVERSITY OF WESTERN AUSTRALI:

Jian Liu, UWA:

Ajmal Mian, UWA:

【Deep learning】Hierarchical Recurrent Attention Networks for Structured Online Maps

Namdar Homayounfar, Uber ATG:

Wei-Chiu Ma, MIT:

Shrinidhi Kowshika Lakshmikanth, Uber ATG:

Raquel Urtasun, University of Toronto: http://www.cs.toronto.edu/~urtasun/

【Deep learning】Generative Modeling using the Sliced Wasserstein Distance

Ishan Deshpande, UIUC:

Ziyu Zhang, Snap Research:

Alex Schwing,:

【Deep learning】Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks

Tom Veniat, Lip6 – MLIA:

Ludovic Denoyer, UPMC:

【Deep learning】Wasserstein Introspective Neural Networks

Kwonjoon Lee, UC San Diego:

Weijian Xu, UC San Diego:

Fan Fan, UC San Diego:

Zhuowen Tu, UCSD, USA: http://pages.ucsd.edu/~ztu/

【Deep learning】Empirical study of the topology and geometry of deep networks

Alhussein Fawzi,:

Seyed-Mohsen Moosavi-Dezfooli,:

Pascal Frossard,:

Stefano Soatto, UCLA: http://vision.ucla.edu/projects.html

【Deep learning】Environment Upgrade Reinforcement Learning for Non-differentiable Multi-stage Pipelines

Shuqin Xie, SJTU:

Cewu Lu, Shanghai Jiao Tong University:

Zitian Chen, Fudan University:

Chao Xu, Shanghai Jiao Tong University:

【Deep learning】Left/Right Asymmetric Layer Skippable Networks

Changmao Cheng, Fudan University:

Yanwei Fu, fudan:

Yu-Gang Jiang, Fudan University:

Wei Liu,:

wenlian Lu, Fudan:

Jianfeng Feng, fudan university:

Xiangyang Xue,:

【Deep learning】Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

Daesik Kim, Seoul National University:

Young Joon Yoo,:

Jee Soo Kim, Seoul national university:

Sang Kuk Lee, Seoul National University:

Nojun Kwak, Seoul National University:

【Deep learning】SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks

Julian Faraone, University of Sydney:

Nicholas Fraser, Xilinx:

Michaela Blott, Xilinx:

Philip Leong,:

【Deep learning】A General Two-Step Quantization Approach for Low-bit Neural Networks with High Accuracy

Peisong Wang, CASIA:

Qinghao Hu, Chinese Academy of Sciences:

Yifan Zhang, CASIA:

Jian Cheng, Chinese Academy of Sciences: http://www.nlpr.ia.ac.cn/jcheng/

【Deep learning】Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks

Nick Johnston, Google:

Damien Vincent, google.com:

David Minnen, google.com:

Michele Covell, google.com:

Saurabh Singh, Univ. of Illinois at Urbana-Champaign:

Sung Jin Hwang, google.com:

George Toderici, Google:

Troy Chinen, google.com:

Joel Shor, google.com:

【Deep learning】Conditional Probability Models for Deep Image Compression

Eirikur Agustsson, ETH Zurich:

Fabian Mentzer, ETHZ Zürich:

Michael Tschannen, ETH Zurich:

Radu Timofte, ETH Zurich:

Luc Van Gool, KTH: http://www.vision.ee.ethz.ch/

【Deep learning】Deep Diffeomorphic Transformer Networks

Nicki Skafte Detlefsen, DTU:

Oren Freifeld, Ben-Gurion University:

Soren Hauberg, Technical University of Denmark:

【Deep learning】The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks

Maxim Berman, ESAT-PSI, KU Leuven:

Amal Rannen Triki, KU Leuven:

Matthew Blaschko, KU Leuven:

【Deep learning】Generative Adversarial Perturbations

Omid Poursaeed, Cornell University:

Isay Katsman, Cornell University:

Bicheng Gao, Shanghai Jiao Tong University:

Serge Belongie,: http://vision.ucsd.edu/person/serge-belongie

【Deep learning】Learning Strict Identity Mappings in Deep Residual Networks

Xin Yu, University of Utah:

Srikumar Ramalingam,:

Zhiding Yu, Carnegie Mellon University:

【Deep learning】Geometric robustness of deep networks: analysis and improvement

Can Kanbak, EPFL:

Seyed-Mohsen Moosavi-Dezfooli,:

Pascal Frossard,:

【Deep learning】Geometry Aware Optimization for Deep Learning: The Good Practice

SOUMAVA KUMAR ROY, AUSTRALIAN NATIONAL UNIVERSITY:

Zakaria Mhammedi, Data61, CSIRO:

Mehrtash Harandi, Australian National University:

【Deep learning】Sim2Real View Invariant Visual Servoing by Recurrent Control

Fereshteh Sadeghi, University of Washington:

Alexander Toshev, Google:

Sergey Levine, UC Berkeley:

【Deep learning】Independently Recurrent Neural Network

Shuai Li, University of Wollongong:

Wanqing Li,:

Chris Cook, University of Wollongong:

Ce Zhu, University of Electronic Science and Technology of China:

Yanbo Gao, University of Electronic Science and Technology of China:

【Deep learning】Diverse Net: When One Right Answer Is Not Enough

Michael Firman, UCL:

Neill Campbell, University of bath:

Lourdes Agapito, University College London:

Gabriel Brostow, University College London UK:

【Deep learning】In-Place Activated Batch Norm for Memory-Optimized Training of DNNs

Samuel Rota Bulo’, Mapillary Research:

Lorenzo Porzi, Mapillary Research:

Peter Kontschieder,:

【Deep learning】Shuffle Net: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Xiangyu Zhang, Megvii Inc:

Xinyu Zhou, Megvii Technology Inc.:

Mengxiao Lin, Megvii Technology Ltd.(Face++):

Jian Sun,: http://research.microsoft.com/en-us/groups/vc/

【Deep learning】Squeeze-and-Excitation Networks

Jie Hu, Momenta:

Li Shen, University of Oxford:

Gang Sun, Momenta:

【Deep learning】Iterative Visual Reasoning Beyond Convolutions

Xinlei Chen, Facebook:

Li-jia Li, Google Inc:

Fei-Fei Li, Google Inc.: http://vision.stanford.edu/resources_links.html

Abhinav Gupta,: http://www.cs.cmu.edu/~abhinavg/

【Deep learning】VITON: An Image-based Virtual Try-on Network

Xintong Han, University of Maryland:

Zuxuan Wu, University of Maryland:

Zhe Wu, University of Maryland:

Ruichi Yu,:

Larry Davis, University of Maryland, USA: http://www.umiacs.umd.edu/~lsd/

【Deep learning】Pack Net: Adding Multiple Tasks to a Single Network by Iterative Pruning

Arun Mallya, UIUC:

Lana Lazebnik,:

【Deep learning】Non-local Neural Networks

Xiaolong Wang, Carnegie Mellon University:

Ross Girshick,: http://www.cs.berkeley.edu/~rbg/

Abhinav Gupta,: http://www.cs.cmu.edu/~abhinavg/

Kaiming He,: http://research.microsoft.com/en-us/um/people/kahe/

【Deep learning】CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization

Frederick Tung, Simon Fraser University:

Greg Mori,: http://www.cs.sfu.ca/~mori/

【Deep learning】clc Net: Improving the Efficiency of Convolutional Neural Network using Channel Local Convolutions

Dongqing Zhang, Imagination AI LLC:

【Deep learning】Towards Effective Low-bitwidth Convolutional Neural Networks

Bohan Zhuang, The University of Adelaide:

Chunhua Shen, University of Adelaide:

Mingkui Tan, South China University of Technology:

Lingqiao Liu, University of Adelaide:

Ian Reid,: http://www.robots.ox.ac.uk/~ian/

【Deep learning】Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

Jason Kuen, NTU, Singapore:

Xiangfei Kong, Nanyang Technological University:

Zhe Lin, Adobe Systems, Inc.: http://www.adobe.com/technology/people/san-jose/zhe-lin.html

Gang Wang,:

Jianxiong Yin, NVIDIA:

Simon See, NVIDIA:

Yap-Peng Tan,:

【Deep learning】Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation

Qingchao Chen, Unviersity College London:

Yang Liu, University of Cambridge:

Zhaowen Wang, Adobe:

Ian Wassell,:

Kevin Chetty,:

【Deep learning】Matching Adversarial Networks

Gellert Mattyus, UBER ATG:

Raquel Urtasun, University of Toronto: http://www.cs.toronto.edu/~urtasun/

【Deep learning】People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting

Mark Marsden, Dublin City University:

Kevin Mc Guinness, DCU:

Suzanne Little, DCU:

Ciara Keogh, University College Dublin, Ireland:

Noel O’Connor, DCU:

【Deep learning】OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning

Jose Lezama, Universidad de la Republica, Uruguay:

Qiang Qiu,:

Pablo Musé, Universidad de la Republica, Uruguay:

Guillermo Sapiro, Duke:

【Deep learning】Efficient parametrization of multi-domain deep neural networks

Sylvestre-Alvise Rebuffi, University of Oxford:

Hakan Bilen, University of Oxford:

Andrea Vedaldi, U Oxford: http://www.robots.ox.ac.uk/~vedaldi/index.html

【Deep learning】Importance Weighted Adversarial Nets for Partial Domain Adaptation

Jing Zhang, University of Wollongong:

Zewei Ding, University of Wollongong:

Wanqing Li,:

Philip Ogunbona, University of Wollongong:

【Deep learning】Visual Feature Attribution using Wasserstein GANs

Christian Baumgartner, ETH Zurich:

Lisa Koch, ETH Zurich:

Kerem Tezcan, ETH Zurich:

Jia Xi Ang, ETH Zurich:

Ender Konukoglu, ETH Zurich:

【Deep learning】Detail-Preserving Pooling in Deep Networks

Faraz Saeedan, TU Darmstadt:

Nicolas Weber,:

Michael Goesele, TU Darmstadt:

Stefan Roth,: http://www.igp.ethz.ch/photogrammetry/

【Deep learning】Multi-Agent Diverse Generative Adversarial Networks

Viveka Kulharia, University of Oxford:

Arnab Ghosh, University of Oxford:

Vinay P. Namboodiri, Indian Institute of Technology Kanpur:

Phil Torr, Oxford:

Puneet Kumar Dokania, University of Oxford:

【Deep learning】A PID Controller Approach for Stochastic Optimization of Deep Networks

An Wangpeng , Tsinghua University:

Haoqian Wang, Tsinghua University, Shenzhen Graduate School:

Qingyun Sun, Stanford Univsersity:

Jun Xu, Hong Kong Polytechnic U:

QIonghai Dai, Tsinghua University:

Lei Zhang, The Hong Kong Polytechnic University: http://www4.comp.polyu.edu.hk/~cslzhang/

【Deep learning】Learning-Compression” algorithms for neural net pruning”

Miguel Carreira-perpinan, UC Merced:

Yerlan Idelbayev, UC Merced:

【Deep learning】Large-scale Distance Metric Learning with Uncertainty

Qi Qian, Alibaba Group:

Shenghuo Zhu, Alibaba Group: http://www.nec-labs.com/~zsh/

Rong Jin, Alibaba Group:

Jiasheng Tang, Alibaba Group:

Hao Li, Alibaba Group:

【Deep learning】Art of singular vectors and universal adversarial perturbations

Valentin Khrulkov, Skoltech:

Ivan Oseledets, Skoltech:

【Deep learning】Learning Nested Structures in Deep Neural Networks

Eunwoo Kim, Seoul National University:

Chanho Ahn, Seoul National University:

Songhwai Oh, Seoul National University:

【Deep learning】Context Embedding Networks

Kun ho Kim, Caltech:

Oisin Mac Aodha, Caltech:

Pietro Perona, California Institute of Technology, USA: http://vision.caltech.edu/Perona.html

【Deep learning】SBNet: Sparse Block’s Network for Fast Inference

Mengye Ren, Uber ATG:

Andrei Pokrovsky, Uber ATG:

Bin Yang, Uber ATG, Uof T:

Raquel Urtasun, University of Toronto: http://www.cs.toronto.edu/~urtasun/

【Deep learning】Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks

Ruth Fong, University of Oxford:

Andrea Vedaldi, U Oxford: http://www.robots.ox.ac.uk/~vedaldi/index.html

【Deep learning】Block Drop: Dynamic Inference Paths in Residual Networks

Zuxuan Wu, University of Maryland:

Tushar Nagarajan, University of Texas at Austin:

Abhishek Kumar,:

Steven Rennie,:

Larry Davis, University of Maryland, USA: http://www.umiacs.umd.edu/~lsd/

Kristen Grauman,: http://www.cs.utexas.edu/~grauman/

Rogerio Feris, IBM: http://rogerioferis.com/

【Deep learning】Interpretable Convolutional Neural Networks

Quanshi Zhang, UCLA:

Yingnian Wu,:

Song-Chun Zhu,: http://www.stat.ucla.edu/~sczhu/

【Deep learning】Interleaved Structured Sparse Convolutional Neural Networks

Guotian Xie, Sun Yat-Sen University:

Ting Zhang, Microsoft Research Asia:

Jianhuang Lai, Sun Yat-sen University:

Jingdong Wang, Microsoft Research:

【Deep learning】Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

Yen-Cheng Liu, National Taiwan University:

Yu-Ying Yeh, National Taiwan University:

Tzu-Chien Fu, Northwestern University:

Wei-Chen Chiu, National Chiao Tung University:

Sheng-De Wang, National Taiwan University:

Yu-Chiang Frank Wang, Academia Sinica: http://www.citi.sinica.edu.tw/pages/ycwang/index_en.html

【Deep learning】Deep Learning under Privileged Information Using Heteroscedastic Dropout

Ozan Sener, Stanford University:

Silvio Savarese,: http://cvgl.stanford.edu/silvio/

John Lambert, Stanford University:

【Deep learning】Interpret Neural Networks by Identifying Critical Data Routing Paths

Yulong Wang, Tsinghua University:

Hang Su, Tsinghua University:

Xiaolin Hu, tsinghua:

【Deep learning】Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

Bichen Wu, UC Berkeley:

Xiangyu Yue, UC Berkeley:

Alvin Wan, UC Berkeley:

Peter Jin, UC Berkeley:

Sicheng Zhao, UC Berkeley:

Noah Golmant, UC Berkeley:

Amir Gholaminejad, UC Berkeley:

Joseph Gonzalez, UC Berkeley:

Kurt Keutzer, UC Berkeley:

【Deep learning】Learning Multi-grid Generative Conv Nets by Minimal Contrastive Divergence

Ruiqi Gao, UCLA:

Yang Lu, University of California Los Angeles:

Junpei Zhou,:

Song-Chun Zhu,: http://www.stat.ucla.edu/~sczhu/

Yingnian Wu,:

【Deep learning】Boosting Adversarial Attacks with Momentum

Yinpeng Dong, Tsinghua Univeristy:

Fangzhou Liao, Tsinghua University:

Tianyu Pang, Tsinghua University:

Hang Su, Tsinghua University:

Jun Zhu, Tsinghua University:

Xiaolin Hu, tsinghua:

Jianguo Li, Intel Lab:

【Deep learning】NISP: Pruning Networks using Neuron Importance Score Propagation

Ruichi Yu,:

Ang Li, Google Deep Mind:

Chun-Fu (Richard) Chen, IBM T.J. Watson Research Cente:

Jui-Hsin Lai,:

Vlad Morariu, University of Maryland:

Xintong Han, University of Maryland:

Mingfei Gao, University of Maryland:

Ching-Yung Lin,:

Larry Davis, University of Maryland, USA: http://www.umiacs.umd.edu/~lsd/

【Deep learning】Tell Me Where To Look: Guided Attention Inference Network

Kunpeng Li, Northeastern University:

Ziyan Wu, Siemens Corporation:

Kuan-Chuan Peng, Siemens Corporation:

Jan Ernst, Siemens Corporation:

Yun Fu, Northeastern University:

【Deep learning】Wide Compression: Tensor Ring Nets

Wenqi Wang, Purdue University:

YIfan Sun, Technicolor Research:

Brian Eriksson, Adobe:

Wenlin Wang, Duke University:

Vaneet Aggarwal, Purdue University:

【Deep learning】Learning Structure and Strength of CNN Filters for Small Sample Size Training

Rohit Keshari, IIIT Delhi:

Mayank Vatsa, IIIT Dehli:

Richa Singh, IIT Dehli:

Afzel Noore, WVU:

【Deep learning】Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition

Jinmian Ye, University of Electronic Science and Technology of China:

Linnan Wang, Brown:

Guangxi Li, UESTC:

Di Chen,:

Shandian Zhe, School of Computing, University of Utah:

Zenglin Xu, University of Electronic Science and Technology of China:

【Deep learning】Spatially-Adaptive Filter Units for Deep Neural Networks

Domen Tabernik, University of Ljubljana:

Matej Kristan, University of Ljubljana:

Ales Leonardis, University of Birmingham, UK:

【Deep learning】SGAN: An Alternative Training of Generative Adversarial Networks

Tatjana Chavdarova, Idiap and EPFL:

Francois Fleuret, Idiap Research Institute:

【Deep learning】Explicit Loss-Error-Aware Quantization for Deep Neural Networks

Aojun Zhou, Intel labs china:

Anbang Yao,:

【PointCloud analysis】Folding Net: Interpretable Unsupervised Learning on 3D Point Clouds

Yaoqing Yang, Carnegie Mellon University:

Chen Feng, MERL:

Yiru Shen, Clemson University:

Dong Tian, Mitsubishi Electric Research Laboratories:

【PointCloud analysis】Point Fusion: Deep Sensor Fusion for 3D Bounding Box Estimation

Danfei Xu, Stanford Univesity:

dragomir Anguelov, Zoox Inc.:

Ashesh Jain, Zoox Inc.:

【PointCloud analysis】GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition

Yifan Feng, Xidian university:

Zizhao Zhang,:

xibin Zhao,:

Rongrong Ji,:

Yue Gao, Tsinghua University:

【PointCloud analysis】Learning 3D Shape Completion from Point Clouds with Weak Supervision

David Stutz, MPI Saarbruecken:

Andreas Geiger, MPI Tuebingen / ETH Zuerich:

【PointCloud analysis】SPLATNet: Sparse Lattice Networks for Point Cloud Processing

Hang Su, University of Massachusetts, Amherst:

Varun Jampani, NVIDIA Research:

Deqing Sun, NVIDIA: http://cs.brown.edu/~dqsun/index.html

Evangelos Kalogerakis, UMass:

Subhransu Maji,: http://people.cs.umass.edu/~smaji/

Ming-Hsuan Yang, UC Merced: http://faculty.ucmerced.edu/mhyang/

Jan Kautz, NVIDIA:

【PointCloud analysis】PU-Net: Point Cloud Upsampling Network

Lequan Yu, The Chinese University of Hong:

XIANZHI LI, CUHK:

Chi-Wing Fu,:

Daniel Cohen-Or,:

Pheng-Ann Heng,:

【PointCloud analysis】Inverse Composition Discriminative Optimization for Point Cloud Registration

Jayakorn Vongkulbhisal, Carnegie Mellon University:

Beñat Irastorza Ugalde,:

Fernando de la Torre,:

João Costeira,:

【PointCloud analysis】3D-RCNN: Instance-level 3D Scene Understanding via Render-and-Compare

Abhijit Kundu, Georgia Institute of Technology:

Yin Li, Georgia Tech:

James Rehg, Georgia Institute of Technology: http://www.cc.gatech.edu/~rehg/

【PointCloud analysis】Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net

Wenjie Luo, Uber ATG.:

Bin Yang, Uber ATG, Uof T:

Raquel Urtasun, University of Toronto: http://www.cs.toronto.edu/~urtasun/

【PointCloud analysis】Density Adaptive Point Set Registration

Felix Järemo Lawin, Linköping University:

Martin Danelljan,:

Fahad Khan, Computer Vision Laboratory, Linkoping University , Sweden:

Per-Erik Forssen, Linkoping University:

Michael Felsberg, Link_ping University:

【PointCloud analysis】Tangent Convolutions for Dense Prediction in 3D

Maxim Tatarchenko, Freiburg:

Jaesik Park, Intel Labs:

Qian-Yi Zhou, ABQ Technologies:

Vladlen Koltun, Intel Labs: http://vladlen.info/publications/

【PointCloud analysis】A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation

Lukas Rahmann,:

Riccardo Roveri, ETH Zurich:

Cengiz Oztireli,:

Markus Gross,:

【PointCloud analysis】Point Net VLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition

Mikaela Angelina Uy, NUS:

Gim Hee Lee, National University of SIngapore:

【PointCloud analysis】Voxel Net: End-to-End Learning for Point Cloud Based 3D Object Detection

Yin Zhou, Lawrence Berkeley National Lab:

Oncel Tuzel,:

【PointCloud analysis】Neighbors Do Help: Deeply Exploiting Local Structures of Point Clouds

Yiru Shen, Clemson University:

Chen Feng, MERL:

Yaoqing Yang, Carnegie Mellon University:

Dong Tian, Mitsubishi Electric Research Laboratories:

【PointCloud analysis】Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

Loic Landrieu, IGN:

Martin Simonovsky, Universite Paris Est, ENPC:

【PointCloud analysis】Attentional Shape Context Net for Point Cloud Recognition

Saining Xie, UCSD:

Sainan Liu, UCSD:

Zeyu Chen, UCSD:

Zhuowen Tu, UCSD, USA: http://pages.ucsd.edu/~ztu/

【PointCloud analysis】Variational Autoencoders for Deforming 3D Mesh Models

Qingyang Tan, UCAS:

Lin Gao, Chinese Academy of Sciences:

Yu-Kun Lai, Cardiff University:

Shihong Xia, Institute of Computing Technology, CAS, Beijing, China:

【PointCloud analysis】De LS-3D: Deep Localization and Segmentation with a 3D Semantic Map

Peng Wang, Baidu:

Ruigang Yang, University of Kentucky: http://vis.uky.edu/~ryang/

Binbin Cao, Baidu:

Wei Xu,:

Yuanqing Lin,:

【PointCloud analysis】Alive Caricature from 2D to 3D

Qianyi Wu, USTC:

Juyong Zhang, University of Science and Technology of China:

Yu-Kun Lai, Cardiff University:

Jianmin Zheng, Nanyang Technological University:

Jianfei Cai,: http://www3.ntu.edu.sg/home/asjfcai/

【PointCloud analysis】Pixar: Real-time 3D Object Detection from Point Clouds

Bin Yang, Uber ATG, Uof T:

Wenjie Luo, Uber ATG.:

Raquel Urtasun, University of Toronto: http://www.cs.toronto.edu/~urtasun/

【PointCloud analysis】3D Semantic Segmentation with Submanifold Sparse Convolutional Networks

Benjamin Graham, Facebook AI Research:

Laurens van der Maaten, Facebook:

Martin Engelcke, University of Oxford:

【PointCloud analysis】SO-Net: Self-Organizing Network for Point Cloud Analysis

Jiaxin Li, National University of Singapore:

Ben Chen, National Univ of Singapore:

Gim Hee Lee, National University of SIngapore: