2018 ECCV

下面是2018 ECCV文章的主题标签,文章列表来源于https://eccv2018.org/program/main-conference/
Topic: Scene parsing; Object segmentation; Image segmentation; Video segmentation; Boundary detection; Contour analysis; Object tracking; Action recognition; Video analysis; Human detection; Human parsing; Face recognition; Face parsing; Object recognition; Object detection; Saliency detection; Scene recognition; Image retrieval; 3D modeling; Feature matching; Motion estimation; Stereo matching; Optical flow; Region matching; Image editing; Computational photography; Texture analysis; Machine learning; GAN; Deep learning; Pointcloud analysis;

【Scene parsing】Efficient Uncertainty Estimation for Semantic Segmentation in Videos

Po-Yu Huang, National Tsing Hua University:

Wan-Ting Hsu, National Tsing Hua University:

Chun-Yueh Chiu, National Tsing Hua University:

Tingfan Wu, Umbo Computer Vision:

Min Sun, NTHU: http://aliensunmin.github.io/

【Scene parsing】Adaptive Affinity Field for Semantic Segmentation

Tsung-Wei Ke, UC Berkeley / ICSI:

Jyh-Jing Hwang, UC Berkeley / ICSI:

Ziwei Liu, UC Berkeley / ICSI:

Stella Yu, UC Berkeley / ICSI: http://www1.icsi.berkeley.edu/~stellayu/

【Scene parsing】Effective Use of Synthetic Data for Urban Scene Semantic Segmentation

Fatemeh Sadat Saleh, Australian National University (ANU):

Mohammad Sadegh Aliakbarian, Data61:

Mathieu Salzmann, EPFL:

Lars Petersson, Data61/CSIRO:

Jose Manuel Alvarez, Toyota Research Institute: https://rsu.data61.csiro.au/people/jalvarez/

【Scene parsing】Multi-Scale Context Intertwining for Semantic Segmentation

Di Lin, Shenzhen University:

Yuanfeng Ji, Shenzhen University:

Dani Lischinski, The Hebrew University of Jerusalem: http://www.cs.huji.ac.il/~danix/

Danny Cohen-Or, Tel Aviv University:

Hui Huang, Shenzhen University: http://vcc.szu.edu.cn/~huihuang

【Scene parsing】ICNet for Real-Time Semantic Segmentation on High-Resolution Images

Hengshuang Zhao, The Chinese University of Hong Kong:

Xiaojuan Qi, CUHK:

Xiaoyong Shen, CUHK:

Jianping Shi, Sensetime Group Limited:

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

【Scene parsing】Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training

Yang Zou, Carnegie Mellon University:

Zhiding Yu, NVIDIA:

  1. V. K. Vijaya Kumar, CMU, USA:

Jinsong Wang, General Motors:

【Scene parsing】End-to-End Joint Semantic Segmentation of Actors and Actions in Video

Jingwei Ji, Stanford University:

Shyamal Buch, Stanford University:

Alvaro Soto, Universidad Catolica de Chile:

Juan Carlos Niebles, Stanford University: http://www.niebles.net/

【Scene parsing】Unified Perceptual Parsing for Scene Understanding

Tete Xiao, Peking University:

Yingcheng Liu, Peking University:

Yuning Jiang, Megvii(Face++) Inc:

Bolei Zhou, MIT:

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

【Scene parsing】Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Liang-Chieh Chen, Google Inc.:

Yukun Zhu, Google Inc.:

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

Florian Schroff, Google Inc.:

Hartwig Adam, Google:

【Scene parsing】Urban Zoning Using Higher-Order Markov Random Fields on Multi-View Imagery Data

Tian Feng, University of New South Wales:

Quang-Trung Truong, SUTD:

Thanh Nguyen, Deakin University, Australia:

Jing Yu Koh, SUTD:

Lap-Fai Yu, UMass Boston:

Sai-Kit Yeung, Singapore University of Technology and Design: http://www.saikit.org/

Alexander Binder, Singapore University of Technology and Design:

【Scene parsing】Learning to Drive with 360° Surround-View Cameras and a Map

Simon Hecker, ETH Zurich:

Dengxin Dai, ETH Zurich:

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

【Scene parsing】Monocular Scene Parsing and Reconstruction using 3D Holistic Scene Grammar

Siyuan Huang, UCLA:

Siyuan Qi, UCLA:

Yixin Zhu, UCLA:

Yinxue Xiao, University of California, Los Angeles:

Yuanlu Xu, University of California, Los Angeles:

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

【Scene parsing】Audio-Visual Scene Analysis with Self-Supervised Multisensory Features

Andrew Owens, UC Berkeley:

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

【Scene parsing】Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation

Chaowei Xiao, University of Michigan, Ann Arbor:

Ruizhi Deng, Simon Fraser University:

Bo Li, University of Illinois at Urbana–Champaign and UC Berkeley:

Fisher Yu, UC Berkeley:

Mingyan Liu, University of Michigan, Ann Arbor:

Dawn Song, UC Berkeley: https://people.eecs.berkeley.edu/~dawnsong/

【Scene parsing】PSANet: Point-wise Spatial Attention Network for Scene Parsing

Hengshuang Zhao, The Chinese University of Hong Kong:

Yi ZHANG, The Chinese University of Hong Kong:

Shu Liu, CUHK:

Jianping Shi, Sensetime Group Limited:

Chen Change Loy, Chinese University of Hong Kong: http://www.eecs.qmul.ac.uk/~ccloy/

Dahua Lin, The Chinese University of Hong Kong: http://dahua.me/

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

【Scene parsing】D2S: Densely Segmented Supermarket Dataset

Patrick Follmann, MVTec Software Gmb H:

Tobias Böttger, MVTec Software Gmb H:

Philipp Härtinger, MVTec Software Gmb H:

Rebecca König, MVTec Software Gmb H:

【Scene parsing】3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation

Angela Dai, Stanford University:

Matthias Niessner, Technical University of Munich: http://niessnerlab.org/publications.html

【Scene parsing】Ex Fuse: Enhancing Feature Fusion for Semantic Segmentation

Zhenli Zhang, Fudan University:

Xiangyu Zhang, Megvii Inc:

Chao Peng, Megvii(Face++) Inc:

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

【Scene parsing】ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

Sachin Mehta, University of Washington:

Mohammad Rastegari, Allen Institute for Artificial Intelligence:

Anat Caspi, University of Washington:

Linda Shapiro, University of Washington: http://homes.cs.washington.edu/~shapiro/

Hannaneh Hajishirzi, University of Washington: https://homes.cs.washington.edu/~hannaneh/

【Scene parsing】Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation

Zhenyu Zhang, Nanjing University of Sci & Tech:

Zhen Cui, Nanjing University of Science and Technology:

Zequn Jie, Tencent AI Lab:

Xiang Li, NJUST:

Chunyan Xu, Nanjing University of Science and Technology:

Jian Yang, Nanjing University of Science and Technology:

【Scene parsing】Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation

Ruochen Fan, Tsinghua University:

Qibin Hou, Nankai University:

Ming-Ming Cheng, Nankai University:

Gang Yu, Face++:

Ralph Martin, Cardiff University:

Shimin Hu, Tsinghua University: http://cg.cs.tsinghua.edu.cn/prof_hu.htm

【Scene parsing】Efficient Semantic Scene Completion Network with Spatial Group Convolution

Jiahui Zhang, Tsinghua University:

Hao Zhao, Intel Labs China:

Anbang Yao, Intel Labs China:

Yurong Chen, Intel Labs China:

Hongen Liao, Tsinghua University: http://at3d.med.tsinghua.edu.cn/en/members/Professor.html

【Scene parsing】Concept Mask: Large-Scale Segmentation from Semantic Concepts

Yufei Wang, Facebook:

Zhe Lin, Adobe Research: http://www.adobe.com/technology/people/san-jose/zhe-lin.html

Xiaohui Shen, Adobe Research:

Scott Cohen, Adobe Research:

Jianming Zhang, Adobe Research:

【Scene parsing】Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation

Xinge Zhu, Sensetime Group Limited:

Hui Zhou, Sensetime Group Limited.:

Ceyuan Yang, Sense Time Group Limited:

Jianping Shi, Sensetime Group Limited:

Dahua Lin, The Chinese University of Hong Kong: http://dahua.me/

【Scene parsing】Bi Se Net: Bilateral Segmentation Network for Real-time Semantic Segmentation

Changqian Yu, Huazhong University of Science and Technology:

Jingbo Wang, Peking University:

Chao Peng, Megvii(Face++) Inc:

Changxin Gao, Huazhong University of Science and Technology:

Gang Yu, Face++:

Nong Sang, School of Automation, Huazhong University of Science and Technology:

【Scene parsing】Generative Semantic Manipulation with Mask-Contrasting GAN

Xiaodan Liang, Carnegie Mellon University:

【Scene parsing】Semantic Scene Understanding under Dense Fog with Synthetic and Real Data

Christos Sakaridis, ETH Zurich:

Dengxin Dai, ETH Zurich:

Simon Hecker, ETH Zurich:

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

【Scene parsing】On Regularized Losses for Weakly-supervised CNN Segmentation

Meng Tang, University of Waterloo:

Ismail Ben Ayed, ETS:

Federico Perazzi, Disney Research:

Abdelaziz Djelouah, Disney Research:

Christopher Schroers, Disney Research:

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

【Object segmentation】Semi-convolutional Operators for Instance Segmentation

Samuel Albanie, University of Oxford:

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

David Novotny, Oxford University:

Diane Larlus, Naver Labs Europe:

【Object segmentation】Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation

Xiaoxiao Li, The Chinese University of Hong Kong:

Chen Change Loy, Chinese University of Hong Kong: http://www.eecs.qmul.ac.uk/~ccloy/

【Object segmentation】Affinity Derivation and Graph Merge for Instance Segmentation

Yiding Liu, University of Science and Technology of China:

Siyu Yang, Beihang University:

Bin Li, Microsoft Research Asia:

Wengang Zhou, University of Science and Technology of China:

Ji-Zeng Xu, Microsoft Research Asia:

Houqiang Li, University of Science and Technology of China:

Yan Lu, Microsoft Research Asia:

【Object segmentation】Unsupervised Video Object Segmentation with Motion-based Bilateral Networks

Siyang Li, University of Southern California:

Bryan Seybold, Google Inc.:

Alexey Vorobyov, Google Inc.:

Xuejing Lei, University of Southern California:

C.-C. Jay Kuo, USC:

【Object segmentation】You Tube-VOS: Sequence-to-Sequence Video Object Segmentation

Ning Xu, Adobe Research:

Linjie Yang, Snap Research:

Dingcheng Yue, UIUC:

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

Brian Price, Adobe:

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

Scott Cohen, Adobe Research:

Yuchen Fan, Image Formation and Processing (IFP) Group, University of Illinois at Urbana-Champaign:

Yuchen Liang, UIUC:

Thomas Huang, University of Illinois at Urbana Champaign:

【Object segmentation】Video Match: Matching based Video Object Segmentation

Yuan-Ting Hu, University of Illinois at Urbana-Champaign:

Jia-Bin Huang, Virginia Tech:

Alexander Schwing, UIUC: http://www.alexander-schwing.de/

【Object segmentation】Learning to Segment via Cut-and-Paste

Tal Remez, Tel-Aviv University:

Matthew Brown, Google:

Jonathan Huang, Google: https://ai.google/research/people/JonathanHuang

【Object segmentation】Bayesian Instance Segmentation in Open Set World

Trung Pham, NVIDIA:

Vijay Kumar B G, University of Adelaide:

Thanh-Toan Do, The University of Adelaide:

Gustavo Carneiro, University of Adelaide: http://cs.adelaide.edu.au/~carneiro/research.html

Ian Reid, University of Adelaide, Australia: http://www.robots.ox.ac.uk/~ian/

【Object segmentation】Predicting Future Instance Segmentations by Forecasting Convolutional Features

Pauline Luc, Facebook AI Research:

Camille Couprie, Facebook:

yann lecun, Facebook:

Jakob Verbeek, INRIA: http://lear.inrialpes.fr/~verbeek/

【Object segmentation】SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaption

Wenqiang Xu, Shanghai Jiaotong University:

Yonglu Li, Shanghai Jiao Tong University:

Jun Lv, SJTU:

Cewu Lu, Shanghai Jiao Tong Univercity: http://mvig.sjtu.edu.cn/

【Object segmentation】Video Object Segmentation by Learning Location-Sensitive Embeddings

Hai Ci, Peking University:

Chunyu Wang, Microsoft Research asia:

Yizhou Wang, PKU:

【Object segmentation】A Dataset for Lane Instance Segmentation in Urban Environments

Brook Roberts, Five AI Ltd.:

Sebastian Kaltwang, Five AI Ltd.:

Sina Samangooei, Five AI Ltd.:

Mark Pender-Bare, Five AI Ltd.:

Konstantinos Tertikas, Five AI Ltd.:

John Redford, Five AI Ltd.:

【Object segmentation】Dynamic Multimodal Instance Segmentation guided by natural language queries

Edgar Margffoy-Tuay, Universidad de los Andes:

Emilio Botero, Universidad de los Andes:

Juan Pérez, Universidad de los Andes:

PABLO ARBELÁEZ, Universidad de los Andes:

【Object segmentation】Person Lab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model

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

Tyler Zhu, Google:

Liang-Chieh Chen, Google Inc.:

Spyros Gidaris, Ecole des Ponts Paris Tech:

Jonathan Tompson, Google:

Kevin Murphy, Google:

【Object segmentation】Sequential Clique Optimization for Video Object Segmentation

Yeong Jun Koh, Korea University:

Young-Yoon Lee, Samsung:

Chang-Su Kim, Korea university: http://mcl.korea.ac.kr/people/professor/

【Object segmentation】Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation

Xuan Chen, NUS:

Jun Hao Liew, NUS:

Wei Xiong, ASTAR Institute for Infocomm Research, Singapore:

Chee-Kong Chui, NUS:

Sim-Heng Ong, NUS:

【Object segmentation】Weakly- and Semi-Supervised, Non-Overlapping Instance Segmentation of Things and Stuff

Anurag Arnab, University of Oxford:

Philip Torr, University of Oxford: http://www.robots.ox.ac.uk/~tvg/

Qizhu Li, University of Oxford:

【Image segmentation】Key-Word-Aware Network for Referring Expression Image Segmentation

Hengcan Shi, University of Electronic Science and Technology of China:

Hongliang Li, University of Electronic Science and Technology of China:

Fanman Meng, University of Electronic Science and Technology of China:

Qingbo Wu, University of Electronic Science and Technology of China:

【Image segmentation】A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI

Zhiwen Fan, Xiamen University:

Liyan Sun, Xiamen University:

Xinghao Ding, Xiamen University:

Yue Huang, Xiamen University:

Congbo Cai, Xiamen University:

John Paisley, Columbia University: http://www.columbia.edu/~jwp2128/

【Image segmentation】Depth-aware CNN for RGB-D Segmentation

Weiyue Wang, USC:

Ulrich Neumann, USC: https://graphics.usc.edu/cgit/un.html

【Video segmentation】Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation

Yuan-Ting Hu, University of Illinois at Urbana-Champaign:

Jia-Bin Huang, Virginia Tech:

Alexander Schwing, UIUC: http://www.alexander-schwing.de/

【Boundary detection】Geometric Constrained Joint Lane Segmentation and Lane Boundary Detection

Jie Zhang, Shanghai Jiao Tong University:

Yi Xu, Shanghai Jiao Tong University:

Bingbing Ni, Shanghai Jiao Tong University:

Zhenyu Duan, Shanghai Jiao Tong University:

【Boundary detection】SEAL: A Framework Towards Simultaneous Edge Alignment and Learning

Zhiding Yu, NVIDIA:

Weiyang Liu, Georgia Tech:

Yang Zou, Carnegie Mellon University:

Chen Feng, Mitsubishi Electric Research Laboratories (MERL):

Srikumar Ramalingam, University of Utah:

  1. V. K. Vijaya Kumar, CMU, USA:

Kautz Jan, NVIDIA:

【Boundary detection】Learning to Predict Crisp Edge

Ruoxi Deng, Central South University:

Chunhua Shen, University of Adelaide: https://cs.adelaide.edu.au/~chhshen/

Shengjun Liu, Central South University:

Huibing Wang, Dalian University of Technology:

Xinru Liu, Central South University:

【Boundary detection】Interactive Boundary Prediction for Object Selection

Hoang Le, Portland State University:

Long Mai, Adobe Research:

Brian Price, Adobe:

Scott Cohen, Adobe Research:

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

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

【Contour analysis】Linear Span Network for Object Skeleton Detection

Chang Liu, University of Chinese Academy of Sciences:

Wei Ke, University of Chinese Academy of Sciences:

Fei Qin, University of Chinese Academy of Sciences:

Qixiang Ye, University of Chinese Academy of Sciences, China: https://ucassdl.cn/content/work/paper.html

【Contour analysis】Shape correspondences from learnt template-based parametrization

Thibault Groueix, École des ponts Paris Tech:

Bryan Russell, Adobe Research:

Mathew Fisher, Adobe Research:

Vladimir Kim, Adobe Research:

Mathieu Aubry, École des ponts Paris Tech: http://imagine.enpc.fr/~aubrym/

【Contour analysis】Model-free Consensus Maximization for Non-Rigid Shapes

Thomas Probst, ETH Zurich:

Ajad Chhatkuli , ETHZ:

Danda Pani Paudel, ETH Zürich:

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

【Contour analysis】Deep Shape Matching

Filip Radenovic, Visual Recognition Group, CTU Prague:

Giorgos Tolias, Vision Recognition Group, Czech Technical University in Prague:

Ondrej Chum, Vision Recognition Group, Czech Technical University in Prague: http://cmp.felk.cvut.cz/~chum/

【Contour analysis】Universal Sketch Perceptual Grouping

Ke LI, Queen Mary University of London:

Kaiyue Pang, Queen Mary University of London:

Jifei Song, Queen Mary, University of London:

Yi-Zhe Song, Queen Mary University of London:

Tao Xiang, Queen Mary, University of London, UK:

Timothy Hospedales, Edinburgh University: http://homepages.inf.ed.ac.uk/thospeda/

Honggang Zhang, Beijing University of Posts and Telecommunications:

【Contour analysis】Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching

Hanyu Wang, NLPR, Institute of Automation, Chinese Academy of Sciences:

Jianwei Guo, NLPR, Institute of Automation, Chinese Academy of Sciences:

Yan Dong-Ming, NLPR, CASIA:

Weize Quan, NLPR, Institute of Automation, Chinese Academy of Sciences:

Xiaopeng Zhang, Institute of Automation, Chinese Academy of Sciences:

【Contour analysis】Efficient Sliding Window Computation for NN-Based Template Matching

Lior Talker, Haifa University:

Yael Moses, IDC, Israel:

Ilan Shimshoni, University of Haifa: https://is-web.hevra.haifa.ac.il/index.php/en/publications-ilan-shimshoni

【Contour analysis】Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance

Zhixin Shu, Stony Brook University:

Mihir Sahasrabudhe, Centrale Supelec:

Alp Guler, INRIA:

Dimitris Samaras, Stony Brook University: http://www3.cs.stonybrook.edu/~samaras/

Nikos Paragios, Therapanacea:

Iasonas Kokkinos , UCL:

【Contour analysis】Improving Shape Deformation in Unsupervised Image-to-Image Translation

Aaron Gokaslan, Brown University:

Vivek Ramanujan, Brown University:

Daniel Ritchie, Brown University:

Kwang In Kim, University of Bath:

James Tompkin, Brown University: http://jamestompkin.com/

【Contour analysis】K-convexity shape priors for segmentation

Hossam Isack, UWO:

Lena Gorelick, University of Western Ontario:

Karin n G, University of Western Ontario:

Olga Veksler, University of Western Ontario: http://www.csd.uwo.ca/faculty/olga/

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

【Contour analysis】Learning 3D Shape Priors for Shape Completion and Reconstruction

Jiajun Wu, MIT:

Chengkai Zhang, MIT:

Xiuming Zhang, MIT:

Zhoutong Zhang, MIT:

Joshua Tenenbaum, MIT:

Bill Freeman, MIT: https://billf.mit.edu/

【Contour analysis】Deep Factorised Inverse-Sketching

Kaiyue Pang, Queen Mary University of London:

Da Li, QMUL:

Jifei Song, Queen Mary, University of London:

Yi-Zhe Song, Queen Mary University of London:

Tao Xiang, Queen Mary, University of London, UK:

Timothy Hospedales, Edinburgh University: http://homepages.inf.ed.ac.uk/thospeda/

【Contour analysis】Deep Cross-modality Adaptation via Semantics Preserving Adversarial Learning for Sketch-based 3D Shape Retrieval

Jiaxin Chen, New York University Abu Dhabi:

Yi Fang, New York University: https://wp.nyu.edu/mmvc/

【Contour analysis】Mask Text Spotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes

Pengyuan Lyu, Huazhong University of Science and Technology:

Minghui Liao, Huazhong University of Science and Technology:

Cong Yao, Megvii:

Wenhao Wu, Megvii:

Xiang Bai, Huazhong University of Science and Technology: http://cloud.eic.hust.edu.cn:8071/~xbai/

【Contour analysis】Learning 3D shapes as multi-layered height maps using 2D convolutional neural networks

Kripasindhu Sarkar, University of Kaiserslautern:

Basavaraj Hampiholi, University of Kaiserslautern:

Kiran Varanasi, German Research Center for Artificial Intelligence:

Didier Stricker, DFKI: https://av.dfki.de/members/stricker/

【Contour analysis】Sketchy Scene: Richly-Annotated Scene Sketches

Changqing Zou, University of Maryland (UMD):

Qian Yu, Queen Mary University of London:

Ruofei Du, UMD:

Haoran Mo, sun yat sen university:

Yi-Zhe Song, Queen Mary University of London:

Tao Xiang, Queen Mary, University of London, UK:

Chengying Gao, sun yat sen university:

Baoquan Chen, Shandong University: http://www.cs.sdu.edu.cn/~baoquan/

Hao Zhang, SFU:

【Object tracking】Efficient 6-Do F Tracking of Handheld Objects from an Egocentric Viewpoint

Rohit Pandey, Google:

Pavel Pidlypenskyi, Google:

Shuoran Yang, Google:

Christine Kaeser-Chen, Google:

【Object tracking】Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving

Peiliang LI, HKUST Robotics Institute:

Tong QIN, HKUST Robotics Institute:

Shaojie Shen, HKUST: http://uav.ust.hk/

【Object tracking】Tracking Net: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild

Matthias Müller, King Abdullah University of Science and Technology (KAUST):

Adel Bibi, KAUST:

Silvio Giancola, KAUST:

Salman Al-Subaihi, KAUST:

Bernard Ghanem, KAUST: http://www.bernardghanem.com/

【Object tracking】Unveiling the Power of Deep Tracking

Goutam Bhat, Linkoping University:

Joakim Johnander, Linköping University:

Martin Danelljan, Linkoping University:

Fahad Shahbaz Khan, Linköping University:

Michael Felsberg, Linköping University: http://users.isy.liu.se/cvl/mfe/

【Object tracking】Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World

Matteo Fabbri, University of Modena and Reggio Emilia:

Fabio Lanzi, University of Modena and Reggio Emilia:

SIMONE CALDERARA, University of Modena and Reggio Emilia, Italy:

Andrea Palazzi, University of Modena and Reggio Emilia:

ROBERTO VEZZANI, University of Modena and Reggio Emilia, Italy:

Rita Cucchiara, Universita Di Modena E Reggio Emilia: http://aimagelab.ing.unimore.it/imagelab/person.asp?idpersona=1

【Object tracking】Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers

Eunbyung Park, UNC-CHAPEL HILL:

Alex Berg, University of North Carolina, USA: http://acberg.com/

【Object tracking】Long-term Tracking in the Wild: a Benchmark

Efstratios Gavves, University of Amsterdam:

Luca Bertinetto, University of Oxford:

Joao Henriques, University of Oxford:

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

Philip Torr, University of Oxford: http://www.robots.ox.ac.uk/~tvg/

Ran Tao, University of Amsterdam:

Jack Valmadre, Oxford:

【Object tracking】Collaborative Deep Reinforcement Learning for Multi-Object Tracking

Liangliang Ren, Tsinghua University:

Zifeng Wang, Tsinghua University:

Jiwen Lu, Tsinghua University:

Qi Tian , The University of Texas at San Antonio:

Jie Zhou, Tsinghua University, China: https://www.tsinghua.edu.cn/publish/auen/1713/2011/20110506105532098625469/20110506105532098625469_.html

【Object tracking】Visual Tracking via Spatially Aligned Correlation Filters Network

mengdan zhang, Institute of Automation, Chinese Academy of Sciences:

qiang wang, Institute of Automation, Chinese Academy of Sciences:

Junliang Xing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences:

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

peixi peng, Institute of Automation, Chinese Academy of Sciences:

Weiming Hu, Institute of Automation,Chinese Academy of Sciences: http://people.ucas.ac.cn/~huweiming

Steve Maybank, University of London: http://www.dcs.bbk.ac.uk/~sjmaybank/

【Object tracking】Online Multi-Object Tracking with Dual Matching Attention Networks

Ji Zhu, Shanghai Jiao Tong University:

Hua Yang, Shanghai Jiao Tong University:

Nian Liu, Northwestern Polytechnical University:

Minyoung Kim, Perceptive Automata:

Wenjun Zhang, Shanghai Jiao Tong University:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

【Object tracking】Neural Nonlinear least Squares with Application to Dense Tracking and Mapping

Ronald Clark, Imperial College London:

Michael Bloesch, Imperial:

Jan Czarnowski, Imperial College London:

Andrew Davison, Imperial College London:

Stefan Leutenegger, Imperial College London: http://wp.doc.ic.ac.uk/sleutene/

【Object tracking】Real-time ‘Actor-Critic’ Tracking

Boyu Chen, Dalian University of Technology:

Dong Wang, Dalian University of Technology:

Peixia Li, Dalian University of Technology:

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

【Object tracking】Multi-object Tracking with Neural Gating using bilinear LSTMs

Chanho Kim, Georgia Tech:

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

James Rehg, Georgia Institute of Technology: http://www.cc.gatech.edu/~rehg/

【Object tracking】Deep Reinforcement Learning with Iterative Shift for Visual Tracking

Liangliang Ren, Tsinghua University:

Xin Yuan, Tsinghua University:

Jiwen Lu, Tsinghua University:

Ming Yang, Horizon Robotics:

Jie Zhou, Tsinghua University, China: https://www.tsinghua.edu.cn/publish/auen/1713/2011/20110506105532098625469/20110506105532098625469_.html

【Object tracking】Structured Siamese Network for Real-Time Visual Tracking

Yunhua Zhang, Dalian University of Technology:

Lijun Wang, Dalian University of Technology:

Dong Wang, Dalian University of Technology:

Mengyang Feng, Dalian University of Technology:

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

Jinqing Qi, Dalian University of Technology:

【Object tracking】Distractor-aware Siamese Networks for Visual Object Tracking

Zheng Zhu, CASIA:

Qiang Wang, University of Chinese Academy of Sciences:

Bo Li, sensetime:

Wu Wei, Sensetime:

Junjie Yan, Sensetime Group Limited: http://www.cbsr.ia.ac.cn/users/jjyan/main.htm

【Object tracking】Joint Representation and Truncated Inference Learning for Correlation Filter based Tracking

Yingjie Yao, Harbin Institute of technology:

Xiaohe Wu, Harbin Institute of technology:

Lei Zhang, University of Pittsburgh: http://www4.comp.polyu.edu.hk/~cslzhang/

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

Wangmeng Zuo, Harbin Institute of Technology, China:

【Object tracking】The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking

Dawei Du, University of Chinese Academy of Sciences:

Yuankai Qi, Harbin Institute of Technology:

Hongyang Yu, Harbin Institute of Technology:

Yifang Yang, University of Chinese Academy of Sciences:

Kaiwen Duan, University of Chinese Academy of Sciences:

guorong Li, CAS:

Weigang Zhang, Harbin Institute of Technology, Weihai:

Qingming Huang, University of Chinese Academy of Sciences:

Qi Tian , The University of Texas at San Antonio:

【Object tracking】Learning Dynamic Memory Networks for Object Tracking

Tianyu Yang, City University of Hong Kong:

Antoni Chan, City University of Hong Kong, Hong, Kong: http://www.cs.cityu.edu.hk/~abchan/

【Object tracking】Asynchronous, Photometric Feature Tracking using Events and Frames

Daniel Gehrig, University of Zurich:

Henri Rebecq, University of Zurich:

Guillermo Gallego, University of Zurich:

Davide Scaramuzza, University of Zurich& ETH Zurich, Switzerland: http://rpg.ifi.uzh.ch/people_scaramuzza.html

【Object tracking】Object Detection in Video with Spatiotemporal Sampling Networks

Gedas Bertasius, University of Pennsylvania:

Lorenzo Torresani, Dartmouth College:

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

【Object tracking】Dual-Agent Deep Reinforcement Learning for Deformable Face Tracking

Minghao Guo, Tsinghua University:

Jiwen Lu, Tsinghua University:

Jie Zhou, Tsinghua University, China: https://www.tsinghua.edu.cn/publish/auen/1713/2011/20110506105532098625469/20110506105532098625469_.html

【Object tracking】Combining 3D Model Contour Energy and Keypoints for Object Tracking

Bogdan Bugaev, Saint Petersburg Academic University:

Anton Kryshchenko, Saint Petersburg Academic University:

Roman Belov, Keen Tools:

【Object tracking】A Framework for Evaluating 6-DOF Object Trackers

Mathieu Garon, Université Laval:

Denis Laurendeau, Laval University:

Jean-Francois Lalonde, Université Laval: http://vision.gel.ulaval.ca/~jflalonde/

【Object tracking】Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking

Chenglong Li, Anhui University:

Chengli Zhu, Anhui University:

Yan Huang, Institute of Automation, Chinese Academy of Sciences:

Jin Tang, Anhui University:

Liang Wang, NLPR, China:

【Object tracking】Joint 3D tracking of a deformable object in interaction with a hand

Aggeliki Tsoli, FORTH:

Antonis Argyros, CSD-UOC and ICS-FORTH: http://users.ics.forth.gr/~argyros/

【Object tracking】Self-supervised Tracking by Colorization

Carl Vondrick, MIT:

Abhinav Shrivastava, UMD / Google:

Alireza Fathi, Google:

Sergio Guadarrama, Google:

Kevin Murphy, Google:

【Object tracking】Triplet Loss with Theoretical Analysis in Siamese Network for Real-Time Object Tracking

Xingping Dong, Beijing Institute of Technology:

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

【Object tracking】Deep Regression Tracking with Shrinkage Loss

Xiankai Lu, Shanghai Jiao Tong University:

Chao Ma, University of Adelaide:

Bingbing Ni, Shanghai Jiao Tong University:

Xiaokang Yang, Shanghai Jiao Tong University of China:

Ian Reid, University of Adelaide, Australia: http://www.robots.ox.ac.uk/~ian/

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

【Object tracking】Deep TAM: Deep Tracking and Mapping

Huizhong Zhou, University of Freiburg:

Benjamin Ummenhofer, University of Freiburg:

Thomas Brox, University of Freiburg: http://lmb.informatik.uni-freiburg.de/people/brox/index.en.html

【Action recognition】Learning to Anonymize Faces for Privacy Preserving Action Detection

Zhongzheng Ren, University of California, Davis:

Yong Jae Lee, University of California, Davis:

Michael Ryoo, Indiana University:

【Action recognition】Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds

Haroon Idrees, Carnegie Mellon University:

Muhammad Tayyab, UCF:

Kishan Athrey, UCF:

Mubarak Shah, University of Central Florida: http://crcv.ucf.edu/people/faculty/shah.html

Dong Zhang, University of Central Florida, USA:

【Action recognition】CTAP: Complementary Temporal Action Proposal Generation

Jiyang Gao, USC:

Kan Chen, University of Southern California, USA:

Ram Nevatia, U of Southern California: http://iris.usc.edu/USC-Computer-Vision.html

【Action recognition】Egocentric Activity Prediction via Event Modulated Attention

Yang Shen, Shanghai Jiao Tong University:

Bingbing Ni, Shanghai Jiao Tong University:

Zefan Li, Shanghai Jiao Tong University:

Ning Zhuang, Shanghai Jiao Tong University:

【Action recognition】Good Line Cutting: towards Accurate Pose Tracking of Line-assisted VO/VSLAM

Yipu Zhao, Georgia Institute of Technology:

Patricio Vela, Georgia Institute of Technology:

【Action recognition】Multi-Scale Structure-Aware Network for Human Pose Estimation

Lipeng Ke, University of Chinese Academy of Sciences:

Ming-Ching Chang, Albany University:

Honggang Qi, University of Chinese Academy of Sciences:

Siwei Lyu, University at Albany: http://www.cs.albany.edu/~lsw/

【Action recognition】Neural Graph Matching Networks for Fewshot 3D Action Recognition

Michelle Guo, Stanford University:

Edward Chou, Stanford University:

De-An Huang, Stanford University:

Shuran Song, Princeton:

Serena Yeung, Stanford University:

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

【Action recognition】Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning

Chenyang Si, Institute of Automation, Chinese Academy of Sciences:

Ya Jing, Institute of Automation, Chinese Academy of Sciences:

wei wang, Institute of Automation Chinese Academy of Sciences:

Liang Wang, NLPR, China:

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

【Action recognition】Spatio-Temporal Channel Correlation Networks for Action Classification

Ali Diba, KU Leuven:

Mohsen Fayyaz, University of Bonn:

Vivek Sharma, Karlsruhe Institute of Technology:

Mohammad Arzani, Sensifai:

Rahman Yousefzadeh, sensifai:

Jürgen Gall, University of Bonn:

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

【Action recognition】Online Detection of Action Start in Untrimmed, Streaming Videos

Zheng Shou, Columbia University:

Junting Pan, Columbia University:

Jonathan Chan, Columbia University:

Kazuyuki Miyazawa, Mitsubishi Electric:

Hassan Mansour, Mitsubishi Electric Research Laboratories (MERL):

Anthony Vetro, Mitsubishi Electric Research Lab:

Xavier Giro-i-Nieto, Universitat Politecnica de Catalunya:

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

【Action recognition】Two Stream Pose Transfer Guided by Dense Pose Estimation

Natalia Neverova, Facebook AI Research:

Alp Guler, INRIA:

Iasonas Kokkinos, Facebook, France: http://cvn.ecp.fr/personnel/iasonas/index.html

【Action recognition】Diagnosing Error in Temporal Action Detectors

Humam Alwassel, KAUST:

Fabian Caba, KAUST:

Victor Escorcia, KAUST:

Bernard Ghanem, KAUST: http://www.bernardghanem.com/

【Action recognition】Hierarchical Relational Networks for Group Activity Recognition and Retrieval

Mostafa Ibrahim, Simon Fraser University:

Greg Mori, Simon Fraser University: http://www.cs.sfu.ca/~mori/

【Action recognition】BSN: Boundary Sensitive Network for Temporal Action Proposal Generation

Tianwei Lin, Shanghai Jiao Tong University:

Xu Zhao, Shanghai Jiao Tong University:

Haisheng Su, Shanghai Jiao Tong University:

Chongjing Wang, China Academy of Information and Communications Technology:

Ming Yang, Shanghai Jiao Tong University:

【Action recognition】Deeply Learned Compositional Models for Human Pose Estimation

Wei Tang, Northwestern University:

Pei Yu, Northwestern University:

Ying Wu, Northwestern University:

【Action recognition】W-TALC: Weakly-supervised Temporal Activity Localization and Classification

Sujoy Paul, University of California-Riverside:

Sourya Roy, University of California, Riverside:

Amit Roy-Chowdhury , University of California, Riverside, USA:

【Action recognition】Part-Activated Deep Reinforcement Learning for Action Prediction

Lei Chen, Tianjin University:

Jiwen Lu, Tsinghua University:

Zhanjie Song, Tianjin University:

Jie Zhou, Tsinghua University, China: https://www.tsinghua.edu.cn/publish/auen/1713/2011/20110506105532098625469/20110506105532098625469_.html

【Action recognition】Recurrent Tubelet Proposal and Recognition Networks for Action Detection

Dong Li, University of Science and Technology of China:

Zhaofan Qiu, University of Science and Technology of China:

Qi Dai, Microsoft Research:

Ting Yao, Microsoft Research:

Tao Mei, JD.com:

【Action recognition】IM2Hand3D: Leveraging Multi-task Network for 3D Hand Pose Estimation from a Color Image

Xiaoming Deng, Chinese Academy of Sciences:

Wenyong Zheng, Chinese Academy of Sciences:

Yinda Zhang, Princeton University:

Jian Shi, Chinese Academy of Sciences:

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

Liang Chang, Beijing Normal University:

Yuying Zhu, Chinese Academy of Sciences:

【Action recognition】RESOUND: Towards Action Recognition without Representation Bias

Yingwei Li, UCSD:

Nuno Vasconcelos, UC San Diego: http://www.svcl.ucsd.edu/

Yi Li, University of California San Diego: http://users.cecs.anu.edu.au/~yili/

【Action recognition】Simple Baselines for Human Pose Estimation and Tracking

Bin Xiao, MSR Asia:

Haiping Wu, MSR Asia:

Yichen Wei, MSR Asia:

【Action recognition】Pose Partition Networks for Multi-Person Pose Estimation

Xuecheng Nie, NUS:

Jiashi Feng, NUS: https://sites.google.com/site/jshfeng/

Junliang Xing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences:

Shuicheng Yan, Qihoo/360: http://www.lv-nus.org/index.html

【Action recognition】PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-modalities

Lan Wang, Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications:

Chenqiang Gao, Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications:

Luyu Yang, Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications:

Yue Zhao, Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications:

Wangmeng Zuo, Harbin Institute of Technology, China:

Deyu Meng, Xi’an Jiaotong University:

【Action recognition】Adversarial Geometry-Aware Human Motion Prediction

Liangyan Gui, Carnegie Mellon University:

Yu-Xiong Wang, Carnegie Mellon University:

Xiaodan Liang, Carnegie Mellon University:

José M. F. Moura, Carnegie Mellon University: https://users.ece.cmu.edu/~moura/

【Action recognition】Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition

Junwu Weng, Nanyang Technological University:

Mengyuan Liu, Nanyang Technological University:

Xudong Jiang, Nanyang Technological University:

Junsong Yuan, State University of New York at Buffalo, USA: https://cse.buffalo.edu/~jsyuan/

【Action recognition】Stereo relative pose from line and point feature triplets

Alexander Vakhitov, Skoltech:

Victor Lempitsky, Skoltech:

Yinqiang Zheng, National Institute of Informatics: https://researchmap.jp/yinqiangzheng

【Action recognition】Modality Distillation with Multiple Stream Networks for Action Recognition

Nuno Garcia, IIT:

Pietro Morerio, IIT:

Vittorio Murino, Istituto Italiano di Tecnologia: https://www.iit.it/people/vittorio-murino

【Action recognition】Deep Bilinear Learning for RGB-D Action Recognition

HU Jian-Fang, Sun Yat-sen University:

Jason Wei Shi Zheng, Sun Yat Sen University:

Pan Jiahui, Sun Yat-sen University:

Jian-Huang Lai, Sun Yat-sen University:

Jianguo Zhang, University of Dundee:

【Action recognition】Few-Shot Human Motion Prediction via Meta-Learning

Liangyan Gui, Carnegie Mellon University:

Yu-Xiong Wang, Carnegie Mellon University:

Deva Ramanan, Carnegie Mellon University: http://www.ics.uci.edu/~dramanan/

José M. F. Moura, Carnegie Mellon University: https://users.ece.cmu.edu/~moura/

【Action recognition】Weakly-supervised 3D Hand Pose Estimation from Monocular RGB Images

Yujun Cai, Nanyang Technological University:

Liuhao Ge, NTU:

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

Junsong Yuan, State University of New York at Buffalo, USA: https://cse.buffalo.edu/~jsyuan/

【Action recognition】stag Net: An Attentive Semantic RNN for Group Activity Recognition

Mengshi Qi, Beihang University:

Jie Qin, ETH Zurich:

Annan Li, Beijing University of Aeronautics and Astronautics:

Yunhong Wang, State Key Laboratory of Virtual Reality Technology and System, Beihang University, Beijing 100191, China: http://irip.buaa.edu.cn/Chinese.html

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

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

【Action recognition】Action Anticipation with RBF Kernelized Feature Mapping RNN

Yuge Shi, Australian National University:

Basura Fernando, Australian National University:

RICHARD HARTLEY, Australian National University, Australia:

【Action recognition】Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera

Timo von Marcard, University of Hannover:

Roberto Henschel, Leibniz University of Hannover:

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

Bodo Rosenhahn, Leibniz University Hannover:

Gerard Pons-Moll, MPII, Germany: https://ps.is.tuebingen.mpg.de/person/gpons

【Action recognition】Exploiting temporal information for 3D human pose estimation

Mir Rayat Imtiaz Hossain, University of British Columbia:

Jim Little, University of British Columbia, Canada: https://www.cs.ubc.ca/~little/

【Action recognition】Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization

Humam Alwassel, KAUST:

Fabian Caba, KAUST:

Bernard Ghanem, KAUST: http://www.bernardghanem.com/

【Action recognition】Deep Autoencoder for Combined Human Pose Estimation and Body Model Upscaling

Matthew Trumble, University of Surrey:

Andrew Gilbert, University of Surrey:

John Collomosse, Adobe Research:

Adrian Hilton, University of Surrey: https://www.surrey.ac.uk/people/adrian-hilton

【Action recognition】In the Eye of Beholder: Joint Learning of Gaze and Actions in First Person Vision

Yin Li, CMU:

Miao Liu, Georgia Tech:

James Rehg, Georgia Institute of Technology: http://www.cc.gatech.edu/~rehg/

【Action recognition】Dividing and Aggregating Network for Multi-view Action Recognition

Dongang Wang, The University of Sydney:

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

Wen Li, ETHZ:

Dong Xu, University of Sydney: http://www.ntu.edu.sg/home/dongxu/

【Action recognition】Motion Feature Network: Fixed Motion Filter for Action Recognition

Myunggi Lee, Seoul National University:

Seung Eui Lee, Seoul National University:

Sung Joon Son, Seoul National University:

Gyutae Park, Seoul National University:

Nojun Kwak, Seoul National University: http://mipal.snu.ac.kr/index.php/Nojun_Kwak

【Action recognition】What do I Annotate Next? An Empirical Study of Active Learning for Action Localization

Fabian Caba, KAUST:

Joon-Young Lee, Adobe Research:

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

Bernard Ghanem, KAUST: http://www.bernardghanem.com/

【Action recognition】Accelerating Dynamic Programs via Nested Benders Decomposition with Application to Multi-Person Pose Estimation

Shaofei Wang, Baidu Inc.:

Alexander Ihler, UC Irvine:

Konrad Kording, Northwestern:

Julian Yarkony, Experian Data Lab:

【Action recognition】Point-to-Point Regression Point Net for 3D Hand Pose Estimation

Liuhao Ge, NTU:

Zhou Ren, Snap Research, USA,:

Junsong Yuan, State University of New York at Buffalo, USA: https://cse.buffalo.edu/~jsyuan/

【Action recognition】Graph Distillation for Action Detection with Privileged Information in RGB-D Videos

Zelun Luo, Stanford University:

Lu Jiang, Google:

Jun-Ting Hsieh, Stanford University:

Juan Carlos Niebles, Stanford University: http://www.niebles.net/

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

【Action recognition】R2P2: A Reparamete Rized Push forward Policy for Diverse, Precise Generative Path Forecasting

Nicholas Rhinehart, CMU:

Kris Kitani, CMU:

Paul Vernaza, NEC Labs America:

【Action recognition】Compositional Learning of Human Object Interactions

Keizo Kato, CMU:

Yin Li, CMU:

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

【Action recognition】A Unified Framework for Multi-View Multi-Class Object Pose Estimation

Chi Li, Johns Hopkins University:

Jin Bai, Johns Hopkins University:

Gregory D. Hager, The Johns Hopkins University: http://www.nec-labs.com/paul-vernaza

【Action recognition】Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification

Yang Du, NLPR:

Chunfeng Yuan, NLPR:

Weiming Hu, Institute of Automation,Chinese Academy of Sciences: http://people.ucas.ac.cn/~huweiming

【Action recognition】3D Ego-Pose Estimation via Imitation Learning

Ye Yuan, Carnegie Mellon University:

Kris Kitani, CMU:

【Action recognition】Auto Loc: Weakly-supervised Temporal Action Localization in Untrimmed Videos

Zheng Shou, Columbia University:

Hang Gao, Columbia University:

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

Kazuyuki Miyazawa, Mitsubishi Electric:

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

【Video analysis】ECO: Efficient Convolutional Network for Online Video Understanding

Mohammadreza Zolfaghari, University of Freiburg:

kamaljeet singh, University of Freiburg:

Thomas Brox, University of Freiburg: http://lmb.informatik.uni-freiburg.de/people/brox/index.en.html

【Video analysis】Audio-Visual Event Localization in Unconstrained Videos

Yapeng Tian, University of Rochester:

Jing Shi, University of Rochester:

Bochen Li, University of Rochester:

Zhiyao Duan, Unversity of Rochester:

Chenliang Xu, University of Rochester: http://www.cs.rochester.edu/~cxu22/

【Video analysis】Temporal Relational Reasoning in Videos

Bolei Zhou, MIT:

Alex Andonian, Massachusetts Institute of Technology:

Aude Oliva, MIT:

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

【Video analysis】Fast Multi-fiber Network for Video Recognition

Yunpeng Chen, National University of Singapore:

Yannis Kalantidis, Facebook Research, USA:

Jianshu Li, NUS:

Yan Shuicheng, National University of Singapore:

Jiashi Feng, NUS: https://sites.google.com/site/jshfeng/

【Video analysis】Deep Phys: Video-Based Physiological Measurement Using Convolutional Attention Networks

Weixuan Chen, MIT Media Lab:

Daniel Mc Duff, Microsoft Research:

【Video analysis】Deep Video Quality Assessor: From Spatio-temporal Visual Sensitivity to A Convolutional Neural Aggregation Network

Woojae Kim, Yonsei University:

Jongyoo Kim, Yonsei University:

Sewoong Ahn, Yonsei University:

Jinwoo Kim, Yonsei University:

Sanghoon Lee, Yonsei University, Korea: http://insight.yonsei.ac.kr/gnuboard/

【Video analysis】Learning to Separate Object Sounds by Watching Unlabeled Video

Ruohan Gao, University of Texas at Austin:

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

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

【Video analysis】Multiple-gaze geometry: Inferring novel 3D locations from gazes observed in monocular video

Ernesto Brau, Ci BO Technologies:

Jinyan Guan, UC San Diego:

Tanya Jeffries, U. Arizona:

Kobus Barnard, University of Arizona:

【Video analysis】Deep Discriminative Model for Video Classification

Mohammad Tavakolian, University of Oulu:

Abdenour Hadid, Finland:

【Video analysis】Improving Sequential Determinantal Point Processes for Supervised Video Summarization

Aidean Sharghi, University of Central Florida:

Boqing Gong, Tencent AI Lab:

Ali Borji, University of Central Florida: http://ilab.usc.edu/borji/

Chengtao Li, MIT:

Tianbao Yang, University of Iowa: http://homepage.divms.uiowa.edu/~tyng/

【Video analysis】Learning Discriminative Video Representations Using Adversarial Perturbations

Jue Wang, ANU: http://www.juew.org/

Anoop Cherian, MERL:

【Video analysis】Massively Parallel Video Networks

Viorica Patraucean, Deep Mind:

Joao Carreira, Deep Mind:

Laurent Mazare, Deep Mind:

Simon Osindero, Deep Mind:

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

【Video analysis】Predicting Gaze in Egocentric Video by Learning Task-dependent Attention Transition

Yifei Huang, The University of Tokyo:

【Video analysis】Videos as Space-Time Region Graphs

Xiaolong Wang, CMU:

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

【Video analysis】Retrospective Encoders for Video Summarization

Ke Zhang, USC:

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

Fei Sha, USC: http://www-bcf.usc.edu/~feisha/

【Video analysis】Video Compression through Image Interpolation

Chao-Yuan Wu, UT Austin:

Nayan Singhal, UT Austin:

Philipp Kraehenbuehl, UT Austin: https://www.philkr.net/

【Video analysis】SDC-Net: Video prediction using spatially-displaced convolution

Fitsum Reda, NVIDIA:

Guilin Liu, NVIDIA:

Kevin Shih, NVIDIA:

Robert Kirby, Nvidia:

Jon Barker, Nvidia:

David Tarjan, Nvidia:

Andrew Tao, NVIDIA:

Bryan Catanzaro, NVIDIA:

【Video analysis】Robust Anchor Embedding for Unsupervised Video Re-Identification in the Wild

Mang YE, Hong Kong Baptist University:

Xiangyuan Lan, Department of Computer Science, Hong Kong Baptist University:

Pong Chi Yuen, Department of Computer Science, Hong Kong Baptist University:

【Video analysis】CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving

Xiaodan Liang, Carnegie Mellon University:

Tairui Wang, Petuum Inc:

Luona Yang, Carnegie Mellon University:

Eric Xing, Petuum Inc.: http://www.cs.cmu.edu/~epxing/

【Video analysis】Flow-Grounded Spatial-Temporal Video Prediction from Still Images

Yijun Li, University of California, Merced:

CHEN FANG, Adobe Research, San Jose, CA:

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

Zhaowen Wang, Adobe Research:

Xin Lu, Adobe:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

【Video analysis】Pose Guided Human Video Generation

Ceyuan Yang, Sense Time Group Limited:

Zhe Wang, Sensetime Group Limited:

Xinge Zhu, Sensetime Group Limited:

Chen Huang, Carnegie Mellon University:

Jianping Shi, Sensetime Group Limited:

Dahua Lin, The Chinese University of Hong Kong: http://dahua.me/

【Video analysis】Integrating Egocentric Videos in Top-view Surveillance Videos: Joint Identification and Temporal Alignment

Shervin Ardeshir, University of Central Florida:

Ali Borji, University of Central Florida: http://ilab.usc.edu/borji/

【Video analysis】Video Summarization Using Fully Convolutional Sequence Networks

Mrigank Rochan, University of Manitoba:

Linwei Ye, University of Manitoba:

Yang Wang, University of Manitoba:

【Video analysis】Compound Memory Networks for Few-shot Video Classification

Linchao Zhu, University of Technology, Sydney:

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

【Video analysis】How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization

Yandong Li, University of Central Florida:

Boqing Gong, Tencent AI Lab:

Tianbao Yang, University of Iowa: http://homepage.divms.uiowa.edu/~tyng/

Liqiang Wang, University of Central Florida: http://www.cs.ucf.edu/~lwang/

【Video analysis】Move Forward and Tell: A Progressive Generator of Video Descriptions

Yilei Xiong, The Chinese University of Hong Kong:

Bo Dai, the Chinese University of Hong Kong:

Dahua Lin, The Chinese University of Hong Kong: http://dahua.me/

【Video analysis】DYAN: A Dynamical Atoms-Based Network for Video Prediction

Wenqian Liu, Northeastern University:

Abhishek Sharma, Northeastern University:

Octavia Camps, Northeastern University:

Mario Sznaier, Northeastern University:

【Video analysis】Summarizing First-Person Videos from Third Persons’ Points of View

HSUAN-I HO, National Taiwan University:

Wei-Chen Chiu, National Chiao Tung University:

Yu-Chiang Frank Wang, National Taiwan University: http://www.citi.sinica.edu.tw/pages/ycwang/index_en.html

【Video analysis】Video Re-localization via Cross Gated Bilinear Matching

Yang Feng, University of Rochester:

Lin Ma, Tencent AI Lab:

Wei Liu, Tencent AI Lab: http://www.ee.columbia.edu/~wliu/

Tong Zhang, Tecent AI Lab: http://tongzhang-ml.org/

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

【Video analysis】Deep Kalman Filtering Network for Video Compression Artifact Reduction

Guo Lu, Shanghai Jiao Tong University:

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

Dong Xu, University of Sydney: http://www.ntu.edu.sg/home/dongxu/

Xiaoyun Zhang, Shanghai Jiao Tong University:

Zhiyong Gao, Shanghai Jiao Tong University:

Ming Ting Sun, -:

【Video analysis】Pivot Correlational Neural Network for Multimodal Video Categorization

Sunghun Kang, KAIST:

Junyeong Kim, KAIST:

Hyunsoo Choi, SAMSUNG ELECTRONICS CO.,LTD:

Sungjin Kim, SAMSUNG ELECTRONICS CO.,LTD:

Chang D. Yoo, KAIST: http://slsp.kaist.ac.kr/xe/index.php?mid=home

【Video analysis】Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset

Heng Wang, Facebook Inc:

Lorenzo Torresani, Dartmouth College:

Matt Feiszli, Facebook Research:

Manohar Paluri, Facebook:

Du Tran, Facebook:

Jamie Ray, Facebook Research:

Yufei Wang, Facebook:

【Video analysis】Cross-Modal and Hierarchical Modeling of Video and Text

Bowen Zhang, University of Southern California:

Hexiang Hu, University of Southern California:

Fei Sha, USC: http://www-bcf.usc.edu/~feisha/

【Video analysis】Weakly-supervised Video Summarization using Variational Encoder-Decoder and Web Prior

Sijia Cai, The Hong Kong Polytechnic University:

Wangmeng Zuo, Harbin Institute of Technology:

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

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

【Video analysis】Folded Recurrent Neural Networks for Future Video Prediction

Marc Oliu, Universitat Oberta de Catalunya:

Javier Selva, Universitat de Barcelona:

Sergio Escalera, Computer Vision Center (UAB) & University of Barcelona,: http://sergioescalera.com/

【Video analysis】Object Level Visual Reasoning in Videos

Fabien Baradel, LIRIS:

Natalia Neverova, Facebook AI Research:

Christian Wolf, INSA Lyon, France:

Julien Mille, INSA Centre Val de Loire:

Greg Mori, Simon Fraser University: http://www.cs.sfu.ca/~mori/

【Video analysis】Context VP: Fully Context-Aware Video Prediction

Wonmin Byeon, NVIDIA:

Qin Wang, ETH Zurich:

Rupesh Kumar Srivastava, NNAISENSE:

Petros Koumoutsakos, ETH Zurich: http://www.cse-lab.ethz.ch/

【Video analysis】Multimodal Dual Attention Memory for Video Story Question Answering

Kyungmin Kim, Seoul National University:

Seong-Ho Choi, Seoul National University:

Jin-Hwa Kim, Seoul National University:

Byoung-Tak Zhang, Seoul National University: https://bi.snu.ac.kr/~btzhang/

【Video analysis】Real-Time Blind Video Temporal Consistency

Wei-Sheng Lai, University of California, Merced:

Jia-Bin Huang, Virginia Tech:

Oliver Wang, Adobe Systems Inc:

Eli Shechtman, Adobe Research, US: http://www.adobe.com/technology/people/seattle/eli-shechtman.html

Ersin Yumer, Argo AI:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

【Video analysis】Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification

Saining Xie, UCSD:

Chen Sun, Google:

Jonathan Huang, Google: https://ai.google/research/people/JonathanHuang

Zhuowen Tu, UC San Diego: http://pages.ucsd.edu/~ztu/

Kevin Murphy, Google:

【Video analysis】Teaching Machines to Understand Baseball Games: Large Scale Baseball Video Database for Multiple Video Understanding Tasks

Minho Shim, Yonsei University:

KYUNGMIN KIM, Yonsei University:

Young Hwi Kim, Yonsei University:

Seon Joo Kim, Yonsei Univ.:

【Video analysis】Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning

Uta Büchler, Heidelberg University:

Biagio Brattoli, Heidelberg University:

Bjorn Ommer, Heidelberg University:

【Video analysis】Deep Volumetric Video From Very Sparse Multi-View Performance Capture

Zeng Huang, University of Southern California:

Tianye Li, University of Southern California:

Weikai Chen, USC Institute for Creative Technology:

Yajie Zhao, USC Institute for Creative Technology:

Jun Xing, Institute for Creative Technologies, USC:

Chloe Le Gendre, USC Institute for Creative Technology:

Linjie Luo, Snap Inc:

Chongyang Ma, Snap Inc.:

Hao Li, Pinscreen/University of Southern California/USC ICT: https://www.hao-li.com/Hao_Li/Hao_Li_-_about_me.html

【Human detection】Adversarial Open-World Person Re-Identification

Xiang Li, Sun Yat-sen University:

Ancong Wu, Sun Yat-sen University:

Jason Wei Shi Zheng, Sun Yat Sen University:

【Human detection】Person Search by Multi-Scale Matching

Xu Lan, Queen Mary University of London:

Xiatian Zhu, Queen Mary University, London, UK:

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

【Human detection】Bi-box Regression for Pedestrian Detection and Occlusion Estimation

CHUNLUAN ZHOU, Nanyang Technological University:

Junsong Yuan, State University of New York at Buffalo, USA: https://cse.buffalo.edu/~jsyuan/

【Human detection】Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

Shifeng Zhang, CBSR, NLPR, CASIA:

Longyin Wen, GE Global Research:

Xiao Bian, GE Global Research:

Zhen Lei, NLPR, CASIA, China:

Stan Li, National Lab. of Pattern Recognition, China: http://www.cbsr.ia.ac.cn/users/szli/

【Human detection】Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-identification

Cheng Wang, Huazhong Univ. of Science and Technology:

Qian Zhang, Horizon Robotics:

Chang Huang, Horizon Robotics, Inc.:

Wenyu Liu, Huazhong University of Science and Technology:

Xinggang Wang, Huazhong Univ. of Science and Technology: http://www.xinggangw.info/

【Human detection】Unsupervised Person Re-identification by Deep Learning Tracklet Association

Minxian Li, Nanjing University and Science and Technology:

Xiatian Zhu, Queen Mary University, London, UK:

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

【Human detection】Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based Person Re-identification

Nikolaos Karianakis, Microsoft:

Zicheng Liu, Microsoft:

Yinpeng Chen, Microsoft:

Stefano Soatto, UCLA: http://vision.ucla.edu/projects.html

【Human detection】Scale Aggregation Network for Accurate and Efficient Crowd Counting

Xinkun Cao, Beijing University of Posts and Telecommunications:

Zhipeng Wang, School of Communication and Information Engineering, Beijing University of Posts and Telecommunications:

Yanyun Zhao, Beijing Univiersity of Posts and Telecommunications:

Fei Su, Beijing University of Posts and Telecommunications:

【Human detection】Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation

Tao Song, Hikvision Research Institute:

Leiyu Sun, Hikvision Research Institute:

Di Xie, Hikvision Research Institute:

Haiming Sun, Hikvision Research Institute:

Shiliang Pu, Hikvision Research Institute:

【Human detection】Person Search via A Mask-guided Two-stream CNN Model

Di Chen, Nanjing University of Science and Techonology:

Shanshan Zhang, Max Planck Institute for Informatics:

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

Jian Yang, Nanjing University of Science and Technology:

Ying Tai, Tencent:

【Human detection】Pose-Normalized Image Generation for Person Re-identification

Xuelin Qian, Fudan University:

Yanwei Fu, Fudan Univ.:

Tao Xiang, Queen Mary, University of London, UK:

Wenxuan Wang, Fudan University:

Jie Qiu, Nara Institute of Science and Technology:

Yang Wu, Nara Institute of Science and Technology:

Yu-Gang Jiang, Fudan University:

Xiangyang Xue, Fudan University:

【Human detection】RCAA: Relational Context-Aware Agents for Person Search

Xiaojun Chang, Carnegie Mellon University:

Po-Yao Huang, Carnegie Mellon University:

Xiaodan Liang, Carnegie Mellon University:

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

Alexander Hauptmann, Carnegie Mellon University:

【Human detection】Graininess-Aware Deep Feature Learning for Pedestrian Detection

Chunze Lin, Tsinghua University:

Jiwen Lu, Tsinghua University:

Jie Zhou, Tsinghua University, China: https://www.tsinghua.edu.cn/publish/auen/1713/2011/20110506105532098625469/20110506105532098625469_.html

【Human detection】Iterative Crowd Counting

Viresh Ranjan, Stony Brook University:

Hieu Le, Stony Brook University:

Minh Hoai Nguyen, Stony Brook University: http://www3.cs.stonybrook.edu/~minhhoai/

【Human detection】Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association

Dapeng Chen, The Chinese University of Hong Kong:

Hongsheng Li, Chinese University of Hong Kong:

Xihui Liu, The Chinese University of Hong Kong:

Jing Shao, The Chinese University of Hong Kong:

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

【Human detection】Learnable PINs: Cross-Modal Embeddings for Person Identity

Samuel Albanie, University of Oxford:

Arsha Nagrani, Oxford University:

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

【Human detection】Domain Adaptation through Synthesis for Unsupervised Person Re-identification

Slawomir Bak, Argo AI:

Jean-Francois Lalonde, Université Laval: http://vision.gel.ulaval.ca/~jflalonde/

Pete Carr, Argo AI:

【Human detection】Person Search in Videos with One Portrait Through Visual and Temporal Links

Qingqiu Huang, CUHK:

Wentao Liu, Sensetime:

Dahua Lin, The Chinese University of Hong Kong: http://dahua.me/

【Human detection】Part-Aligned Bilinear Representations for Person Re-Identification

Yumin Suh, Seoul National University:

Jingdong Wang, Microsoft Research:

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

【Human detection】Learning Efficient Single-stage Pedestrian Detection by Asymptotic Localization Fitting

Wei Liu, National University of Defense Technology: http://www.ee.columbia.edu/~wliu/

Shengcai Liao, NLPR, Chinese Academy of Sciences, China:

Weidong Hu, National University of Defence Technology:

Xuezhi Liang, Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences:

Xiao Chen, National University of Defense Technology:

【Human detection】Maximum Margin Metric Learning Over Discriminative Nullspace for Person Re-identification

T M Feroz Ali, Indian Institute of Technology Bombay, Mumbai:

Subhasis Chaudhuri, Indian Institute of Technology Bombay:

【Human detection】Hard-Aware Point-to-Set Deep Metric for Person Re-identification

Rui Yu, Huazhong University of Science and Technology:

Zhiyong Dou, Huazhong University of Science and Technology:

Song Bai, HUST:

ZHAO-XIANG ZHANG, Chinese Academy of Sciences, China:

Yongchao Xu, HUST:

Xiang Bai, Huazhong University of Science and Technology: http://cloud.eic.hust.edu.cn:8071/~xbai/

【Human detection】Person Re-identification with Deep Similarity-Guided Graph Neural Network

Yantao Shen, The Chinese University of Hong Kong:

Hongsheng Li, Chinese University of Hong Kong:

Shuai Yi, The Chinese University of Hong Kong:

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

【Human parsing】Joint Person Segmentation and Identification in Synchronized First- and Third-person Videos

Mingze Xu, Indiana University:

Chenyou Fan, JD.com:

Yuchen Wang, Indiana University:

Michael Ryoo, Indiana University:

David Crandall, Indiana University: http://www.cs.indiana.edu/~djcran/

【Human parsing】Instance-level Human Parsing via Part Grouping Network

Ke Gong, SYSU:

Xiaodan Liang, Carnegie Mellon University:

Yicheng Li, Sun Yat-sen University:

Yimin Chen, sensetime:

Liang Lin, Sun Yat-sen University: http://ss.sysu.edu.cn/~ll/index.html

【Human parsing】Integral Human Pose Regression

Xiao Sun, Microsoft Research Asia:

Bin Xiao, MSR Asia:

Fangyin Wei, Peking University:

Shuang Liang, Tongji University:

Yichen Wei, MSR Asia:

【Human parsing】Generating Multimodal Human Dynamics with a Transformation based Representation

Xinchen Yan, University of Michigan:

Akash Rastogi, UM:

Ruben Villegas, University of Michigan:

Eli Shechtman, Adobe Research, US: http://www.adobe.com/technology/people/seattle/eli-shechtman.html

Sunkavalli Kalyan, Adobe Research:

Sunil Hadap, Adobe:

Ersin Yumer, Argo AI:

Honglak Lee, UM: http://web.eecs.umich.edu/~honglak/

【Human parsing】Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation

Xuecheng Nie, NUS:

Jiashi Feng, NUS: https://sites.google.com/site/jshfeng/

Shuicheng Yan, Qihoo/360: http://www.lv-nus.org/index.html

【Human parsing】Propagating LSTM: 3D Pose Estimation based on Joint Interdependency

Kyoungoh Lee, Yonsei University:

Inwoong Lee, Yonsei University:

Sanghoon Lee, Yonsei University, Korea: http://insight.yonsei.ac.kr/gnuboard/

【Human parsing】Leveraging Motion Priors in Videos for Improving Human Segmentation

Yu-Ting Chen, NTHU:

Wen-Yen Chang, NTHU:

Hai-Lun Lu, NTHU:

Tingfan Wu, Umbo Computer Vision:

Min Sun, NTHU: http://aliensunmin.github.io/

【Human parsing】Body Net: Volumetric Inference of 3D Human Body Shapes

Gul Varol, INRIA:

Duygu Ceylan, Adobe Research:

Bryan Russell, Adobe Research:

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

Ersin Yumer, Argo AI:

Ivan Laptev, INRIA Paris: http://www.di.ens.fr/~laptev/index.html

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

【Human parsing】Learning Human-Object Interactions by Graph Parsing Neural Networks

Siyuan Qi, UCLA:

Wenguan Wang, Beijing Institute of Technology:

Baoxiong Jia, UCLA:

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

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

【Human parsing】Macro-Micro Adversarial Network for Human Parsing

Yawei Luo, University of Technology Sydney:

Zhedong Zheng, University of Technology Sydney:

Liang Zheng, University of Technology Sydney:

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

【Human parsing】Learning 3D Human Pose from Structure and Motion

Rishabh Dabral, IIT Bombay:

Anurag Mundhada, IIT Bombay:

Abhishek Sharma, Gobasco AI Labs:

【Human parsing】Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation

Helge Rhodin, EPFL:

Mathieu Salzmann, EPFL:

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

【Human parsing】Hand Pose Estimation via Latent 2.5D Heatmap Regression

Umar Iqbal, University of Bonn:

Pavlo Molchanov, NVIDIA:

Thomas Breuel, NVIDIA:

Jürgen Gall, University of Bonn:

Kautz Jan, NVIDIA:

【Human parsing】Multi Pose Net: Fast Multi-Person Pose Estimation using Pose Residual Network

Muhammed Kocabas, Middle East Technical University:

Salih Karagoz, Middle East Technical University:

Emre Akbas, Middle East Technical University:

【Human parsing】Occlusion-aware Hand Pose Estimation Using Hierarchical Mixture Density Network

Qi Ye, Imperial College London:

Tae-Kyun Kim, Imperial College London: https://labicvl.github.io/

【Human parsing】Pairwise Body-Part Attention for Recognizing Human-Object Interactions

Haoshu Fang, SJTU:

Jinkun Cao, Shanghai Jiao Tong University:

Yu-Wing Tai, Tencent You Tu:

Cewu Lu, Shanghai Jiao Tong Univercity: http://mvig.sjtu.edu.cn/

【Human parsing】Human Motion Analysis with Deep Metric Learning

HUSEYIN COSKUN, Technical University of Munich:

David Joseph Tan, CAMP, TU Munich:

Sailesh Conjeti, Technical University of Munich:

Nassir Navab, TU Munich, Germany: http://campar.in.tum.de/Main/NassirNavab

Federico Tombari, Technical University of Munich, Germany: http://vision.deis.unibo.it/fede/

【Human parsing】Pose Proposal Networks

Taiki Sekii, Konica Minolta, inc.:

【Human parsing】HBE: Hand Branch Ensemble network for real time 3D hand pose estimation

Yidan Zhou, Dalian University of Technology:

Jian Lu, Laboratory of Advanced Design and Intelligent Computing, Dalian University:

Kuo Du, Dalian University of Technology:

Xiangbo Lin, Dalian University of Technology:

Yi Sun, Dalian University of Technology:

Xiaohong Ma, Dalian University of Technology:

【Human parsing】Hand Map: Robust Hand Pose Estimation via Intermediate Dense Guidance Map Supervision

Xiaokun Wu, University of Bath:

Daniel Finnegan, University of Bath:

Eamonn O’Neill, University of Bath:

Yongliang Yang, University of Bath:

【Human parsing】Learning Type-Aware Embeddings for Fashion Compatibility

Mariya Vasileva, University of Illinois at Urbana-Champaign:

Bryan Plummer, Boston University:

Krishna Dusad, University of Illinois at Urbana-Champaign:

Shreya Rajpal, University of Illinois at Urbana-Champaign:

David Forsyth, Univeristy of Illinois at Urbana-Champaign: http://luthuli.cs.uiuc.edu/~daf/

Ranjitha Kumar, UIUC: CS:

【Face recognition】Pairwise Relational Networks for Face Recognition

Bong-Nam Kang, POSTECH:

【Face recognition】The Devil of Face Recognition is in the Noise

Liren Chen, Sensetime Group Limited:

Fei Wang, Sense Time:

Cheng Li, Sense Time Research:

Shiyao Huang, Sense Time Co Ltd:

Yanjie Chen, sensetime:

Chen Qian, Sense Time:

Chen Change Loy, Chinese University of Hong Kong: http://www.eecs.qmul.ac.uk/~ccloy/

【Face recognition】Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition

Xiaohang Zhan, The Chinese University of Hong Kong:

Ziwei Liu, The Chinese University of Hong Kong:

Junjie Yan, Sensetime Group Limited: http://www.cbsr.ia.ac.cn/users/jjyan/main.htm

Dahua Lin, The Chinese University of Hong Kong: http://dahua.me/

Chen Change Loy, Chinese University of Hong Kong: http://www.eecs.qmul.ac.uk/~ccloy/

【Face recognition】Face Recognition with Contrastive Convolution

Chunrui Han, ICT, Chinese Academy of Sciences, China:

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

Meina Kan, ICT, CAS:

Shuzhe Wu, Chinese Academy of Sciences:

xilin chen, ICT, Chinese Academy of Sciences, China:

【Face recognition】Facial Expression Recognition with Inconsistently Annotated Datasets

Jiabei Zeng, Institute of Computing Technology, Chinese Academy on Sciences:

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

Chen Xilin, Institute of Computing Technology, Chinese Academy of Sciences:

【Face recognition】Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition

yitong wang, Tencent AI Lab:

dihong gong, Tencent AI Lab:

zheng zhou, Tencent AI Lab:

xing ji, Tencent AI Lab:

Hao Wang, Tencent AI Lab:

Zhifeng Li, Tencent AI Lab:

Wei Liu, Tencent AI Lab: http://www.ee.columbia.edu/~wliu/

Tong Zhang, Tecent AI Lab: http://tongzhang-ml.org/

【Face recognition】Visual Psychophysics for Making Face Recognition Algorithms More Explainable

Brandon Richard Webster, University of Notre Dame:

So Yon Kwon, Perceptive Automata:

Samuel Anthony, Perceptive Automata:

Christopher Clarizio, University of Notre Dame:

Walter Scheirer, University of Notre Dame:

【Face detection】From Face Recognition to Models of Identity: A Bayesian Approach to Learning about Unknown Identities from Unsupervised Data

Daniel Castro, Imperial College London:

Sebastian Nowozin, Microsoft Research Cambridge: http://www.nowozin.net/sebastian/

【Face detection】Pyramid Box: A Context-assisted Single Shot Face Detector

Xu Tang, Baidu:

Daniel Du, Baidu:

Zeqiang He, Baidu:

jingtuo liu, baidu:

【Face detection】Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets

Xiaofeng Liu, Carnegie Mellon University:

  1. V. K. Vijaya Kumar, CMU, USA:

Chao Yang, University of Southern California:

Qingming Tang, TTIC:

Jane You, The Hong Kong Polytechnic University:

【Face detection】Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States

Guosheng Hu, Any Vision:

Li Liu, the inception institute of artificial intelligence:

Yang Yuan, Any Vision:

Zehao Yu, Xiamen University:

Yang Hua, Queen’s University Belfast:

Zhihong Zhang, Xiamen University:

Fumin Shen, UESTC:

Ling Shao, Inception Institute of Artificial Intelligence: http://lshao.staff.shef.ac.uk/

Timothy Hospedales, Edinburgh University: http://homepages.inf.ed.ac.uk/thospeda/

Neil Robertson, Queen’s University Belfast:

Yongxin Yang, University of Edinburgh:

【Face detection】Deep Structure Inference Network for Facial Action Unit Recognition

Ciprian Corneanu, Universitat de Barcelona:

Meysam Madadi, CVC:

Sergio Escalera, Computer Vision Center (UAB) & University of Barcelona,: http://sergioescalera.com/

【Face detection】Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks

Siqi Yang, UQ ITEE:

Arnold Wiliem, University of Queensland:

Shaokang Chen, University of Queensland:

Brian Lovell, University of Queensland: http://researchers.uq.edu.au/researcher/327

【Face parsing】Generative Adversarial Network with Spatial Attention for Face Attribute Editing

Gang Zhang, Institute of Computing Technology, CAS:

Meina Kan, Institute of Computing Technology, Chinese Academy of Sciences:

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

Xilin Chen, China:

【Face parsing】Lip Movements Generation at a Glance

Lele Chen, University of Rochester:

Zhiheng Li, Wu Han University:

Ross Maddox, University of Rochester:

Zhiyao Duan, Unversity of Rochester:

Chenliang Xu, University of Rochester: http://www.cs.rochester.edu/~cxu22/

【Face parsing】ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes

Taihong Xiao, Peking University:

Jiapeng Hong, Peking University:

Jinwen Ma, Peking University:

【Face parsing】Face Super-resolution Guided by Facial Component Heatmaps

Xin Yu, Australian National University:

Basura Fernando, Australian National University:

Bernard Ghanem, KAUST: http://www.bernardghanem.com/

Fatih Porikli, ANU: http://www.porikli.com/

RICHARD HARTLEY, Australian National University, Australia:

【Face parsing】GANimation: Anatomically-aware Facial Animation from a Single Image

Albert Pumarola, Institut de Robotica i Informatica Industrial:

Antonio Agudo, Institut de Robotica i Informatica Industrial, CSIC-UPC:

Aleix Martinez, The Ohio State University:

Alberto Sanfeliu, Industrial Robotics Institute:

Francesc Moreno, IRI:

【Face parsing】Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

Baris Gecer, Imperial College London:

Binod Bhattarai, Imperial College London:

Josef Kittler, University of Surrey, UK:

Tae-Kyun Kim, Imperial College London: https://labicvl.github.io/

【Face parsing】Facial Dynamics Interpreter Network: What are the Important Relations between Local Dynamics for Facial Trait Estimation?

Seong Tae Kim, KAIST:

Yong Man Ro, KAIST:

【Face parsing】RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments

Tobias Fischer, Imperial College London:

Hyung Jin Chang, University of Birmingham:

Yiannis Demiris, Imperial College London:

【Face parsing】Attribute-Guided Face Generation Using Conditional Cycle GAN

Yongyi Lu, HKUST:

Yu-Wing Tai, Tencent You Tu:

Chi-Keung Tang, Hong Kong University of Science and Technology:

【Face parsing】A Deeply-initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment

Roberto Valle, Universidad Politécnica de Madrid:

José Buenaposada, Universidad Rey Juan Carlos:

Antonio Valdés, Universidad Complutense de Madrid:

Luis Baumela, Universidad Politecnica de Madrid:

【Face parsing】X2Face: A network for controlling face generation by using images, audio, and pose codes

Olivia Wiles, University of Oxford:

A Koepke, University of Oxford:

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

【Face parsing】Learning Warped Guidance for Blind Face Restoration

Xiaoming Li, Harbin Institute of Technology:

Ming Liu, Harbin Institute of Technology:

Yuting Ye, Harbin Institute of Technology:

Wangmeng Zuo, Harbin Institute of Technology, China:

Liang Lin, Sun Yat-sen University: http://ss.sysu.edu.cn/~ll/index.html

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

【Face parsing】Face De-spoofing

Yaojie Liu, Michigan State University:

Amin Jourabloo, Michigan State University:

Xiaoming Liu, Michigan State University:

【Face parsing】Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment

Zhiwen Shao, Shanghai Jiao Tong University:

Zhilei Liu, Tianjin University:

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

Lizhuang Ma, Shanghai Jiao Tong University:

【Face parsing】Dense Semantic and Topological Correspondence of 3D Faces without Landmarks

Zhenfeng Fan, Chinese Academy of Sciences:

hu xiyuan, The Chinese academy of science:

chen chen, The Chinese academy of science:

peng silong, The Chinese academy of science:

【Face parsing】Grid Face: Face Rectification via Learning Local Homography Transformations

Erjin Zhou, Megvii Research:

【Face parsing】Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection

Siqi Liu, Department of Computer Science, Hong Kong Baptist University:

Xiangyuan Lan, Department of Computer Science, Hong Kong Baptist University:

Pong Chi Yuen, Department of Computer Science, Hong Kong Baptist University:

【Object recognition】Deep KSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition

Melih Engin, university of wollongong:

Lei Wang, University of Wollongong, Australia:

Luping Zhou, University of Wollongong, Australia:

Xinwang Liu, National University of Defense Technology:

【Object recognition】Attributes as Operators

Tushar Nagarajan, UT Austin:

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

【Object recognition】Objects that Sound

Relja Arandjelovi?, Deep Mind:

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

【Object recognition】Deep Generative Models for Weakly-Supervised Multi-Label Classification

Hong-Min Chu, National Taiwan University:

Chih-Kuan Yeh, Carnegie Mellon University:

Yu-Chiang Frank Wang, National Taiwan University: http://www.citi.sinica.edu.tw/pages/ycwang/index_en.html

【Object recognition】Attention-GAN for Object Transfiguration in Wild Images

Xinyuan Chen, Shanghai Jiao Tong University:

Chang Xu, University of Sydney:

Xiaokang Yang, Shanghai Jiao Tong University of China:

Dacheng Tao, University of Sydney:

【Object recognition】Grassmann Pooling for Fine-Grained Visual Classification

Xing Wei, Xi’an Jiaotong University:

Yihong Gong, Xi’an Jiaotong University:

Yue Zhang, Xi’an Jiaotong University:

Nanning Zheng, Xi’an Jiaotong University:

Jiawei Zhang, City University of Hong Kong:

【Object recognition】Towards Human-Level License Plate Recognition

Jiafan Zhuang, University of Science and Technology of China:

Zilei Wang, University of Science and Technology of China:

【Object recognition】Synthetically Supervised Feature Learning for Scene Text Recognition

Yang Liu, University of Cambridge:

Zhaowen Wang, Adobe Research:

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

Ian Wassell, University of Cambridge:

【Object detection】Where are the blobs: Counting by Localization with Point Supervision     Issam

Hadj Laradji, University of British Columbia (UBC):

Negar Rostamzadeh, Element AI:

Pedro Pinheiro, EPFL:

David Vazquez, Element AI:

Mark Schmidt, University of British Columbia:

【Object detection】C-WSL: Count-guided Weakly Supervised Localization

Mingfei Gao, University of Maryland:

Ang Li, Google Deep Mind:

Ruichi Yu, University of Maryland, College Park:

Vlad Morariu, Adobe Research:

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

【Object detection】Text Snake: A Flexible Representation for Detecting Text of Arbitrary Shapes

Shangbang Long, Peking University:

Jiaqiang Ruan, Peking University:

Wenjie Zhang, Peking University:

Xin He, Megvii:

Wenhao Wu, Megvii:

Cong Yao, Megvii:

【Object detection】Quantized Densely Connected U-Nets for Efficient Landmark Localization

Zhiqiang Tang, Rutgers:

Xi Peng, Rutgers University:

Shijie Geng, Rutgers:

Shaoting Zhang, University of North Carolina at Charlotte: http://webpages.uncc.edu/~szhang16/

Lingfei Wu, IBM T. J. Watson Research Center:

Dimitris Metaxas, Rutgers:

【Object detection】Parallel Feature Pyramid Network for Object Detection

Seung-Wook Kim, Korea University:

Hyong-Keun Kook, Korea University:

Jee-Young Sun, Korea University:

Mun-Cheon Kang, Korea University:

Sung-Jea Ko, Korea University:

【Object detection】Deep Feature Pyramid Reconfiguration for Object Detection

Tao Kong, Tsinghua:

Fuchun Sun, Tsinghua:

Wenbing Huang, Tencent AI Lab:

【Object detection】Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes

Fangneng Zhan, Nanyang Technological University:

Shijian Lu, Nanyang Technological University:

Chuhui Xue, Nanyang Technological University:

【Object detection】Context Refinement for Object Detection

Zhe Chen, University of Sydney:

Shaoli Huang, University of Sydney:

Dacheng Tao, University of Sydney:

【Object detection】Localization Recall Precision

Kemal Oksuz, Middle East Technical University:

Bar?? Can Çam, Roketsan:

Emre Akbas, Middle East Technical University:

Sinan Kalkan, Middle East Technical University:

【Object detection】Object Detection with an Aligned Spatial-Temporal Memory

Fanyi Xiao, University of California Davis:

Yong Jae Lee, University of California, Davis:

【Object detection】Quantization Mimic: Towards Very Tiny CNN for Object Detection

Yi Wei, Tsinghua University:

Xinyu Pan, MMLAB, CUHK:

Hongwei Qin, Sense Time:

Junjie Yan, Sensetime: http://www.cbsr.ia.ac.cn/users/jjyan/main.htm

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

【Object detection】Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input

David Harwath, MIT CSAIL:

Adria Recasens, Massachusetts Institute of Technology:

Dídac Surís, Universitat Politecnica de Catalunya:

Galen Chuang, MIT:

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

James Glass, MIT:

【Object detection】Implicit 3D Orientation Learning for 6D Object Detection from RGB Images

Martin Sundermeyer, German Aerospace Center (DLR):

Zoltan Marton, DLR:

Maximilian Durner, DLR:

Rudolph Triebel, German Aerospace Center (DLR):

【Object detection】Det Net: Design Backbone for Object Detection

Zeming Li, Tsinghua University:

Megvii Inc:

Chao Peng, Megvii(Face++) Inc:

Gang Yu, Face++:

Yangdong Deng, Tsinghua University:

Xiangyu Zhang, Megvii Inc:

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

【Object detection】License Plate Detection and Recognition in Unconstrained Scenarios

Sérgio Silva, UFRGS:

Claudio Jung, UFRGS:

【Object detection】Zero-Annotation Object Detection with Web Knowledge Transfer

Qingyi Tao, Nanyang Techonological University:

Hao Yang, NTU:

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

【Object detection】Receptive Field Block Net for Accurate and Fast Object Detection

Songtao Liu, BUAA:

Di Huang, Beihang University, China:

Yunhong Wang, State Key Laboratory of Virtual Reality Technology and System, Beihang University, Beijing 100191, China: http://irip.buaa.edu.cn/Chinese.html

【Object detection】TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection

Yunchao Wei, UIUC:

Zhiqiang Shen, UIUC:

Honghui Shi, UIUC:

Bowen Cheng, UIUC:

Jinjun Xiong, IBM Thomas J. Watson Research Center:

Jiashi Feng, NUS: https://sites.google.com/site/jshfeng/

Thomas Huang, UIUC:

【Object detection】Visual-Inertial Object Detection and Mapping

Xiaohan Fei, UCLA:

Stefano Soatto, UCLA: http://vision.ucla.edu/projects.html

【Object detection】Learning Region Features for Object Detection

Jiayuan Gu, Peking University:

Han Hu, Microsoft Research Asia:

Liwei Wang, Peking University:

Yichen Wei, MSR Asia:

Jifeng Dai, Microsoft Research Asia:

【Object detection】Zero-Shot Object Detection

Ankan Bansal, University of Maryland:

Karan Sikka, SRI International:

Gaurav Sharma, NEC Labs America:

Rama Chellappa, University of Maryland:

Ajay Divakaran, SRI, USA:

【Object detection】ML-Loc Net: Improving Object Localization with Multi-view Learning Network

Xiaopeng Zhang, National University of Singapore:

Jiashi Feng, NUS: https://sites.google.com/site/jshfeng/

【Object detection】SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection

Yong Hyun Kim, POSTECH:

【Object detection】Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers

Nataraj Jammalamadaka, Intel Labs:

Xia Zhu, Intel Labs:

Dipankar Das, Intel Labs:

Bharat Kaul, Intel Labs:

Theodore Willke, Intel Labs:

【Object detection】Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping

Chuhui Xue, Nanyang Technological University:

Shijian Lu, Nanyang Technological University:

Fangneng Zhan, Nanyang Technological University:

【Object detection】Modeling Visual Context is Key to Augmenting Object Detection Datasets

NIKITA DVORNIK, INRIA:

Julien Mairal, INRIA:

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

【Object detection】Self-produced Guidance for Weakly-supervised Object Localization

Xiaolin Zhang, University of Technology Sydney:

Yunchao Wei, UIUC:

Guoliang Kang, UTS:

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

Thomas Huang, UIUC:

【Object detection】Deep Regionlets for Object Detection

Hongyu Xu, University of Maryland:

Xutao Lv, Intellifusion:

Xiaoyu Wang, -:

Zhou Ren, Snap Inc.:

Navaneeth Bodla, University of Maryland:

Rama Chellappa, University of Maryland:

【Object detection】Fighting Fake News: Image Splice Detection via Learned Self-Consistency

Jacob Huh, Carnegie Mellon University:

Andrew Liu, University of California, Berkeley:

Andrew Owens, UC Berkeley:

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

【Object detection】Weakly Supervised Region Proposal Network and Object Detection

Peng Tang, Huazhong University of Science and Technology:

Xinggang Wang, Huazhong Univ. of Science and Technology: http://www.xinggangw.info/

Angtian Wang, Huazhong University of Science and Technology:

Yongluan Yan, Huazhong University of Science and Technology:

Wenyu Liu, Huazhong University of Science and Technology:

Junzhou Huang, Tencent AI Lab:

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

【Object detection】Fully Motion-Aware Network for Video Object Detection

Shiyao Wang, Tsinghua University:

Yucong Zhou, Beihang University:

Junjie Yan, Sensetime Group Limited: http://www.cbsr.ia.ac.cn/users/jjyan/main.htm

【Object detection】SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network

Yongqiang Zhang, Harbin institute of Technology/KAUST:

Yancheng Bai, KAUST/ISCAS:

Mingli Ding, Harbin institute of Technology:

Bernard Ghanem, KAUST: http://www.bernardghanem.com/

【Object detection】Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline

Zhenbo Xu, University of Science and Technology in China:

Wei Yang, University of Science and Technology in China:

Ajin Meng, University of Science and Technology in China:

Nanxue Lu, University of Science and Technology in China:

Huan Huang, Xingtai Financial Holdings Group Co., Ltd.:

【Object detection】Transferring Common-Sense Knowledge for Object Detection

Krishna Kumar Singh, University of California Davis:

Santosh Divvala, Allen AI: http://homes.cs.washington.edu/~santosh/

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

Yong Jae Lee, University of California, Davis:

【Object detection】Unsupervised Hard-Negative Mining from Videos for Object Detection

Sou Young Jin, UMASS Amherst:

Huaizu Jiang, UMass Amherst:

Aruni Roy Chowdhury, University of Massachusetts, Amherst:

Ashish Singh, UMASS Amherst:

Aditya Prasad, UMASS Amherst:

Deep Chakraborty, UMASS Amherst:

Erik Learned-Miller, University of Massachusetts, Amherst:

【Object detection】Corner Net: Detecting Objects as Paired Keypoints

Hei Law, University of Michigan:

Jia Deng, University of Michigan:

【Object detection】Acquisition of Localization Confidence for Accurate Object Detection

Borui Jiang, Peking University:

Ruixuan Luo, Peking University:

Jiayuan Mao, Tsinghua University:

Tete Xiao, Peking University:

Yuning Jiang, Megvii(Face++) Inc:

【Object detection】Using Object Information for Spotting Text

Shitala Prasad, NTU Singapore:

Wai-Kin Adams Kong, Nanyang Technological University:

【Object detection】Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Yunchao Wei, UIUC:

Bowen Cheng, UIUC:

Honghui Shi, UIUC:

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

Jinjun Xiong, IBM Thomas J. Watson Research Center:

Thomas Huang, UIUC:

【Object detection】Deep Continuous Fusion for Multi-Sensor 3D Object Detection

Ming Liang, Uber:

Shenlong Wang, Uber ATG, University of Toronto:

Bin Yang, Uber ATG, University of Toronto:

Raquel Urtasun, Uber ATG: http://www.cs.toronto.edu/~urtasun/

【Object detection】Learning to Look around Objects for Top-View Representations of Outdoor Scenes

Samuel Schulter, NEC Labs:

Menghua Zhai, University of Kentucky:

Nathan Jacobs, University of Kentucky:

Manmohan Chandraker, NEC Labs America: http://cseweb.ucsd.edu/~mkchandraker/

【Saliency detection】Connecting Gaze, Scene and Attention

Eunji Chong, Georgia Institute of Technology:

Nataniel Ruiz, Georgia Institute of Technology:

Richard Wang, Georgia Institute of Technology:

Yun Zhang, Georgia Institute of Technology:

【Saliency detection】Unsupervised CNN-based co-saliency detection with graphical optimization

Kuang-Jui Hsu, Academia Sinica:

Chung-Chi Tsai, Texas A&M University:

Yen-Yu Lin, Academia Sinica:

Xiaoning Qian, Texas A&M University:

Yung-Yu Chuang, National Taiwan University:

【Saliency detection】Saliency Preservation in Low-Resolution Grayscale Images

Shivanthan Yohanandan, RMIT University:

Adrian Dyer, RMIT University:

Dacheng Tao, University of Sydney:

Andy Song, RMIT University:

【Saliency detection】Saliency Detection in 360$^\circ$ Videos

Ziheng Zhang, Shanghaitech University:

Yanyu Xu, Shanghaitech University:

Shenghua Gao, Shanghaitech University:

Jingyi Yu, Shanghai Tech University:

【Saliency detection】Toward Scale-Invariance and Position-Sensitive Object Proposal Networks

Hsueh-Fu Lu, Umbo Computer Vision:

Ping-Lin Chang, Umbo Computer Vision:

Xiaofei Du, Umbo Computer Vision:

【Saliency detection】Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks

Adria Recasens, Massachusetts Institute of Technology:

Petr Kellnhofer, MIT:

Simon Stent, Toyota Research Institute:

Wojciech Matusik, MIT:

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

【Saliency detection】Pseudo Pyramid Deeper Bidirectional Conv LSTM for Video Saliency Detection

Hongmei Song, Beijing Institute of Technology:

Sanyuan Zhao, Beijing Institute of Technology:

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

Kin-Man Lam, The Hong Kong Polytechnic University:

【Saliency detection】Reverse Attention for Salient Object Detection

huhan Chen, Yangzhou University:

Xiuli Tan, Yangzhou University:

Ben Wang, Yangzhou University:

Xuelong Hu, Yangzhou University:

【Saliency detection】AGIL: Learning Attention from Human for Visuomotor Tasks

Ruohan Zhang, University of Texas at Austin:

Zhuode Liu, Google Inc.:

Luxin Zhang, Peking University:

Jake Whritner, University of Texas at Austin:

Karl Muller, University of Texas at Austin:

Mary Hayhoe, University of Texas at Austin:

Dana Ballard, University of Texas at Austin:

【Saliency detection】Viewpoint Estimation – Insights & Model

Gilad Divon, Technion:

Ayellet Tal, Technion: http://webee.technion.ac.il/labs/cgm/

【Saliency detection】Task-driven Webpage Saliency

Quanlong Zheng, City University of Hong Kong:

Jianbo Jiao, City University of Hong Kong:

Ying Cao, City University of Hong Kong:

Rynson Lau, City University of Hong Kong:

【Saliency detection】Appearance-Based Gaze Estimation via Evaluation-Guided Asymmetric Regression

Yihua Cheng, Beihang University:

Feng Lu, U. Tokyo:

Xucong Zhang, Max Planck Institute for Informatics and Saarland University:

【Saliency detection】Deep VS: A Deep Learning Based Video Saliency Prediction Approach

Lai Jiang, BUAA:

Mai Xu, BUAA:

Minglang Qiao, BUAA:

Zulin Wang, BUAA:

【Saliency detection】Deep Pictorial Gaze Estimation

Seonwook Park, ETH Zurich:

Adrian Spurr, ETH Zurich:

Otmar Hilliges, ETH Zurich:

【Saliency detection】Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

Matthias Kümmerer, University of Tübingen:

Thomas Wallis, University of Tübingen:

Matthias Bethge, University of Tübingen:

【Saliency detection】Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground

Deng-Ping Fan, Nankai University:

Jiang-Jiang Liu, Nankai University:

Shanghua Gao, Nankai University:

Qibin Hou, Nankai University:

Ming-Ming Cheng, Nankai University:

Ali Borji, University of Central Florida: http://ilab.usc.edu/borji/

【Saliency detection】Contour Knowledge Transfer for Salient Object Detection

Xin Li, UESTC:

Fan Yang, UESTC:

Hong Cheng, UESTC:

Wei Liu, Digital Media Technology Key Laboratory of Sichuan Province, UESTC: http://www.ee.columbia.edu/~wliu/

Dinggang Shen, UNC:

【Scene recognition】Question-Guided Hybrid Convolution for Visual Question Answering

gao peng, Chinese university of hong kong:

Hongsheng Li, Chinese University of Hong Kong:

Shuang Li, The Chinese University of Hong Kong:

Pan Lu, Tsinghua University:

Yikang LI, The Chinese University of Hong Kong:

Steven Hoi, SMU:

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

【Scene recognition】Textual Explanations for Self-Driving Vehicles

Jinkyu Kim, UC Berkeley:

Anna Rohrbach, UC Berkeley:

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

John Canny, UC Berkeley:

Zeynep Akata, University of Amsterdam:

【Scene recognition】Recurrent Fusion Network for Image captioning

Wenhao Jiang, Tencent AI Lab:

Lin Ma, Tencent AI Lab:

Yu-Gang Jiang, Fudan University:

Wei Liu, Tencent AI Lab: http://www.ee.columbia.edu/~wliu/

Tong Zhang, Tecent AI Lab: http://tongzhang-ml.org/

【Scene recognition】Unpaired Image Captioning by Language Pivoting

Jiuxiang Gu, Nanyang Technological University:

Shafiq Joty, Nanyang Technological University:

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

Gang Wang, Alibaba Group:

【Scene recognition】Grounding Visual Explanations

Lisa Anne Hendricks, Uc berkeley:

Ronghang Hu, University of California, Berkeley:

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

Zeynep Akata, University of Amsterdam:

【Scene recognition】Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation

Yikang LI, The Chinese University of Hong Kong:

Bolei Zhou, MIT:

Yawen Cui, National University of Defense Technology:

Jianping Shi, Sensetime Group Limited:

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

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

【Scene recognition】The Mutex Watershed: Efficient, Parameter-Free Image Partitioning

Steffen Wolf, Univertity of Heidelberg:

Constantin Pape, University of Heidelberg:

Nasim Rahaman, University of Heidelberg:

Anna Kreshuk, University of Heidelberg:

Ullrich Köthe, University of Heidelberg:

Fred Hamprecht, Heidelberg Collaboratory for Image Processing:

【Scene recognition】Real-to-Virtual Domain Unification for End-to-End Autonomous Driving

Luona Yang, Carnegie Mellon University:

Xiaodan Liang, Carnegie Mellon University:

Eric Xing, Petuum Inc.: http://www.cs.cmu.edu/~epxing/

【Scene recognition】Women also Snowboard: Overcoming Bias in Captioning Models

Lisa Anne Hendricks, UC Berkeley:

Kaylee Burns, UC Berkeley:

Kate Saenko, Boston University:

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

Anna Rohrbach, UC Berkeley:

【Scene recognition】Question Type Guided Attention in Visual Question Answering

Yang Shi, University of California, Irvine:

Tommaso Furlanello, University of Southern California:

Sheng Zha, Amazon Web Services:

Anima Anandkumar, Amazon:

【Scene recognition】Memory Aware Synapses: Learning what

Rahaf Aljundi, KU Leuven:

Francesca babiloni, KU Leuven:

Mohamed Elhoseiny, Facebook:

Marcus Rohrbach, Facebook AI Research:

Tinne Tuytelaars, K.U. Leuven: http://homes.esat.kuleuven.be/~tuytelaa/

【Scene recognition】Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition

Guojun Yin, University of Science and Technology of China:

Lu Sheng, The Chinese University of Hong Kong:

Bin Liu, University of Science and Technology of China:

Nenghai Yu, University of Science and Technology of China:

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

Chen Change Loy, Chinese University of Hong Kong: http://www.eecs.qmul.ac.uk/~ccloy/

Jing Shao, The Chinese University of Hong Kong:

【Scene recognition】Zero-shot keyword search for visual speech recognition in-the-wild

Themos Stafylakis, University of Nottingham:

Georgios Tzimiropoulos, University of Nottingham: http://www.cs.nott.ac.uk/~pszyt/

【Scene recognition】Understanding Degeneracies and Ambiguities in Attribute Transfer

Attila Szabo, University of Bern:

Qiyang Hu, University of Bern:

Tiziano Portenier, University of Bern:

Matthias Zwicker, University of Maryland:

Paolo Favaro, Bern University, Switzerland:

【Scene recognition】Start, Follow, Read: End-to-End Full Page Handwriting Recognition

Curtis Wigington, Brigham Young University:

Chris Tensmeyer, Brigham Young University:

Brian Davis, Brigham Young University:

Bill Barrett, Brigham Young University:

Brian Price, Adobe:

Scott Cohen, Adobe Research:

【Scene recognition】Rethinking the Form of Latent States in Image Captioning

Bo Dai, the Chinese University of Hong Kong:

Deming Ye, Tsinghua University:

Dahua Lin, The Chinese University of Hong Kong: http://dahua.me/

【Scene recognition】Semi-supervised Fused GAN for Conditional Image Generation

Navaneeth Bodla, University of Maryland:

Gang Hua, Microsoft Cloud and AI: http://www.cs.stevens.edu/~ghua/

Rama Chellappa, University of Maryland:

【Scene recognition】Fine-grained Video Categorization with Redundancy Reduction Attention

Chen Zhu, University of Maryland:

Xiao Tan, Baidu Inc.:

Feng Zhou, Baidu Inc.:

Xiao Liu, Baidu Research:

Kaiyu Yue, Baidu Inc.:

Errui Ding, Baidu Inc.:

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

【Scene recognition】Open Set Learning with Counterfactual Images

Lawrence Neal, Oregon State University:

Matthew Olson, Oregon State University:

Xiaoli Fern, Oregon State University:

Weng-Keen Wong, Oregon State University:

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

【Scene recognition】Transductive Centroid Projection for Semi-supervised Large-scale Recognition

Yu Liu, The Chinese University of Hong Kong:

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

Guanglu Song, Sensetime:

Jing Shao, Sensetime:

【Scene recognition】Goal-Oriented Visual Question Generation via Intermediate Rewards

Junjie Zhang, University of Technology, Sydney:

Qi Wu, University of Adelaide:

Chunhua Shen, University of Adelaide: https://cs.adelaide.edu.au/~chhshen/

Jian Zhang, UTS:

Jianfeng Lu, Nanjing University of Science and Technology:

Anton Van Den Hengel, University of Adelaide:

【Scene recognition】Wild Dash – Creating Hazard-Aware Benchmarks

Oliver Zendel, AIT Austrian Institute of Technology:

Katrin Honauer, Heidelberg University:

Markus Murschitz, AIT Austrian Institute of Technology:

Daniel Steininger, AIT Austrian Institute of Technology:

Gustavo Fernandez, n/a:

【Scene recognition】Learning Visual Question Answering by Bootstrapping Hard Attention

Mateusz Malinowski, Deep Mind:

Carl Doersch, Deep Mind:

Adam Santoro, Deep Mind:

Peter Battaglia, Deep Mind:

【Scene recognition】Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data

Yabin Zhang, South China University of Technology:

Tang Hui, South China University of Technology:

Kui Jia, South China University of Technology:

【Scene recognition】Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification

Danfeng Hong, Technical University of Munich (TUM):

German Aerospace Center (DLR):

Naoto Yokoya, RIKEN Center for Advanced Intelligence Project (AIP):

Jian Xu, German Aerospace Center (DLR):

Xiaoxiang Zhu, DLR&TUM:

【Scene recognition】VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions

Qing Li, University of Science and Technology of China:

Qingyi Tao, Nanyang Techonological University:

Shafiq Joty, Nanyang Technological University:

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

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

【Scene recognition】NNEval: Neural Network based Evaluation Metric for Image Captioning

Naeha Sharif, University of Western Australia:

Lyndon White, University of Western Australia:

Mohammed Bennamoun, University of Western Australia: http://www.csse.uwa.edu.au/~bennamou/

Syed Afaq Ali Shah, Department of Computer Science and Software Engineering, The University of Western Australia:

【Scene recognition】Coded Illumination and Imaging for Fluorescence Based Classification

Yuta Asano, Tokyo Institute of Technology:

Misaki Meguro, Tokyo Institute of Technology:

Chao Wang, Kyushu Institute of Technology:

Antony Lam, Saitama University:

Yinqiang Zheng, National Institute of Informatics: https://researchmap.jp/yinqiangzheng

Takahiro Okabe, Kyushu Institute of Technology:

Imari Sato, National Institute of Informatics:

【Scene recognition】CPla Net: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps

Paul Hongsuck Seo, POSTECH:

Tobias Weyand, Google Inc.:

Jack Sim, Google LLC:

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

【Scene recognition】”Factual” or “Emotional”: Stylized Image Captioning with Adaptive Learning and Attention”

Tianlang Chen, University of Rochester:

Zhongping Zhang, University of Rochester:

Quanzeng You, Microsoft:

CHEN FANG, Adobe Research, San Jose, CA:

Zhaowen Wang, Adobe Research:

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

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

【Scene recognition】Does Haze Removal Help Image Classification?

Yanting Pei, Beijing Jiaotong University:

Yaping Huang, Beijing Jiaotong University:

Qi Zou, Beijing Jiaotong University:

Yuhang Lu, University of South Carolina:

Song Wang, University of South Carolina:

【Scene recognition】Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition

Huajie Jiang, ICT, CAS:

Ruiping Wang, ICT, CAS:

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

Xilin Chen, China:

【Scene recognition】Sphere Net: Learning Spherical Representations for Detection and Classification in Omnidirectional Images

Benjamin Coors, MPI Intelligent Systems, Bosch:

Alexandru Condurache, Bosch:

Andreas Geiger, MPI-IS and University of Tuebingen:

【Scene recognition】A dataset and architecture for visual reasoning with a working memory

Guangyu Robert Yang, Columbia University:

Igor Ganichev, Google Brain:

Xiao-Jing Wang, New York University:

Jon Shlens, Google:

David Sussillo, Google Brain:

【Scene recognition】Selective Zero-Shot Classification with Augmented Attributes

Jie Song, College of Computer Science and Technology, Zhejiang University:

Chengchao Shen, Zhejiang University:

Jie Lei, Zhejiang University:

An-Xiang Zeng, Alibaba:

Kairi Ou, Alibaba:

Dacheng Tao, University of Sydney:

Mingli Song, Zhejiang University:

【Scene recognition】Ask, Acquire and Attack: Data-free UAP generation using Class impressions

Konda Reddy Mopuri, Indian Institute of Science, Bangalore:

Phani Krishna Uppala, Indian Institute of Science:

Venkatesh Babu RADHAKRISHNAN, Indian Institute of Science:

【Scene recognition】Visual Question Generation for Class Acquisition of Unknown Objects

Kohei Uehara, The University of Tokyo:

Antonio Tejero-de-Pablos, The University of Tokyo:

Yoshitaka Ushiku, The University of Tokyo:

Tatsuya Harada, The University of Tokyo:

【Scene recognition】Deep Attention Neural Tensor Network for Visual Question Answering

Yalong Bai, Harbin Institute of Technology:

Jianlong Fu, Microsoft Research:

Tao Mei, JD.com:

【Scene recognition】Improving Fine-Grained Visual Classification using Pairwise Confusion

Abhimanyu Dubey, Massachusetts Institute of Technology:

Otkrist Gupta, MIT:

Pei Guo, Brigham Young University:

Ryan Farrell, Brigham Young University:

Ramesh Raskar, Massachusetts Institute of Technology:

Nikhil Naik, MIT:

【Scene recognition】Evaluating Capability of Deep Neural Networks for Image Classification via Information Plane

Hao Cheng, Shanghaitech University:

Dongze Lian, Shanghaitech University:

Shenghua Gao, Shanghaitech University:

Yanlin Geng, Shanghaitech University:

【Scene recognition】Deep Imbalanced Attribute Classification using Visual Attention Aggregation

Nikolaos Sarafianos, University of Houston:

Xiang Xu, University of Houston:

Ioannis Kakadiaris, University of Houston:

【Scene recognition】Geolocation Estimation of Photos using a Hierarchical Model and Scene Classification

Eric Müller-Budack, Leibniz Information Centre of Science and Technology (TIB):

Kader Pustu-Iren, Leibniz Information Center of Science and Technology (TIB):

Ralph Ewerth, Leibniz Information Center of Science and Technology (TIB):

【Scene recognition】Boosted Attention: Leveraging Human Attention for Image Captioning

Shi Chen, University of Minnesota:

Qi Zhao, University of Minnesota:

【Scene recognition】Is Robustness the Cost of Accuracy? — Lessons Learned from 18 Deep Image Classifiers

Dong Su, IBM Research T.J. Watson Center:

Huan Zhang, UC Davis:

Hongge Chen, MIT:

Jinfeng Yi, JD AI Research:

Pin-Yu Chen, IBM Research:

Yupeng Gao, IBM Research AI:

【Scene recognition】Hierarchy of Alternating Specialists for Scene Recognition

Hyo Jin Kim, University of North Carolina at Chapel Hill:

Jan-Michael Frahm, UNC-Chapel Hill:

【Scene recognition】Visual Question Answering as a Meta Learning Task

Damien Teney, The Unversity of Adelaide:

Anton van den Hengel, The University of Adelaide:

【Scene recognition】Recognition in Terra Incognita

Sara Beery, Caltech:

Grant van Horn, Caltech:

Pietro Perona, Caltech: http://vision.caltech.edu/Perona.html

【Scene recognition】Exploring Visual Relationship for Image Captioning

Ting Yao, Microsoft Research:

Yingwei Pan, University of Science and Technology of China:

Yehao Li, Sun Yat-Sen University:

Tao Mei, JD.com:

【Scene recognition】Spatial Pyramid Calibration for Image Classification

Yan Wang, Shanghai Jiao Tong University:

Lingxi Xie, JHU:

Siyuan Qiao, Johns Hopkins University:

Ya Zhang, Cooperative Medianet Innovation Center, Shang hai Jiao Tong University:

Wenjun Zhang, Shanghai Jiao Tong University:

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

【Scene recognition】DFT-based Transformation Invariant Pooling Layer for Visual Classification

Jongbin Ryu, Hanyang University:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

Jongwoo Lim, Hanyang University:

【Scene recognition】A New Large Scale Dynamic Texture Dataset with Application to Conv Net Understanding

Isma Hadji, York University:

Rick Wildes, York University:

【Scene recognition】Less is More: Picking Informative Frames for Video Captioning

Yangyu Chen, University of Chinese Academy of Sciences:

Shuhui Wang, vipl,ict,Chinese academic of science:

Weigang Zhang, Harbin Institute of Technology, Weihai:

Qingming Huang, University of Chinese Academy of Sciences, China:

【Scene recognition】Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition

Ming Sun, baidu:

Yuchen Yuan, Baidu Inc.:

Feng Zhou, Baidu Research:

Errui Ding, Baidu Inc.:

【Scene recognition】Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

Chaojian Yu, Huazhong University of Science and Technology:

Qi Zheng, Huazhong University of Science and Technology:

Xinyi Zhao, Huazhong University of Science and Technology:

Peng Zhang, Huazhong University of Science and Technology:

Xinge YOU, School of Electronic Information and Communications,Huazhong University of Science and Technology:

【Scene recognition】On Offline Evaluation of Vision-based Driving Models

Felipe Codevilla, UAB:

Antonio Lopez, CVC & UAB:

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

Alexey Dosovitskiy, Intel Labs:

【Scene recognition】ISNN – Impact Sound Neural Network for Material and Geometry Classification

Auston Sterling, UNC Chapel Hill:

Justin Wilson, UNC Chapel Hill:

Sam Lowe, UNC Chapel Hill:

Ming Lin, UNC Chapel Hill:

【Scene recognition】Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially Labeled Data

Xihui Liu, The Chinese University of Hong Kong:

Hongsheng Li, Chinese University of Hong Kong:

Jing Shao, The Chinese University of Hong Kong:

Dapeng Chen, The Chinese University of Hong Kong:

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

【Scene recognition】ADVISE: Symbolism and External Knowledge for Decoding Advertisements

Keren Ye, University of Pittsburgh:

Adriana Kovashka, University of Pittsburgh:

【Scene recognition】Visual Coreference Resolution in Visual Dialog using Neural Module Networks

Satwik Kottur, Carnegie Mellon University:

José M. F. Moura, Carnegie Mellon University: https://users.ece.cmu.edu/~moura/

Devi Parikh, Georgia Tech & Facebook AI Research: https://filebox.ece.vt.edu/~parikh/

Dhruv Batra, Georgia Tech & Facebook AI Research:

Marcus Rohrbach, Facebook AI Research:

【Scene recognition】Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study

Zhenyu Wu, Texas A&M University:

Zhangyang Wang, Texas A&M University:

Zhaowen Wang, Adobe Research:

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

【Scene recognition】Deep Co-Training for Semi-Supervised Image Recognition

Siyuan Qiao, Johns Hopkins University:

Wei Shen, Shanghai University:

Zhishuai Zhang, Johns Hopkins University:

Bo Wang, Hikvision Research Institue:

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

【Image retrieval】Generative Domain-Migration Hashing for Sketch-to-Image Retrieval

Jingyi Zhang, University of Electronic Science and Technology of China:

Fumin Shen, UESTC:

Li Liu, the inception institute of artificial intelligence:

Fan Zhu, the inception institute of artificial intelligence:

Mengyang Yu, ETH Zurich:

Ling Shao, Inception Institute of Artificial Intelligence: http://lshao.staff.shef.ac.uk/

Heng Tao Shen, University of Electronic Science and Technology of China (UESTC):

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

【Image retrieval】Cross-Modal Hamming Hashing

Yue Cao, Tsinghua University:

Mingsheng Long, Tsinghua University:

Bin Liu, Tsinghua University:

Jianmin Wang, Tsinghua University, China:

【Image retrieval】A Modulation Module for Multi-task Learning with Applications in Image Retrieval

Xiangyun Zhao, Northwestern University:

Haoxiang Li, Adobe:

Xiaohui Shen, Adobe Research:

Xiaodan Liang, Carnegie Mellon University:

Ying Wu, Northwestern University:

【Image retrieval】Product Quantization Network for Fast Image Retrieval

Tan Yu, Nanyang Technological University:

Junsong Yuan, State University of New York at Buffalo, USA: https://cse.buffalo.edu/~jsyuan/

CHEN FANG, Adobe Research, San Jose, CA:

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

【Image retrieval】Temporal Modular Networks for Retrieving Complex Compositional Activities in Video

Bingbin Liu, Stanford University:

Serena Yeung, Stanford University:

Edward Chou, Stanford University:

De-An Huang, Stanford University:

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

Juan Carlos Niebles, Stanford University: http://www.niebles.net/

【Image retrieval】Beyond Part Models: Person Retrieval with Refined Part Pooling

Yifan Sun, Tsinghua University:

Liang Zheng, Singapore University of Technology and Design:

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

Qi Tian , The University of Texas at San Antonio:

Shengjin Wang, Tsinghua University:

【Image retrieval】Towards Optimal Deep Hashing via Policy Gradient

Xin Yuan, Tsinghua University:

Liangliang Ren, Tsinghua University:

Jiwen Lu, Tsinghua University:

Jie Zhou, Tsinghua University, China: https://www.tsinghua.edu.cn/publish/auen/1713/2011/20110506105532098625469/20110506105532098625469_.html

【Image retrieval】Compositing-aware Image Search

Hengshuang Zhao, The Chinese University of Hong Kong:

Xiaohui Shen, Adobe Research:

Zhe Lin, Adobe Research: http://www.adobe.com/technology/people/san-jose/zhe-lin.html

Sunkavalli Kalyan, Adobe Research:

Brian Price, Adobe:

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

【Image retrieval】Hashing with Binary Matrix Pursuit

Fatih Cakir, Boston University:

Kun He, Boston University:

Stan Sclaroff, Boston University:

【Image retrieval】A Joint Sequence Fusion Model for Video Question Answering and Retrieval

Youngjae Yu, Seoul National University Vision and Learning Lab:

Jongseok Kim, Seoul National University Vision and Learning Lab:

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

【Image retrieval】Curriculum Net: Learning from Large-Scale Web Images without Human Annotation

Sheng Guo, Malong Technologies:

Weilin Huang, Malong Technologies:

Haozhi Zhang, Malong Technologies:

【Image retrieval】Find and Focus: Retrieve and Localize Video Events with Natural Language Queries

Dian SHAO, The Chinese University of Hong Kong:

Yu Xiong, The Chinese University of HK:

Yue Zhao, The Chinese University of Hong Kong:

Qingqiu Huang, CUHK:

Yu Qiao, Multimedia Laboratory, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences: http://mmlab.siat.ac.cn/yuqiao/

Dahua Lin, The Chinese University of Hong Kong: http://dahua.me/

【Image retrieval】Highly-Economized Multi-View Binary Compression for Scalable Image Clustering

Zheng Zhang, Harbin Institute of Technology Shenzhen Graduate School:

Li Liu, the inception institute of artificial intelligence:

Jie Qin, ETH Zurich:

Fan Zhu, the inception institute of artificial intelligence:

Fumin Shen, UESTC:

Yong Xu, Harbin Institute of Technology Shenzhen Graduate School:

Ling Shao, Inception Institute of Artificial Intelligence: http://lshao.staff.shef.ac.uk/

Heng Tao Shen, University of Electronic Science and Technology of China (UESTC):

【Image retrieval】Sidekick Policy Learning for Active Visual Exploration

Santhosh Kumar Ramakrishnan, University of Texas at Austin:

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

【Image retrieval】Progressive Neural Architecture Search

Chenxi Liu, Johns Hopkins University:

Maxim Neumann, Google:

Barret Zoph, Google:

Jon Shlens, Google:

Wei Hua, Google:

Li-Jia Li, Google:

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

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

Jonathan Huang, Google: https://ai.google/research/people/JonathanHuang

Kevin Murphy, Google:

【Image retrieval】Forest Hash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks

Qiang Qiu, Duke University:

Jose Lezama, Universidad de la Republica, Uruguay:

Alex Bronstein, Tel Aviv University, Israel:

Guillermo Sapiro, Duke University:

【Image retrieval】A Zero-Shot Framework for Sketch based Image Retrieval

Sasikiran Yelamarthi , IIT Madras:

Shiva Krishna Reddy M, Indian Institute of Technology Madras:

Ashish Mishra, IIT Madras:

Anurag Mittal, Indian Institute of Technology Madras: http://www.cse.iitm.ac.in/~amittal/

【Image retrieval】Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering

Medhini Narasimhan, University of Illinois at Urbana-Champaign:

Alexander Schwing, UIUC: http://www.alexander-schwing.de/

【Image retrieval】Single Shot Scene Text Retrieval

Lluis Gomez, Universitat Autónoma de Barcelona:

Andres Mafla, Computer Vision Center:

Marçal Rossinyol, Universitat Autónoma de Barcelona:

Dimosthenis Karatzas, Computer Vision Centre:

【Image retrieval】Generalizing A Person Retrieval Model Hetero- and Homogeneously

Zhun Zhong, Xiamen University:

Liang Zheng, Singapore University of Technology and Design:

Shaozi Li, Xiamen University, China:

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

【Image retrieval】Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval

Xi Zhang, Sun Yat-Sen University:

Hanjiang Lai, Sun Yat-Sen university:

Jiashi Feng, NUS: https://sites.google.com/site/jshfeng/

【Image retrieval】Semi-Supervised Generative Adversarial Hashing for Image Retrieval

Guan’an Wang, Chinese Academy of Sciences:

Qinghao Hu, Chinese Academy of Sciences:

Jian Cheng, Chinese Academy of Sciences, China: http://www.nlpr.ia.ac.cn/jcheng/

Zengguang Hou, Chinese Academy of Sciences:

【3D modeling】Fast and Precise Camera Covariance Computation for Large 3D Reconstruction

Michal Polic, Czech Technical University in Prague:

Wolfgang Foerstner, University Bonn:

Tomas Pajdla, Czech Technical University in Prague:

【3D modeling】Semi-Dense 3D Reconstruction with a Stereo Event Camera

Yi Zhou, The Australian National University:

Guillermo Gallego, University of Zurich:

Henri Rebecq, University of Zurich:

Laurent Kneip, Shanghai Tech University:

HONGDONG LI, Australian National University, Australia:

Davide Scaramuzza, University of Zurich& ETH Zurich, Switzerland: http://rpg.ifi.uzh.ch/people_scaramuzza.html

【3D modeling】Simultaneous 3D Reconstruction for Water Surface and Underwater Scene

Yiming Qian, University of Alberta:

Yinqiang Zheng, National Institute of Informatics: https://researchmap.jp/yinqiangzheng

Minglun Gong, Memorial University:

Herb Yang, University of Alberta:

【3D modeling】Specular-to-Diffuse Translation for Multi-View Reconstruction

Shihao Wu, University of Bern:

Hui Huang, Shenzhen University: http://vcc.szu.edu.cn/~huihuang

Tiziano Portenier, University of Bern:

Matan Sela, Technion – Israel Institute of Technology:

Danny Cohen-Or, Tel Aviv University:

Ron Kimmel, Technion:

Matthias Zwicker, University of Maryland:

【3D modeling】Neural Procedural Reconstruction for Residential Buildings

Huayi Zeng, Washington University in St.Louis:

Jiaye Wu, Washington University in St.Louis:

Yasutaka Furukawa, Simon Fraser University:

【3D modeling】Generating 3D Faces using Convolutional Mesh Autoencoders

Anurag Ranjan, MPI for Intelligent Systems:

Timo Bolkart, Max Planck for Intelligent Systems:

Soubhik Sanyal, Max Planck Institute for Intelligent Systems:

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

【3D modeling】Online Dictionary Learning for Approximate Archetypal Analysis

Jieru Mei, Microsoft Research Asia:

Chunyu Wang, Microsoft Research asia:

Wenjun Zeng, Microsoft Research:

【3D modeling】Deep Wrinkles: Accurate and Realistic Clothing Modeling

Zorah Laehner, TU Munich:

Tony Tung, Facebook / Oculus Research:

Daniel Cremers, TUM: http://vision.in.tum.de/

【3D modeling】Fast Light Field Reconstruction With Deep Coarse-To-Fine Modeling of Spatial-Angular Clues

Henry W. F. Yeung, the University of Sydney:

Junhui Hou, City University of Hong Kong, Hong Kong:

Jie Chen, Nanyang Technological University:

Yuk Ying Chung, the University of Sydney:

Xiaoming Chen, University of Science and Technology of China:

【3D modeling】Floor Net: A Unified Framework for Floorplan Reconstruction from 3D Scans

Chen Liu, Washington University in St. Louis:

Jiaye Wu, Washington University in St.Louis:

Yasutaka Furukawa, Simon Fraser University:

【3D modeling】Analyzing Clothing Layer Deformation Statistics of 3D Human Motions

Jinlong YANG, Inria:

Jean-Sebastien Franco, INRIA:

Franck Hétroy-Wheeler, University of Strasbourg:

Stefanie Wuhrer, Inria:

【3D modeling】Articulated Fusion: Real-time Reconstruction of Motion, Geometry and Segmentation Using a Single Depth Camera

Chao Li, The University of Texas at Dallas:

Zheheng Zhao, The University of Texas at Dallas:

Xiaohu Guo, The University of Texas at Dallas:

【3D modeling】GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction

Li Jiang, The Chinese University of Hong Kong:

Xiaojuan Qi, CUHK:

Shaoshuai SHI, The Chinese University of Hong Kong:

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

【3D modeling】Learning to Reconstruct High-quality 3D Shapes with Cascaded Fully Convolutional Networks

Yan-Pei Cao, Tsinghua University:

Zheng-Ning Liu, Tsinghua University:

Zheng-Fei Kuang, Tsinghua University:

Shi-Min Hu, Tsinghua University:

【3D modeling】3D Face Reconstruction from Light Field Images: A Model-free Approach

Mingtao Feng, Hunan Unversity:

Syed Zulqarnain Gilani, The University of Western Australia:

Yaonan Wang, Hunan University:

Ajmal Mian, University of Western Australia:

【3D modeling】Probabilistic Signed Distance Function for On-the-fly Scene Reconstruction

Wei Dong, Peking University:

Qiuyuan Wang, Peking University:

Xin Wang, Peking University:

Hongbin Zha, Peking University, China: http://www.cis.pku.edu.cn/vision/3DVCR/3DVCR_E.html

【3D modeling】3D Vehicle Trajectory Reconstruction in Monocular Video Data Using Environment Structure Constraints

Sebastian Bullinger, Fraunhofer IOSB:

Christoph Bodensteiner, Fraunhofer IOSB:

Michael Arens, Fraunhofer IOSB:

Rainer Stiefelhagen, Karlsruhe Institute of Technology:

【3D modeling】Recovering 3D Planes from a Single Image via Convolutional Neural Networks

Fengting Yang, Pennsylvania State University:

Zihan Zhou, Penn State University:

【3D modeling】Joint optimization for compressive video sensing and reconstruction under hardware constraints

Michitaka Yoshida, Kyushu University:

Akihiko Torii, Tokyo Institute of Technology, Japan:

Masatoshi Okutomi, Tokyo Institute of Technology:

Kenta Endo, Hamamatsu Photonics K. K.:

Yukinobu Sugiyama, Hamamatsu Photonics K. K.:

Hajime Nagahara, Osaka University:

【3D modeling】LAPCSR:A Deep Laplacian Pyramid Generative Adversarial Network for Scalable Compressive Sensing Reconstruction

Kai Xu, Arizona State University:

Zhikang Zhang, Arizona State University:

Fengbo Ren, Arizona State University:

【3D modeling】Extending Layered Models to 3D Motion

Dong Lao, KAUST:

Ganesh Sundaramoorthi, Kaust:

【3D modeling】Single-view Hair Reconstruction using Convolutional Network

Yi Zhou, University of Southern California:

Jun Xing, Institute for Creative Technologies, USC:

Liwen Hu, University of Southern California:

Weikai Chen, USC Institute for Creative Technology:

Hao Li, Pinscreen/University of Southern California/USC ICT: https://www.hao-li.com/Hao_Li/Hao_Li_-_about_me.html

Han-Wei Kung, University of California, Santa Barbara:

【3D modeling】Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images

Nanyang Wang, Fudan University:

Yinda Zhang, Princeton University:

Zhuwen Li, Intel Labs:

Yanwei Fu, Fudan Univ.:

Wei Liu, Tencent AI Lab: http://www.ee.columbia.edu/~wliu/

Yu-Gang Jiang, Fudan University:

【3D modeling】Learning Priors for Semantic 3D Reconstruction

Ian Cherabier, ETH Zurich:

Johannes Schoenberger, ETH Zurich:

Martin R. Oswald, ETH Zurich:

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

Andreas Geiger, MPI-IS and University of Tuebingen:

【3D modeling】Large Scale Urban Scene Modeling from MVS Meshes

Lingjie Zhu, University of Chinese Academy of Sciences:

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences:

Shuhan Shen, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences:

Zhanyi Hu, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences: http://vision.ia.ac.cn/zh/index_cn.html

【3D modeling】Efficient Dense Point Cloud Object Reconstruction using Deformation Vector Fields

Kejie Li, University of Adelaide:

Trung Pham, NVIDIA:

Huangying Zhan, The University of Adelaide:

Ian Reid, University of Adelaide, Australia: http://www.robots.ox.ac.uk/~ian/

【3D modeling】Shape Reconstruction Using Volume Sweeping and Learned Photoconsistency

Vincent Leroy, INRIA Grenoble Rhône-Alpes:

Edmond Boyer, Inria:

Jean-Sebastien Franco, INRIA:

【3D modeling】Learning Category-Specific Mesh Reconstruction from Image Collections

Angjoo Kanazawa, UC Berkeley:

Shubham Tulsiani, UC Berkeley:

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

Jitendra Malik, University of California at Berkley: http://www.cs.berkeley.edu/~malik/

【3D modeling】Super-Resolution and Sparse View CT Reconstruction

Guangming Zang, KAUST:

Ramzi Idoughi, KAUST:

Mohamed Aly, KAUST:

Peter Wonka, KAUST:

Wolfgang Heidrich, KAUST:

【3D modeling】Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization

Thomas Holzmann, Graz University of Technology:

Michael Maurer, Graz University of Technology:

Friedrich Fraundorfer, Graz University of Technology:

Horst Bischof, Graz University of Technology: http://www.icg.tugraz.at/Members/bischof

【3D modeling】Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network

Yao Feng, Shanghai Jiao Tong University:

Fan Wu, Cloud Walk Technology:

Xiao-Hu Shao, Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences:

Yan-Feng Wang, Shanghai Jiao Tong University:

Xi Zhou, Cloud Walk Technology:

【3D modeling】A Unified Framework for Single-View 3D Reconstruction with Limited Pose Supervision

Guandao Yang, Cornell University:

Yin Cui, Cornell University:

Bharath Hariharan, Cornell University:

【3D modeling】Polarimetric Three-View Geometry

Lixiong Chen, National Institute of Informatics:

Yinqiang Zheng, National Institute of Informatics: https://researchmap.jp/yinqiangzheng

Art Subpa-asa, Tokyo Institute of Technology:

Imari Sato, National Institute of Informatics:

【3D modeling】Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility

Benjamin Hepp, ETH Zurich:

Debadeepta Dey, Microsoft:

Sudipta Sinha, Microsoft Research:

Ashish Kapoor, Microsoft: http://research.microsoft.com/en-us/um/people/akapoor/index.html

Neel Joshi, -:

Otmar Hilliges, ETH Zurich:

【Feature matching】Category-Agnostic Semantic Keypoint Representations in Canonical Object Views

Xingyi Zhou, The University of Texas at Austin:

Arjun Karpur, The University of Texas at Austin:

Linjie Luo, Snap Inc:

Qixing Huang, The University of Texas at Austin:

【Feature matching】Local Spectral Graph Convolution for Point Set Feature Learning

Chu Wang, Mc Gill University:

Babak Samari, Mc Gill University:

Kaleem Siddiqi, Mc Gill University:

【Feature matching】PPF-Fold Net: Unsupervised Learning of Rotation Invariant 3D Local Descriptors

Tolga Birdal, TU Munich:

Haowen Deng, Technical University of Munich:

Slobodan Ilic, Siemens AG:

【Feature matching】Learning Local Descriptors by Integrating Geometry Constraints

Zixin Luo, HKUST:

Tianwei Shen, HKUST:

Lei Zhou, HKUST:

Siyu Zhu, HKUST:

Runze Zhang, HKUST:

Tian Fang, HKUST:

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

【Feature matching】Repeatability Is Not Enough: Learning Affine Regions via Discriminability

Dmytro Mishkin, Czech Technical University in Prague:

Filip Radenovic, Visual Recognition Group, CTU Prague:

Jiri Matas, CMP CTU FEE:

【Feature matching】Shuffle-Then-Assemble: Learning Object-Agnostic Visual Relationship Features

XU YANG, NTU:

Hanwang Zhang, Nanyang Technological University:

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

【Feature matching】Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency

Xingyi Zhou, The University of Texas at Austin:

Arjun Karpur, The University of Texas at Austin:

Chuang Gan, MIT:

Linjie Luo, Snap Inc:

Qixing Huang, The University of Texas at Austin:

【Feature matching】Shape Codes: Self-Supervised Feature Learning by Lifting Views to Viewgrids

Dinesh Jayaraman, UC Berkeley:

Ruohan Gao, University of Texas at Austin:

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

【Motion estimation】Self-Calibrating Isometric Non-Rigid Structure-from-Motion

shaifali parashar, CNRS:

Adrien Bartoli, Université Clermont Auvergne:

Daniel Pizarro, Universidad de Alcala:

【Motion estimation】Light Structure from Pin Motion: Simple and Accurate Point Light Calibration for Physics-based Modeling

Hiroaki Santo, Osaka University:

Michael Waechter, Osaka University:

Masaki Samejima, Osaka University:

Yusuke Sugano, Osaka University:

Yasuyuki Matsushita, Osaka University:

【Motion estimation】VSO: Visual Semantic Odometry

Konstantinos-Nektarios Lianos, Geomagical Labs, Inc:

Johannes Schoenberger, ETH Zurich:

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

Torsten Sattler, ETH Zurich:

【Motion estimation】Improved Structure from Motion Using Fiducial Marker Matching

Joseph De Gol, UIUC:

Timothy Bretl, University of Illinois at Urbana-Champaign:

Derek Hoiem, University of Illinois at Urbana-Champaign: http://www.cs.illinois.edu/~dhoiem/

【Motion estimation】Lambda Twist: An Accurate Fast Robust Perspective Three Point

Mikael Persson, Linköping University:

Klas Nordberg, Linköping University:

【Motion estimation】Progressive Structure from Motion

Alex Locher, ETH Zürich:

Michal Havlena, Vuforia, PTC, Vienna:

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

【Motion estimation】Self-Calibration of Cameras with Euclidean Image Plane in Case of Two Views and Known Relative Rotation Angle

Evgeniy Martyushev, South Ural State University:

【Motion estimation】Linear RGB-D SLAM for Planar Environments

Pyojin Kim, Seoul National University:

Brian Coltin, NASA Ames Research Center:

Hyoun Jin Kim, Seoul National University:

【Motion estimation】Affine Correspondences between Central Cameras for Rapid Relative Pose Estimation

Iván Eichhardt, MTA SZTAKI:

Mitya Csetverikov, MTA SZTAKI & ELTE:

【Motion estimation】View-graph Selection Framework for Sf M

Rajvi Shah, IIIT Hyderabad:

Visesh Chari, INRIA:

  1. J. Narayanan, IIIT-Hyderabad:

【Motion estimation】Buster Net: Detecting Copy-Move Image Forgery with Source/Target Localization

Rex Yue Wu, USC ISI:

Wael Abd-Almageed, Information Sciences Institute:

Prem Natarajan, USC ISI:

【Motion estimation】Direct Sparse Odometry With Rolling Shutter

David Schubert, Technical University of Munich:

Vladyslav Usenko, TU Munich:

Nikolaus Demmel, TUM:

Joerg Stueckler, Technical University of Munich:

Daniel Cremers, TUM: http://vision.in.tum.de/

【Motion estimation】Deep IM: Deep Iterative Matching for 6D Pose Estimation

Yi Li, Tsinghua University: http://users.cecs.anu.edu.au/~yili/

Gu Wang, Tsinghua University:

Xiangyang Ji, Tsinghua University:

Yu Xiang, University of Michigan:

Dieter Fox, University of Washington:

【Motion estimation】Cube Net: Equivariance to 3D Rotation and Translation

Daniel Worrall, UCL:

Gabriel Brostow, University College London:

【Motion estimation】Supervising the new with the old: learning SFM from SFM

Maria Klodt, University of Oxford:

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

【Motion estimation】ADVIO: An Authentic Dataset for Visual-Inertial Odometry

Santiago Cortes, Aalto University:

Arno Solin, Aalto University:

Esa Rahtu, Tampere University of Technology:

Juho Kannala, Aalto University, Finland:

【Motion estimation】A Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws

Gilles Simon, Université de Lorraine:

Antoine Fond, Université de Lorraine:

Marie-Odile Berger, INRIA:

【Motion estimation】BOP: Benchmark for 6D Object Pose Estimation

Tomas Hodan, Czech Technical University in Prague:

Frank Michel, Technical University Dresden:

Eric Brachmann, TU Dresden:

Wadim Kehl, Toyota Research Institute:

Anders Buch, University of Southern Denmark:

Dirk Kraft, Syddansk Universitet:

Bertram Drost, MVTec Software Gmb H:

Joel Vidal, National Taiwan University of Science and Technology:

Stephan Ihrke , Fraunhofer ivi:

Xenophon Zabulis, FORTH:

Caner Sahin, Imperial College London:

Fabian Manhardt, TU Munich:

Federico Tombari, Technical University of Munich, Germany: http://vision.deis.unibo.it/fede/

Tae-Kyun Kim, Imperial College London: https://labicvl.github.io/

Jiri Matas, CMP CTU FEE:

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

【Motion estimation】Modeling Varying Camera-IMU Time Offset in Optimization-Based Visual-Inertial Odometry

Yonggen Ling, Tencent AI Lab:

Linchao Bao, Tencent AI Lab:

Zequn Jie, Tencent AI Lab:

Fengming Zhu, Tencent AI Lab:

Ziyang Li, Tencent AI Lab:

Shanmin Tang, Tencent AI Lab:

Yong Sheng Liu, Tencent AI Lab:

Wei Liu, Tencent AI Lab: http://www.ee.columbia.edu/~wliu/

Tong Zhang, Tecent AI Lab: http://tongzhang-ml.org/

【Motion estimation】Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

Sergey Prokudin, Max Planck Institute for Intelligent Systems:

Sebastian Nowozin, Microsoft Research Cambridge: http://www.nowozin.net/sebastian/

Peter Gehler, Amazon:

【Motion estimation】Hybrid Fusion: Real-Time Performance Capture Using a Single Depth Sensor and Sparse IMUs

Zerong Zheng, Tsinghua University:

Tao Yu, Beihang University:

Hao Li, Pinscreen/University of Southern California/USC ICT: https://www.hao-li.com/Hao_Li/Hao_Li_-_about_me.html

Kaiwen Guo, Google Inc.:

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

Lu Fang, Tsinghua University: http://staff.ustc.edu.cn/~fanglu/

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

【Motion estimation】Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry

Nan Yang, Technical University of Munich:

Rui Wang, Technical University of Munich:

Joerg Stueckler, Technical University of Munich:

Daniel Cremers, TUM: http://vision.in.tum.de/

【Motion estimation】Sampling Algebraic Varieties for Robust Camera Autocalibration

Danda Pani Paudel, ETH Zürich:

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

【Motion estimation】Volumetric performance capture from minimal camera viewpoints

Andrew Gilbert, University of Surrey:

Marco Volino, University of Surrey:

John Collomosse, Adobe Research:

Adrian Hilton, University of Surrey: https://www.surrey.ac.uk/people/adrian-hilton

【Motion estimation】Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation

Markus Oberweger, TU Graz:

Mahdi Rad, TU Graz:

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

【Motion estimation】Learning to Navigate for Fine-grained Classification

Ze Yang, Peking University:

Tiange Luo, Peking University:

Dong Wang, Peking University:

Zhiqiang Hu, Peking University:

Jun Gao, Peking University:

Liwei Wang, Peking University:

【Motion estimation】Incremental Non-Rigid Structure-from-Motion with Unknown Focal Length

Thomas Probst, ETH Zurich:

Danda Pani Paudel, ETH Zürich:

Ajad Chhatkuli , ETHZ:

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

【Motion estimation】RIDI: Robust IMU Double Integration

Hang Yan, Washington University in St. Louis:

Qi Shan, Zillow Group:

Yasutaka Furukawa, Simon Fraser University:

【Motion estimation】Deep Model-Based 6D Pose Refinement in RGB

Fabian Manhardt, TU Munich:

Wadim Kehl, Toyota Research Institute:

Nassir Navab, Technische Universität München, Germany: http://campar.in.tum.de/Main/NassirNavab

Federico Tombari, Technical University of Munich, Germany: http://vision.deis.unibo.it/fede/

【Motion estimation】A Minimal Closed-Form Solution for Multi-Perspective Pose Estimation using Points and Lines

Pedro Miraldo, Instituto Superior Técnico, Lisboa:

Tiago Dias, Institute for systems and robotics:

Srikumar Ramalingam, University of Utah:

【Stereo matching】Open-World Stereo Video Matching with Deep RNN

Yiran Zhong, Australian National University:

HONGDONG LI, Australian National University, Australia:

Yuchao Dai, Northwestern Polytechnical University:

【Stereo matching】Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement

Yu Kang Gan, SUN YAT-SEN University:

Xiangyu Xu, Tsinghua University:

Wenxiu Sun, Sense Time Research:

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

【Stereo matching】Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement

Minhyeok Heo, Korea University:

Jaehan Lee, Korea University:

Kyung-Rae Kim, Korea University:

Han-Ul Kim, Korea University:

Chang-Su Kim, Korea university: http://mcl.korea.ac.kr/people/professor/

【Stereo matching】Estimating Depth from RGB and Sparse Sensing

Zhao Chen, Magic Leap, Inc.:

Vijay Badrinarayanan, Magic Leap, Inc.:

Gilad Drozdov, Magic Leap, Inc.:

Andrew Rabinovich, Magic Leap, Inc.:

【Stereo matching】Beyond local reasoning for stereo confidence estimation with deep learning

Fabio Tosi, University of Bologna:

Matteo Poggi, University of Bologna:

Antonio Benincasa, University of Bologna:

Stefano Mattoccia, University of Bologna:

【Stereo matching】Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization

Aashish Sharma, National University of Singapore:

Loong Fah Cheong, NUS:

【Stereo matching】Realtime Time Synchronized Event-based Stereo

Alex Zhu, University of Pennsylvania:

Yibo Chen, University of Pennsylvania:

Kostas Daniilidis, University of Pennsylvania:

【Stereo matching】DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Network Consistency

Yuliang Zou, Virginia Tech:

Zelun Luo, Stanford University:

Jia-Bin Huang, Virginia Tech:

【Stereo matching】Omni Depth: Dense Depth Estimation for Indoors Spherical Panoramas

NIKOLAOS ZIOULIS, CERTH / CENTRE FOR RESEARCH AND TECHNOLOGY HELLAS:

Antonis Karakottas, CERTH / CENTRE FOR RESEARCH AND TECHNOLOGY HELLAS:

Dimitrios Zarpalas, CERTH / CENTRE FOR RESEARCH AND TECHNOLOGY HELLAS:

Petros Daras, ITI-CERTH, Greece:

【Stereo matching】T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks

Chuanxia Zheng, Nanyang Technological University:

Tat-Jen Cham, Nanyang Technological University:

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

【Stereo matching】Seg Stereo: Exploiting Semantic Information for Disparity Estimation

Guorun Yang, Tsinghua University:

Hengshuang Zhao, The Chinese University of Hong Kong:

Jianping Shi, Sensetime Group Limited:

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

【Stereo matching】Active Stereo Net: End-to-End Self-Supervised Learning for Active Stereo Systems

Yinda Zhang, Princeton University:

Sean Fanello, Google:

Sameh Khamis, Google:

Christoph Rhemann, Google:

Julien Valentin, Google:

Adarsh Kowdle, Google:

Vladimir Tankovich, Google:

Shahram Izadi, Google:

Thomas Funkhouser, Princeton, USA:

【Stereo matching】PS-FCN: A Flexible Learning Framework for Photometric Stereo

Guanying Chen, The University of Hong Kong:

Kai Han, University of Oxford:

Kwan-Yee Wong, The University of Hong Kong:

【Stereo matching】MVSNet: Depth Inference for Unstructured Multi-view Stereo

Yao Yao, The Hong Kong University of Science and Technology:

Zixin Luo, HKUST:

Shiwei Li, HKUST:

Tian Fang, HKUST:

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

【Stereo matching】Plane Match: Patch Coplanarity Prediction for Robust RGB-D Registration

Yifei Shi, Princeton University:

Kai Xu, Princeton University and National University of Defense Technology:

Matthias Niessner, Technical University of Munich: http://niessnerlab.org/publications.html

Szymon Rusinkiewicz, Princeton University:

Thomas Funkhouser, Princeton, USA:

【Stereo matching】Stereo Computation for a Single Mixture Image

Yiran Zhong, Australian National University:

Yuchao Dai, Northwestern Polytechnical University:

HONGDONG LI, Australian National University, Australia:

【Stereo matching】Stereo Net: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction

Sameh Khamis, Google:

Sean Fanello, Google:

Christoph Rhemann, Google:

Julien Valentin, Google:

Adarsh Kowdle, Google:

Shahram Izadi, Google:

【Stereo matching】CNN-PS: CNN-based Photometric Stereo for General Non-Convex Surfaces

Satoshi Ikehata, National Institute of Informatics:

【Stereo matching】Self-Supervised Relative Depth Learning for Urban Scene Understanding

Huaizu Jiang, UMass Amherst:

Erik Learned-Miller, University of Massachusetts, Amherst:

Gustav Larsson, University of Chicago:

Michael Maire, Toyota Technological Institute at Chicago: http://ttic.uchicago.edu/~mmaire/

Greg Shakhnarovich, Toyota Technological Institute at Chicago:

【Stereo matching】Learning Monocular Depth by Distilling Cross-domain Stereo Networks

Xiaoyang Guo, The Chinese University of Hong Kong:

Hongsheng Li, Chinese University of Hong Kong:

Shuai Yi, The Chinese University of Hong Kong:

Jimmy Ren, Sensetime Research:

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

【Stereo matching】Eliminating the Dreaded Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery

Gregoire Payen de La Garanderie, Durham University:

Toby Breckon, Durham University:

Amir Atapour-Abarghouei, Durham University:

【Stereo matching】Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

Xinjing Cheng, Baidu:

Peng Wang, Baidu USA LLC:

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

【Stereo matching】Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images

Keisuke Tateno, Technical University Munich:

Nassir Navab, TU Munich, Germany: http://campar.in.tum.de/Main/NassirNavab

Federico Tombari, Technical University of Munich, Germany: http://vision.deis.unibo.it/fede/

【Stereo matching】Reconstruction-based Pairwise Depth Dataset for Depth Image Enhancement Using CNN

Junho Jeon, POSTECH:

Seungyong Lee, POSTECH: http://cg.postech.ac.kr/leesy/

【Stereo matching】Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss

Jianbo Jiao, City University of Hong Kong:

Ying Cao, City University of Hong Kong:

Yibing Song, Tencent AI Lab:

Rynson Lau, City University of Hong Kong:

【Optical flow】Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation

Zhaoyang Lv, GEORGIA TECH:

Kihwan Kim, NVIDIA:

Alejandro Troccoli, NVIDIA:

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

Kautz Jan, NVIDIA:

James Rehg, Georgia Institute of Technology: http://www.cc.gatech.edu/~rehg/

【Optical flow】Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow

Eddy Ilg, University of Freiburg:

Özgün Çiçek, University of Freiburg:

Silvio Galesso, University of Freiburg:

Aaron Klein, Universität Freiburg:

Osama Makansi, University of Freiburg:

Frank Hutter, University of Freiburg:

Thomas Brox, University of Freiburg: http://lmb.informatik.uni-freiburg.de/people/brox/index.en.html

【Optical flow】Structure-from-Motion-Aware Patch Match for Adaptive Optical Flow Estimation

Daniel Maurer, University of Stuttgart:

Nico Marniok, Universität Konstanz:

Bastian Goldluecke, University of Konstanz:

Andrés Bruhn, University of Stuttgart:

【Optical flow】Robust Optical Flow Estimation in Rainy Scenes

Ruoteng Li, National University of Singapore:

Robby Tan, Yale-NUS College, Singapore:

Loong Fah Cheong, NUS:

【Optical flow】Occlusions, Motion and Depth Boundaries with a Generic Network for Optical Flow, Disparity, or Scene Flow Estimation

Eddy Ilg, University of Freiburg:

Tonmoy Saikia, University of Freiburg:

Margret Keuper, University of Mannheim:

Thomas Brox, University of Freiburg: http://lmb.informatik.uni-freiburg.de/people/brox/index.en.html

【Optical flow】Conditional Prior Networks for Optical Flow

Yanchao Yang, UCLA:

Stefano Soatto, UCLA: http://vision.ucla.edu/projects.html

【Optical flow】Unsupervised Learning of Multi-Frame Optical Flow with Occlusions

Joel Janai, Max Planck Institute for Intelligent Systems:

Fatma Güney, University of Oxford:

Anurag Ranjan, MPI for Intelligent Systems:

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

Andreas Geiger, MPI-IS and University of Tuebingen:

【Region matching】Semantic Match Consistency for Long-Term Visual Localization

Carl Toft, Chalmers:

Erik Stenborg, Chalmers University:

Lars Hammarstrand, Chalmers university of technology:

Lucas Brynte, Chalmers University of Technology:

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

Torsten Sattler, ETH Zurich:

Fredrik Kahl, Chalmers:

【Region matching】Attentive Semantic Alignment with Offset-Aware Correlation Kernels

Paul Hongsuck Seo, POSTECH:

Jongmin Lee, POSTECH:

Deunsol Jung, POSTECH:

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

Minsu Cho, POSTECH:

【Region matching】PARN: Pyramidal Affine Regression Networks for Dense Semantic Correspondence Estimation

Sangryul Jeon, Yonsei university:

Seungryung Kim, Yonsei University:

Dongbo Min, Ewha Womans University:

Kwanghoon Sohn , Yonsei Univ.:

【Region matching】Multimodal image alignment through a multiscale chain of neural networks with application to remote sensing

Armand Zampieri, Inria Sophia-Antipolis:

Guillaume Charpiat, INRIA:

Nicolas Girard, Inria Sophia-Antipolis:

Yuliya Tarabalka, Inria Sophia-Antipolis: http://www-sop.inria.fr/members/Yuliya.Tarabalka/

【Region matching】Incremental Multi-graph Matching via Diversity and Randomness based Graph Clustering

Tianshu Yu, Arizona State University:

Junchi Yan, Shanghai Jiao Tong University:

baoxin Li, Arizona State University:

Wei Liu, Tencent AI Lab: http://www.ee.columbia.edu/~wliu/

【Region matching】Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences

Mohammed Fathy, University of Maryland College Park:

Quoc-Huy Tran, NEC Labs:

Zeeshan Zia, Microsoft:

Paul Vernaza, NEC Labs America:

Manmohan Chandraker, NEC Labs America: http://cseweb.ucsd.edu/~mkchandraker/

【Region matching】Adaptively Transforming Graph Matching

Fudong Wang, Wuhan University:

Nan Xue, Wuhan University:

yi-peng Zhang, Syracuse University:

Xiang Bai, Huazhong University of Science and Technology: http://cloud.eic.hust.edu.cn:8071/~xbai/

Gui-Song Xia, Wuhan University:

【Image editing】Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias

Rameswar Panda, UC Riverside:

Jianming Zhang, Adobe Research:

Haoxiang Li, Adobe:

Joon-Young Lee, Adobe Research:

Xin Lu, Adobe:

Amit Roy-Chowdhury , University of California, Riverside, USA:

【Image editing】Fast and Accurate Intrinsic Symmetry Detection

Rajendra Nagar, Indian Institute of Technology Gandhinagar:

Shanmuganathan Raman, IIT Gandhinagar:

【Image editing】A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation

Hieu Le, Stony Brook University:

Tomas F Yago Vicente, Stony Brook University:

Vu Nguyen, Stony Brook University:

Minh Hoai Nguyen, Stony Brook University: http://www3.cs.stonybrook.edu/~minhhoai/

Dimitris Samaras, Stony Brook University: http://www3.cs.stonybrook.edu/~samaras/

【Image editing】Robust image stitching using multiple registrations

Charles Herrmann, Cornell:

Chen Wang, Google Research:

Richard Bowen, Cornell:

Mike Krainin, Google:

Ce Liu, Google: http://people.csail.mit.edu/celiu/

Bill Freeman, MIT: https://billf.mit.edu/

Ramin Zabih, Cornell Tech/Google Research: http://www.cs.cornell.edu/~rdz/

【Image editing】Contextual Based Image Inpainting: Infer, Match and Translate

Yuhang Song, USC:

Chao Yang, University of Southern California:

Zhe Lin, Adobe Research: http://www.adobe.com/technology/people/san-jose/zhe-lin.html

Xiaofeng Liu, Carnegie Mellon University:

Hao Li, Pinscreen/University of Southern California/USC ICT: https://www.hao-li.com/Hao_Li/Hao_Li_-_about_me.html

Qin Huang, University of Southern California:

C.-C. Jay Kuo, USC:

【Image editing】Learning Compression from limited unlabeled Data

Xiangyu He, Chinese Academy of Sciences:

Jian Cheng, Chinese Academy of Sciences, China: http://www.nlpr.ia.ac.cn/jcheng/

【Image editing】Object-centered image stitching

Charles Herrmann, Cornell:

Chen Wang, Google Research:

Richard Bowen, Cornell:

Ramin Zabih, Cornell Tech/Google Research: http://www.cs.cornell.edu/~rdz/

【Image editing】Task-Aware Image Downscaling

Heewon Kim, Seoul National University:

Myungsub Choi, Seoul National University:

Bee Lim, Seoul National University:

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

【Image editing】Deep Texture and Structure Aware Filtering Network for Image Smoothing

Kaiyue Lu, Australian National University & Data61-CSIRO:

Shaodi You, Data61-CSIRO, Australia:

Nick Barnes, CSIRO(Data61):

【Image editing】Bi-directional Feature Pyramid Network with Recursive Attention Residual Modules For Shadow Detection

Lei Zhu, The Chinese University of Hong Kong:

Zijun Deng, South China University of Technology:

Xiaowei Hu, The Chinese University of Hong Kong:

Chi-Wing Fu, The Chinese University of Hong Kong:

Xuemiao Xu, South China University of Technology:

Jing Qin, The Hong Kong Polytechnic University:

Pheng-Ann Heng, The Chinese Univsersity of Hong Kong: http://www.cse.cuhk.edu.hk/~pheng/

【Image editing】Single Image Water Hazard Detection using FCN with Reflection Attention Units

Xiaofeng Han, Nanjing University of Science and Technology:

Chuong Nguyen, CSIRO Data61:

Shaodi You, Data61-CSIRO, Australia:

Jianfeng Lu, Nanjing University of Science and Technology:

【Image editing】Double JPEG Detection in Mixed JPEG Quality Factors using Deep Convolutional Neural Network

Jin-Seok Park, Korea Advanced Institute of Science and Technology (KAIST):

Donghyeon Cho, KAIST:

Wonhyuk Ahn, KAIST:

Heung-Kyu Lee, Korea Advanced Institute of Science and Technology (KAIST):

【Image editing】Selfie Video Stabilization

Jiyang Yu, University of California San Diego:

Ravi Ramamoorthi, University of California San Diego:

【Image editing】A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising

XU JUN, The Hong Kong Polytechnic University:

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

  1. Zhang, The Hong Kong Polytechnic University:

【Image editing】Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Yulun Zhang, Northeastern University:

Kunpeng Li, Northeastern University:

kai li, northeastern university:

Lichen Wang, Northeastern University:

Bineng Zhong, Huaqiao University:

YUN FU, Northeastern University: http://www1.ece.neu.edu/~yunfu/

【Image editing】Multi-scale Residual Network for Image Super-Resolution

Juncheng Li, East China Normal University:

Faming Fang, East China Normal University:

Kangfu Mei, Jiangxi Normal University:

Guixu Zhang, East China Normal University:

【Image editing】Fish Eye Rec Net: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification

Xiaoqing Yin, University of Sydney:

Xinchao Wang, Stevens Institute of Technology:

Jun Yu, HDU:

Maojun Zhang, National University of Defense Technology, China:

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

Dacheng Tao, University of Sydney:

【Image editing】Image Inpainting for Irregular Holes Using Partial Convolutions

Guilin Liu, NVIDIA:

Fitsum Reda, NVIDIA:

Kevin Shih, NVIDIA:

Ting-Chun Wang, NVIDIA:

Andrew Tao, NVIDIA:

Bryan Catanzaro, NVIDIA:

【Computational photography】Graph R-CNN for Scene Graph Generation

Jianwei Yang, Georgia Institute of Technology:

Jiasen Lu, Georgia Institute of Technology:

Stefan Lee, Georgia Institute of Technology:

Dhruv Batra, Georgia Tech & Facebook AI Research:

Devi Parikh, Georgia Tech & Facebook AI Research: https://filebox.ece.vt.edu/~parikh/

【Computational photography】Deep Recursive HDRI: Inverse Tone Mapping using Generative Adversarial Networks

Siyeong Lee, Sogang University:

Gwon Hwan An, Sogang University:

Suk-Ju Kang, Nil:

【Computational photography】Deep Cross-Modal Projection Learning for Image-Text Matching

Ying Zhang, Dalian University of Technology:

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

【Computational photography】Deep Video Generation, Prediction and Completion of Human Action Sequences

Chunyan Bai, Hong Kong University of Science and Technology:

Haoye Cai, Hong Kong University of Science and Technology:

Yu-Wing Tai, Tencent You Tu:

Chi-Keung Tang, Hong Kong University of Science and Technology:

【Computational photography】Shape Stacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking

Oliver Groth, Oxford Robotics Insitute:

Fabian Fuchs, Oxford Robotics Insitute:

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

Ingmar Posner, Oxford:

【Computational photography】Learning to Reenact Faces via Boundary Transfer

Wayne Wu, Sense Time Research:

Yunxuan Zhang, sensetime research:

Cheng Li, Sense Time Research:

Chen Qian, Sense Time:

Chen Change Loy, Chinese University of Hong Kong: http://www.eecs.qmul.ac.uk/~ccloy/

【Computational photography】Deep High Dynamic Range Imaging with Large Foreground Motions

Shangzhe Wu, HKUST:

Jiarui Xu, Hong Kong University of Science and Technology (HKUST):

Yu-Wing Tai, Tencent You Tu:

Chi-Keung Tang, Hong Kong University of Science and Technology:

【Computational photography】End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN

Yunlong Wang, Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation, Chinese Academy of Sciences (CASIA):

Fei Liu, Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation, Chinese Academy of Sciences (CASIA):

Zilei Wang, University of Science and Technology of China:

Guangqi Hou, Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation, Chinese Academy of Sciences (CASIA):

Zhenan Sun, Chinese of Academy of Sciences:

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

【Computational photography】A Hybrid Model for Identity Obfuscation by Face Replacement

Qianru Sun, National University of Singapore:

Ayush Tewari, Max Planck Institute for Informatics:

Weipeng Xu, MPII:

Mario Fritz, Max-Planck-Institut für Informatik: https://scalable.mpi-inf.mpg.de/

Christian Theobalt, MPI Informatik:

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

【Computational photography】Deblurring Natural Image Using Super-Gaussian Fields

Yuhang Liu, Wuhan University:

Wenyong Dong, Wuhan University:

Dong Gong, Northwestern Polytechnical University & The University of Adelaide:

Lei Zhang, The unversity of Adelaide: http://www4.comp.polyu.edu.hk/~cslzhang/

Qinfeng Shi, University of Adelaide: https://cs.adelaide.edu.au/~javen/

【Computational photography】Diverse and Coherent Paragraph Generation from Images

Moitreya Chatterjee, University of Illinois at Urbana Champaign:

Alexander Schwing, UIUC: http://www.alexander-schwing.de/

【Computational photography】The Sound of Pixels

Hang Zhao, Massachusetts Institute of Technology:

Chuang Gan, MIT:

Andrew Rouditchenko, MIT:

Carl Vondrick, MIT:

Josh Mc Dermott, Massachusetts Institute of Technology:

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

【Computational photography】Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation

Chao Wang, Ocean University of China:

Haiyong Zheng, Ocean University of China:

Zhibin Yu, Ocean University of China:

Ziqiang Zheng, Ocean University of China:

Zhaorui Gu, Ocean University of China:

Bing Zheng, Ocean University of China:

【Computational photography】Diverse Image-to-Image Translation via Disentangled Representations

Hsin-Ying Lee, University of California, Merced:

Hung-Yu Tseng, University of California, Merced:

Maneesh Singh, Verisk Analytics:

Jia-Bin Huang, Virginia Tech:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

【Computational photography】Learning-based Video Motion Magnification

Tae-Hyun Oh, MIT CSAIL:

Ronnachai Jaroensri, MIT CSAIL:

Changil Kim, MIT CSAIL:

Mohamed A. Elghareb, Qatar Computing Research Institute:

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

Bill Freeman, MIT: https://billf.mit.edu/

Wojciech Matusik, MIT CSAIL:

【Computational photography】Coded Two-Bucket Cameras for Computer Vision

Mian Wei, University of Toronto:

Navid Navid Sarhangnejad, University of Toronto:

Zhengfan Xia, University of Toronto:

Nikola Katic, University of Toronto:

Roman Genov, University of Toronto:

Kyros Kutulakos, University of Toronto:

【Computational photography】Multimodal Unsupervised Image-to-image Translation

Xun Huang, Cornell University:

Ming-Yu Liu, NVIDIA:

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

Kautz Jan, NVIDIA:

【Computational photography】CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering

Zhengqi Li, Cornell University:

Noah Snavely, -: http://www.cs.cornell.edu/~snavely/

【Computational photography】A Closed-form Solution to Photorealistic Image Stylization

Yijun Li, University of California, Merced:

Ming-Yu Liu, NVIDIA:

Xueting Li, University of California, Merced:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

Kautz Jan, NVIDIA:

【Computational photography】Single Image Highlight Removal with a Sparse and Low-Rank Reflection Model

Jie Guo, Nanjing University:

Zuojian Zhou, Nanjing University Of Chinese Medicine:

Limin Wang, Nanjing University: http://wanglimin.github.io/

【Computational photography】Stacked Cross Attention for Image-Text Matching

Kuang-Huei Lee, Microsoft AI and Research:

Xi Chen, Microsoft AI and Research:

Gang Hua, Microsoft Cloud and AI: http://www.cs.stevens.edu/~ghua/

Houdong Hu, Microsoft AI and Research:

Xiaodong He, JD AI Research:

【Computational photography】Real-Time Hair Rendering using Sequential Adversarial Networks

Lingyu Wei, University of Southern California:

Liwen Hu, University of Southern California:

Vladimir Kim, Adobe Research:

Ersin Yumer, Argo AI:

Hao Li, Pinscreen/University of Southern California/USC ICT: https://www.hao-li.com/Hao_Li/Hao_Li_-_about_me.html

【Computational photography】Spatio-temporal Transformer Network for Video Restoration

Tae Hyun Kim, Max Planck Institute for Intelligent Systems:

Mehdi S. M. Sajjadi, Max Planck Institute for Intelligent Systems:

Michael Hirsch, Max Planck Institut for Intelligent Systems:

Bernhard Schölkopf, Max Planck Institute for Intelligent Systems:

【Computational photography】Programmable Light Curtains

Jian Wang, Carnegie Mellon University:

Joe Bartels, Carnegie Mellon University:

William Whittaker, Carnegie Mellon University:

Aswin Sankaranarayanan, Carnegie Mellon University:

Srinivasa Narasimhan, Carnegie Mellon University: http://www.cs.cmu.edu/~srinivas/

【Computational photography】Single Image Scene Refocusing using Conditional Adversarial Networks

Parikshit Sakurikar, IIIT-Hyderabad:

Ishit Mehta, IIIT Hyderabad:

Vineeth N Balasubramanian, IIT Hyderabad:

  1. J. Narayanan, IIIT-Hyderabad:

【Computational photography】Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Image

Zhengqin Li, UC San Diego:

Manmohan Chandraker, UC San Diego: http://cseweb.ucsd.edu/~mkchandraker/

Sunkavalli Kalyan, Adobe Research:

【Computational photography】Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal

Jie Yang, University of Adelaide:

Dong Gong, Northwestern Polytechnical University & The University of Adelaide:

Lingqiao Liu, University of Adelaide:

Qinfeng Shi, University of Adelaide: https://cs.adelaide.edu.au/~javen/

【Computational photography】Multi-view to Novel view: Synthesizing Views via Self-Learned Confidence

Shao-Hua Sun, University of Southern California:

Jacob Huh, Carnegie Mellon University:

Yuan-Hong Liao, National Tsing Hua University:

Ning Zhang, Snap Chat:

Joseph Lim, USC:

【Computational photography】Joint Camera Spectral Sensitivity Selection and Hyperspectral Image Recovery

Ying Fu, Beijing Institute of Technology:

Tao Zhang, Beijing Institute of Technology:

Yinqiang Zheng, National Institute of Informatics: https://researchmap.jp/yinqiangzheng

debing zhang, Deep Glint:

Hua Huang, Beijing Institute of Technology:

【Computational photography】Scaling Egocentric Vision: The E-Kitchens Dataset

Dima Damen, University of Bristol:

Hazel Doughty, University of Bristol:

Sanja Fidler, University of Toronto:

Antonino Furnari, University of Catania:

Evangelos Kazakos, University of Bristol:

Giovanni Farinella, University of Catania, Italy:

Davide Moltisanti, University of Bristol:

Jonathan Munro, University of Bristol:

Toby Perrett, University of Bristol:

Will Price, University of Bristol:

Michael Wray, University of Bristol:

【Computational photography】Neural Stereoscopic Image Style Transfer

Xinyu Gong, University of Electronic Science and Technology of China:

Haozhi Huang, Tencent AI Lab:

Lin Ma, Tencent AI Lab:

Fumin Shen, UESTC:

Wei Liu, Tencent AI Lab: http://www.ee.columbia.edu/~wliu/

Tong Zhang, Tecent AI Lab: http://tongzhang-ml.org/

【Computational photography】Joint Learning of Intrinsic Images and Semantic Segmentation

Anil Baslamisli, University of Amsterdam:

Thomas Tiel Groenestege, University of Amsterdam:

Partha Das, University of Amsterdam:

Hoang-An Le, University of Amsterdam:

Sezer Karaoglu, University of Amsterdam:

Theo Gevers, University of Amsterdam: https://staff.science.uva.nl/th.gevers/

【Computational photography】Visual Reasoning with a Multi-hop Fi LM Generator

Florian Strub, University of Lille:

Mathieu Seurin, University of Lille:

Ethan Perez, Rice University:

Harm De Vries, Montreal Institute for Learning Algorithms:

Jeremie Mary, Criteo:

Philippe Preux, INRIA:

Aaron Courville, MILA, Université de Montréal:

Olivier Pietquin, Google Brain: http://www.lifl.fr/~pietquin/

【Computational photography】Space-time Knowledge for Unpaired Image-to-Image Translation

Aayush Bansal, Carnegie Mellon University:

Shugao Ma, Facebook / Occulus:

Deva Ramanan, Carnegie Mellon University: http://www.ics.uci.edu/~dramanan/

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

【Computational photography】Estimating the Success of Unsupervised Image to Image Translation

Lior Wolf, Tel Aviv University, Israel: http://www.cs.tau.ac.il/~wolf/

Sagie Benaim, Tel Aviv University:

Tomer Galanti, Tel Aviv University:

【Computational photography】Joint Map and Symmetry Synchronization

Qixing Huang, The University of Texas at Austin:

Xiangru Huang, University of Texas at Austin:

Zhenxiao Liang, Tsinghua University:

Yifan Sun, The University of Texas at Austin:

【Computational photography】Transferring GANs: generating images from limited data

yaxing wang, Computer Vision Center:

Chenshen Wu, Computer Vision Center:

Luis Herranz, Computer Vision Center (Ph.D.):

Joost van de Weijer, Computer Vision Center:

Abel Gonzalez-Garcia, Computer Vision Center:

BOGDAN RADUCANU, Computer Version Center, Edifici:

【Computational photography】To learn image super-resolution, use a GAN to learn how to do image degradation first

Adrian Bulat, University of Nottingham:

Jing Yang, University of Nottingham:

Georgios Tzimiropoulos, University of Nottingham: http://www.cs.nott.ac.uk/~pszyt/

【Computational photography】Structural Consistency and Controllability for Diverse Colorization

Safa Messaoud, University of Illinois at Urbana Champaign:

Alexander Schwing, UIUC: http://www.alexander-schwing.de/

David Forsyth, Univeristy of Illinois at Urbana-Champaign: http://luthuli.cs.uiuc.edu/~daf/

【Computational photography】Image Manipulation with Perceptual Discriminators

Diana Sungatullina, Skolkovo Institute of Science and Technology:

Egor Zakharov, Skolkovo Institute of Science and Technology:

Dmitry Ulyanov, Skolkovo Institute of Science and Technology:

Victor Lempitsky, Skoltech:

【Computational photography】Snap Angle Prediction for 360$^{\circ}$ Panoramas

Bo Xiong, University of Texas at Austin:

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

【Computational photography】Unsupervised holistic image generation from key local patches

Donghoon Lee, Seoul National University:

Sangdoo Yun, Clova AI Research, NAVER Corp.:

Sungjoon Choi, Seoul National University:

Hwiyeon Yoo, Seoul National University:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

Songhwai Oh, Seoul National University:

【Computational photography】Cross Net: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping

Haitian Zheng, HKUST:

Mengqi Ji, HKUST:

Haoqian Wang, Tsinghua University:

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

Lu Fang, Tsinghua University: http://staff.ustc.edu.cn/~fanglu/

【Computational photography】Image Reassembly Combining Deep Learning and Shortest Path Problem

Marie-Morgane Paumard, ETIS:

David Picard, ETIS/LIP6:

Hedi Tabia, France: https://perso-etis.ensea.fr/tabia/

【Computational photography】Probabilistic Video Generation using Holistic Attribute Control

Jiawei He, Simon Fraser University:

Andreas Lehrmann, Facebook:

Joe Marino, California Institute of Technology:

Greg Mori, Simon Fraser University: http://www.cs.sfu.ca/~mori/

Leonid Sigal, University of British Columbia: https://www.cs.ubc.ca/~lsigal/

【Computational photography】3D Motion Sensing from 4D Light Field Gradients

Sizhuo Ma, University of Wisconsin-Madison:

Brandon Smith, University of Wisconsin-Madison:

Mohit Gupta, University of Wisconsin-Madison, USA: http://wisionlab.cs.wisc.edu/people/mohit-gupta/

【Computational photography】Learning to Blend Photos

Wei-Chih Hung, University of California, Merced:

Jianming Zhang, Adobe Research:

Xiaohui Shen, Adobe Research:

Zhe Lin, Adobe Research: http://www.adobe.com/technology/people/san-jose/zhe-lin.html

Joon-Young Lee, Adobe Research:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

【Computational photography】Layer-structured 3D Scene Inference via View Synthesis

Shubham Tulsiani, UC Berkeley:

Richard Tucker, Google:

Noah Snavely: http://www.cs.cornell.edu/~snavely/

【Computational photography】Proximal Dehaze-Net: A Prior Learning-Based Deep Network for Single Image Dehazing

Dong Yang, Xi’an Jiaotong University:

JIAN SUN, Xi’an Jiaotong University:

【Computational photography】Learning to Capture Light Fields through A Coded Aperture Camera

Yasutaka Inagaki, Nagoya University:

Yuto Kobayashi, Nagoya University:

Keita Takahashi, Nagoya University:

Toshiaki Fujii, Nagoya University:

Hajime Nagahara, Osaka University:

【Computational photography】Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks

Miika Aittala, MIT:

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

【Computational photography】Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining

Xia Li, Peking University Shenzhen Graduate School:

Jianlong Wu, Peking University:

Zhouchen Lin, Peking University: http://www.cis.pku.edu.cn/faculty/vision/zlin/zlin.htm

Hong Liu, Peking University Shenzhen Graduate School:

Hongbin Zha, Peking University, China: http://www.cis.pku.edu.cn/vision/3DVCR/3DVCR_E.html

【Computational photography】Imagine This! Scripts to Compositions to Videos

Tanmay Gupta, UIUC:

Dustin Schwenk, Allen Institute for Artificial Intelligence:

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

Derek Hoiem, University of Illinois at Urbana-Champaign: http://www.cs.illinois.edu/~dhoiem/

Aniruddha Kembhavi, Allen Institute for Artificial Intelligence:

【Computational photography】A Style-aware Content Loss for Real-time HD Style Transfer

Artsiom Sanakoyeu, Heidelberg University:

Dmytro Kotovenko, Heidelberg University:

Bjorn Ommer, Heidelberg University:

【Computational photography】Scale-Awareness of Light Field Camera based Visual Odometry

Niclas Zeller, Karlsruhe University of Applied Sciences:

Franz Quint, Karlsruhe University of Applied Sciences:

Uwe Stilla, Technische Universitaet Muenchen:

【Computational photography】Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks

Minjun Li, Fudan University:

Haozhi Huang, Tencent AI Lab:

Lin Ma, Tencent AI Lab:

Wei Liu, Tencent AI Lab: http://www.ee.columbia.edu/~wliu/

Tong Zhang, Tecent AI Lab: http://tongzhang-ml.org/

Yu-Gang Jiang, Fudan University:

【Computational photography】Rendering Portraitures from Monocular Camera and Beyond

Xiangyu Xu, Tsinghua University:

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

Sifei Liu, NVIDIA:

Wenqi Ren, Institute of Information Engineering, Chinese Academy of Sciences:

Yu-Jin Zhang, Tsinghua University:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

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

【Computational photography】Unsupervised Class-Specific Deblurring

Nimisha T M, Indian Institute of Technology Madras:

Sunil Kumar, Indian Institute of Technology Madras:

Rajagopalan Ambasamudram, Indian Institute of Technology Madras:

【Computational photography】Fast, Accurate, and, Lightweight Super-Resolution with Cascading Residual Network

Namhyuk Ahn, Ajou University:

Byungkon Kang, Ajou University:

Kyung-Ah Sohn, Ajou University:

【Computational photography】A Dataset of Flash and Ambient Illumination Pairs from the Crowd

Yagiz Aksoy, ETH Zurich:

Changil Kim, MIT CSAIL:

Petr Kellnhofer, MIT:

Sylvain Paris, Adobe Research: http://people.csail.mit.edu/sparis/

Mohamed A. Elghareb, Qatar Computing Research Institute:

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

Wojciech Matusik, MIT:

【Computational photography】Faces as Lighting Probes via Unsupervised Deep Highlight Extraction

Renjiao Yi, Simon Fraser University:

Chenyang Zhu, Simon Fraser University:

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

Stephen Lin, Microsoft Research:

【Computational photography】Super-Identity Convolutional Neural Network for Face Hallucination

Kaipeng Zhang, National Taiwan University:

ZHANPENG ZHANG, Sense Time Group Limited:

Chia-Wen Cheng, UT Austin:

Winston Hsu, National Taiwan University:

Yu Qiao, Multimedia Laboratory, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences: http://mmlab.siat.ac.cn/yuqiao/

Wei Liu, Tencent AI Lab: http://www.ee.columbia.edu/~wliu/

Tong Zhang, Tecent AI Lab: http://tongzhang-ml.org/

【Computational photography】Interpretable Intuitive Physics Model

Tian Ye, Carnegie Mellon University:

Xiaolong Wang, CMU:

James Davidson, Google:

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

【Computational photography】Variable Ring Light Imaging: Capturing Transient Subsurface Scattering with An Ordinary Camera

Ko Nishino, Kyoto University:

Art Subpa-asa, Tokyo Institute of Technology:

Yuta Asano, Tokyo Institute of Technology:

Mihoko Shimano, National Institute of Informatics:

Imari Sato, National Institute of Informatics:

【Computational photography】Text2Colors: Guiding Image Colorization through Text-Driven Palette Generation

Wonwoong Cho, Korea University:

Hyojin Bahng, Korea University:

David Park, Korea University:

Seungjoo Yoo, Korea University:

Ziming Wu, Hong Kong University of Science and Technology:

Xiaojuan MA, Hong Kong University of Science and Technology:

Jaegul Choo, Korea University: http://davian.korea.ac.kr/

【Computational photography】Learning Data Terms for Image Deblurring

Jiangxin Dong, Dalian University of Technology:

Jinshan Pan, Dalian University of Technology:

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

Zhixun Su, Dalian University of Technology:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

【Computational photography】Deep Boosting for Image Denoising

Chang Chen, University of Science and Technology of China:

Zhiwei Xiong, University of Science and Technology of China:

Xinmei Tian, USTC:

Feng Wu, University of Science and Technology of China:

【Computational photography】End-to-End Deep Structured Models for Drawing Crosswalks

Justin Liang, Uber ATG:

Raquel Urtasun, Uber ATG: http://www.cs.toronto.edu/~urtasun/

【Computational photography】Lifting Layers: Analysis and Applications

Michael Moeller, University of Siegen:

Peter Ochs, Saarland University:

Tim Meinhardt, Technical University of Munich:

Laura Leal-Taixé, TUM:

【Computational photography】DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs

Shi Yan, Tsinghua University:

Chenglei Wu, Oculus Research:

Lizheng Wang, Tsinghua University:

Liang An, Tsinghua University:

Feng Xu, Tsinghua University:

Kaiwen Guo, Google Inc.:

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

【Computational photography】Liquid Pouring Monitoring via Rich Sensory Inputs

Tz-Ying Wu, National Tsing Hua University:

Juan-Ting Lin, National Tsing Hua University:

Tsun-Hsuang Wang, National Tsing Hua University:

Chan-Wei Hu, National Tsing Hua University:

Juan Carlos Niebles, Stanford University: http://www.niebles.net/

Min Sun, NTHU: http://aliensunmin.github.io/

【Computational photography】Physical Primitive Decomposition

Zhijian Liu, Shanghai Jiao Tong University:

Jiajun Wu, MIT:

Bill Freeman, MIT: https://billf.mit.edu/

Joshua Tenenbaum, MIT:

【Computational photography】Swap Net: Garment Transfer in Single View Images

Amit Raj, Georgia Institute of Technology:

Patsorn Sangkloy, Georgia Institute of Technology:

Huiwen Chang, Princeton University:

Jingwan Lu, Adobe Research:

Duygu Ceylan, Adobe Research:

James Hays, Georgia Institute of Technology, USA: http://www.cs.brown.edu/~hays/

【Computational photography】Separating Reflection and Transmission Images in the Wild

Patrick Wieschollek, University of Tuebingen:

Orazio Gallo, NVIDIA Research:

Jinwei Gu, Nvidia:

Kautz Jan, NVIDIA:

【Computational photography】SRFeat: Single Image Super Resolution with Feature Discrimination

Seong-Jin Park, POSTECH:

Hyeongseok Son, POSTECH:

Sunghyun Cho, DGIST:

Ki-Sang Hong, POSTECH:

Seungyong Lee, POSTECH: http://cg.postech.ac.kr/leesy/

【Computational photography】Image Generation from Sketch Constraint Using Contextual GAN

Yongyi Lu, HKUST:

Shangzhe Wu, HKUST:

Yu-Wing Tai, Tencent You Tu:

Chi-Keung Tang, Hong Kong University of Science and Technology:

【Computational photography】Towards Realistic Predictors

Pei Wang, UC San Diego:

Nuno Vasconcelos, UC San Diego: http://www.svcl.ucsd.edu/

【Computational photography】Visual Text Correction

Amir Mazaheri, University of Central Florida:

Mubarak Shah, University of Central Florida: http://crcv.ucf.edu/people/faculty/shah.html

【Computational photography】X-ray Computed Tomography Through Scatter

Adam Geva, Technion:

Yoav Y. Schechner, Technion:

Jonathan Chernyak, Technion:

Rajiv Gupta, MGH Harvard:

【Computational photography】Shift-Net: Image Inpainting via Deep Feature Rearrangement

Zhaoyi Yan, Harbin Institute of Technology:

Xiaoming Li, Harbin Institute of Technology:

Mu LI, The Hong Kong Polytechnic University:

Wangmeng Zuo, Harbin Institute of Technology, China:

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

【Computational photography】Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks

Filippos Kokkinos, Skolkovo Institute of Science and Technology:

Stamatis Lefkimmiatis, Skolkovo Institute of Science and Technology:

【Computational photography】Single Image Intrinsic Decomposition Without a Single Intrinsic Image

Wei-Chiu Ma, MIT:

Hang Chu, University of Toronto:

Bolei Zhou, MIT:

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

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

【Computational photography】Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields

Yongcheng Jing, Zhejiang University:

Yang Liu, Zhejiang University:

Yezhou Yang, Arizona State University: http://www.umiacs.umd.edu/~yzyang/

Zunlei Feng, Zhejiang University:

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

Dacheng Tao, University of Sydney:

Mingli Song, Zhejiang University:

【Computational photography】The Contextual Loss for Image Transformation with Non-Aligned Data

Roey Mechrez, Technion:

Itamar Talmi, Technion:

Lihi Zelnik-Manor, Technion: http://lihi.eew.technion.ac.il/

【Computational photography】Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes

Yang He, MPI Informatics:

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

Mario Fritz, Max-Planck-Institut für Informatik: https://scalable.mpi-inf.mpg.de/

【Computational photography】Deep Burst Denoising

Clement Godard, University College London:

Kevin Matzen, Facebook:

Matt Uyttendaele, Facebook:

【Computational photography】Exploiting Vector Fields for Geometric Rectification of Distorted Document Images

Gaofeng Meng, Chinese Academy of Sciences: http://www.escience.cn/people/menggaofeng/index.html

Yuanqi Su, Xi’an Jiaotong University:

Ying Wu, Northwestern University:

SHIMING XIANG, Chinese Academy of Sciences, China:

Chunhong Pan, Institute of Automation, Chinese Academy of Sciences: http://people.gucas.ac.cn/~panchunhong

【Computational photography】Joint Blind Motion Deblurring and Depth Estimation of Light Field

Dongwoo Lee, Seoul Ntional University:

Haesol Park, Seoul National University:

In Kyu Park, Inha University:

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

【Computational photography】A Geometric Perspective on Structured Light Coding

Mohit Gupta, University of Wisconsin-Madison, USA: http://wisionlab.cs.wisc.edu/people/mohit-gupta/

Nikhil Nakhate, University of Wisconsin-Madison:

【Computational photography】Learning to Forecast and Refine Residual Motion for Image-to-Video Generation

Long Zhao, Rutgers University:

Xi Peng, Rutgers University:

Yu Tian, Rutgers:

Mubbasir Kapadia, Rutgers:

Dimitris Metaxas, Rutgers:

【Computational photography】Revisiting Autofocus for Smartphone Cameras

Abdullah Abuolaim, York University:

Abhijith Punnappurath, York University:

Michael Brown, York University: http://www.comp.nus.edu.sg/~brown/

【Machine learning】Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation

Zhengming Ding, Northeastern University:

Sheng Li, Adobe Research:

Ming Shao, University of Massachusetts Dartmouth:

YUN FU, Northeastern University: http://www1.ece.neu.edu/~yunfu/

【Machine learning】Inner Space Preserving Generative Pose Machine

Shuangjun Liu, Northeastern University:

Sarah Ostadabbas, Northeastern University:

【Machine learning】Fictitious GAN: Training GANs with Historical Models

Yin Xia, Northwestern University:

Xu Chen, Northwestern University:

Hao Ge, Northwestern University:

Ying Wu, Northwestern University:

Randall Berry, Northwestern University:

【Machine learning】Rolling Shutter Pose and Ego-motion Estimation using Shape-from-Template

Yizhen Lao, Université Clermont Auvergne:

Omar Ait-Aider, Université Clermont Auvergne:

Adrien Bartoli, Université Clermont Auvergne:

【Machine learning】Local Orthogonal-Group Testing

Ahmet Iscen, Czech Technical University:

Ondrej Chum, Vision Recognition Group, Czech Technical University in Prague: http://cmp.felk.cvut.cz/~chum/

【Machine learning】Attention-based Ensemble for Deep Metric Learning

Wonsik Kim, Samsung Electronics:

Bhavya Goyal, Samsung Electronics:

Kunal Chawla, Samsung Electronics:

Jungmin Lee, Samsung Electronics:

Keunjoo Kwon, Samsung Electronics:

【Machine learning】TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights

Diwen Wan, University of Electronic Science and Technology of China:

Fumin Shen, UESTC:

Li Liu, the inception institute of artificial intelligence:

Fan Zhu, the inception institute of artificial intelligence:

Jie Qin, ETH Zurich:

Ling Shao, Inception Institute of Artificial Intelligence: http://lshao.staff.shef.ac.uk/

Heng Tao Shen, University of Electronic Science and Technology of China (UESTC):

【Machine learning】Deep Fundamental Matrix Estimation

Rene Ranftl, Intel Labs:

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

【Machine learning】Exploring the Limits of Supervised Pretraining

Dhruv Mahajan, Facebook:

Ross Girshick, Facebook AI Research (FAIR): http://www.cs.berkeley.edu/~rbg/

Vignesh Ramanathan, Facebook:

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

Manohar Paluri, Facebook:

Yixuan Li, Facebook Research:

Ashwin Bharambe, Facebook:

Laurens van der Maaten, Facebook AI Research:

【Machine learning】Saa S: Speed as a Supervisor for Semi-supervised Learning

Safa Cicek, UCLA:

Alhussein Fawzi, UCLA:

Stefano Soatto, UCLA: http://vision.ucla.edu/projects.html

【Machine learning】Progressive Lifelong Learning by Distillation and Retrospection

Saihui Hou, University of Science and Technology of China:

Xinyu Pan, MMLAB, CUHK:

Chen Change Loy, Chinese University of Hong Kong: http://www.eecs.qmul.ac.uk/~ccloy/

Dahua Lin, The Chinese University of Hong Kong: http://dahua.me/

【Machine learning】Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

Xingang Pan, The Chinese University of Hong Kong:

Ping Luo, The Chinese University of Hong Kong:

Jianping Shi, Sensetime Group Limited:

Xiaoou Tang, The Chinese University of Hong Kong: http://mmlab.ie.cuhk.edu.hk/

【Machine learning】Second-order Democratic Aggregation

Tsung-Yu Lin, University of Massachusetts Amherst:

Subhransu Maji, University of Massachusetts, Amherst: http://people.cs.umass.edu/~smaji/

Piotr Koniusz, Data61/CSIRO, ANU:

【Machine learning】Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

Arun Mallya, UIUC:

Svetlana Lazebnik, UIUC: http://www.cs.illinois.edu/homes/slazebni/

Dillon Davis, UIUC:

【Machine learning】Deep JDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation

Bharath Bhushan Damodaran, IRISA,Universite de Bretagne-Sud:

Benjamin Kellenberger, Wageningen University and Research:

Rémi Flamary, Université Côte d’Azur:

Devis Tuia, Wageningen University and Research:

Nicolas Courty, IRISA, Universite Bretagne-Sud:

【Machine learning】Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

Ananya Harsh Jha, Indraprastha Institute of Information Technology Delhi:

Saket Anand, Indraprastha Institute of Information Technology Delhi:

Maneesh Singh, Verisk Analytics:

VSR Veeravasarapu, Verisk Analytics:

【Machine learning】Deep GUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model

Stéphane Lathuiliere, INRIA:

Pablo Mesejo-Santiago, University of Granada:

Xavier Alameda-Pineda, INRIA:

Radu Horaud, INRIA:

【Machine learning】Generalized Loss-Sensitive Adversarial Learning with Manifold Margins

Marzieh Edraki, University of Central Florida:

Guo-Jun Qi, University of Central Florida:

【Machine learning】Adversarial Open Set Domain Adaptation

Kuniaki Saito, The University of Tokyo:

Shohei Yamamoto, The University of Tokyo:

Yoshitaka Ushiku, The University of Tokyo:

Tatsuya Harada, The University of Tokyo:

【Machine learning】Multi-modal Cycle-consistent Generalized Zero-Shot Learning

RAFAEL FELIX, The University of Adelaide:

Vijay Kumar B G, University of Adelaide:

Ian Reid, University of Adelaide, Australia: http://www.robots.ox.ac.uk/~ian/

Gustavo Carneiro, University of Adelaide: http://cs.adelaide.edu.au/~carneiro/research.html

【Machine learning】Mask Connect: Connectivity Learning by Gradient Descent

Karim Ahmed, Dartmouth College:

Lorenzo Torresani, Dartmouth College:

【Machine learning】Correcting the Triplet Selection Bias for Triplet Loss

Baosheng Yu, The University of Sydney:

Tongliang Liu, The University of Sydney:

Mingming Gong, CMU & U Pitt:

Changxing Ding, South China University of Technology:

Dacheng Tao, University of Sydney:

【Machine learning】Escaping from Collapsing Modes in a Constrained Space

Chieh Lin, National Tsing Hua University:

Chia-Che Chang, National Tsing Hua University:

Che-Rung Lee, National Tsing Hua University:

Hwann-Tzong Chen, National Tsing Hua University: http://www.cs.nthu.edu.tw/~htchen/

【Machine learning】Perturbation Robust Representations of Topological Persistence Diagrams

Anirudh Som, Arizona State University:

Kowshik Thopalli, Arizona State University:

Karthikeyan Natesan Ramamurthy, IBM Research:

Vinay Venkataraman, Arizona State University:

Ankita Shukla, Indraprastha Institute of Information Technology – Delhi:

Pavan Turaga, Arizona State University:

【Machine learning】Attend and Rectify: a gated attention mechanism for fine-grained recovery

Pau Rodriguez Lopez, Computer Vision Center, Universitat Autonoma de Barcelona:

Guillem Cucurull, Computer Vision Center, Universitat Autonoma de Barcelona:

Josep Gonfaus, Computer Vision Center:

Jordi Gonzalez, UA Barcelona:

Xavier Roca, Computer Vision Center, Universitat Autonoma de Barcelona:

【Machine learning】Extreme Network Compression via Filter Group Approximation

Bo Peng, Hikvision Research Institute:

Wenming Tan, Hikvision Research Institute:

Zheyang Li, Hikvision Research Institute:

Shun Zhang, Hikvision Research Institute:

Di Xie, Hikvision Research Institute:

Shiliang Pu, Hikvision Research Institute:

【Machine learning】Convolutional Block Attention Module

Sanghyun Woo, KAIST:

Jongchan Park, KAIST:

Joon-Young Lee, Adobe Research:

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

【Machine learning】Hybrid Net: Classification and Reconstruction Cooperation for Semi-Supervised Learning

Thomas Robert, LIP6 / Sorbonne Universite:

Nicolas Thome, CNAM, Paris:

Matthieu Cord, Sorbonne University:

【Machine learning】Clustering Convolutional Kernels to Compress Deep Neural Networks

Sanghyun Son, Seoul National University:

Seungjun Nah, Seoul National University:

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

【Machine learning】MRF Optimization with Separable Convex Prior on Partially Ordered Labels

Csaba Domokos, Technical University of Munich:

Frank Schmidt, BCAI:

Daniel Cremers, TUM: http://vision.in.tum.de/

【Machine learning】Partial Adversarial Domain Adaptation

Zhangjie Cao, Tsinghua University:

Lijia Ma, Tsinghua University:

Mingsheng Long, Tsinghua University:

Jianmin Wang, Tsinghua University, China:

【Machine learning】Improving Embedding Generalization via Scalable Neighborhood Component Analysis

Zhirong Wu, UC Berkeley:

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

Stella Yu, UC Berkeley / ICSI: http://www1.icsi.berkeley.edu/~stellayu/

【Machine learning】U-PC: Unsupervised Planogram Compliance

Archan Ray, University of Massachusetts Amherst:

Nishant Kumar, SMART-FM:

Avishek Shaw, Tata Consultancy Services Limited:

Dipti Prasad Mukherjee, ISI, Kolkata:

【Machine learning】Seeing Tree Structure from Vibration

Tianfan Xue, MIT:

Jiajun Wu, MIT:

Zhoutong Zhang, MIT:

Chengkai Zhang, MIT:

Joshua Tenenbaum, MIT:

Bill Freeman, MIT: https://billf.mit.edu/

【Machine learning】A Scalable Exemplar-based Subspace Clustering Algorithm for Class-Imbalanced Data

Chong You, Johns Hopkins University:

Chi Li, Johns Hopkins University:

Daniel Robinson, Johns Hopkins University:

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

【Machine learning】Deterministic Consensus Maximization with Biconvex Programming

Zhipeng Cai, The University of Adelaide:

Tat-Jun Chin, University of Adelaide:

Huu Le, University of Adelaide:

David Suter, University of Adelaide:

【Machine learning】Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors

Dmitry Baranchuk, MSU / Yandex:

Artem Babenko, MIPT/Yandex:

Yury Malkov, NTech Lab:

【Machine learning】End-to-End Incremental Learning

Francisco M. Castro, University of Málaga:

Manuel J. Marín-Jiménez, University of Córdoba:

Nicolás Guil, University of Málaga:

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

Karteek Alahari, Inria:

【Machine learning】Descending, lifting or smoothing: Secrets of robust cost optimization

Christopher Zach, Toshiba Research:

Guillaume Bourmaud, University of Bordeaux:

【Machine learning】Learning with Biased Complementary Labels

Xiyu Yu, The University of Sydney:

Tongliang Liu, The University of Sydney:

Mingming Gong, University of Pittsburgh:

Dacheng Tao, University of Sydney:

【Machine learning】Robust fitting in computer vision: easy or hard?

Tat-Jun Chin, University of Adelaide:

Zhipeng Cai, The University of Adelaide:

Frank Neumann, The University of Adelaide, School of Computer Science, Faculty of Engineering, Computer and Mathematical Science:

【Machine learning】MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models

Siddharth Tourani, Visual Learning Lab, HCI, Uni-Heidelberg:

Alexander Shekhovtsov, Czech Technical University in Prague, Czech Republic: http://cmp.felk.cvut.cz/~shekhovt/

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

Bogdan Savchynskyy, Heidelberg University:

【Machine learning】A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers

Tianyun Zhang, Syracuse University:

Shaokai Ye, Syracuse University:

Kaiqi Zhang, Syracuse University:

Yanzhi Wang, Syracuse University:

Makan Fardad, Syracuse Universtiy:

Wujie Wen, Florida International University:

【Machine learning】Interpretable Basis Decomposition for Visual Explanation

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

Bolei Zhou, MIT:

David Bau, MIT:

Yiyou Sun, Harvard:

【Machine learning】Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization

Daniel Jakubovitz, Tel Aviv University:

Raja Giryes, Tel Aviv University:

【Machine learning】Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: the Benefit of Target Expectation Maximization

Guoliang Kang, UTS:

Liang Zheng, Singapore University of Technology and Design:

Yan Yan, UTS:

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

【Machine learning】Separable Cross-Domain Translation

Yedid Hoshen, Facebook AI Research (FAIR):

Lior Wolf, Tel Aviv University, Israel: http://www.cs.tau.ac.il/~wolf/

【Machine learning】Learning to Dodge A Bullet

shi jin, Shanghai Tech University:

Jinwei Ye, Louisiana State University:

Yu Ji, Plex-VR:

RUIYANG LIU, Shanghai Tech University:

Jingyi Yu, Shanghai Tech University:

【Machine learning】Toward Characteristic-Preserving Image-based Virtual Try-On Network

Bochao Wang, Sun Yet-sen University:

Huabin Zheng, Sun Yat-Sen University:

Xiaodan Liang, Carnegie Mellon University:

Yimin Chen, sensetime:

Liang Lin, Sun Yat-sen University: http://ss.sysu.edu.cn/~ll/index.html

【Machine learning】Deep Feature Factorization For Unsupervised Concept Discovery

Edo Collins, EPFL:

Radhakrishna Achanta, EPFL:

Sabine Süsstrunk, EPFL:

【Machine learning】Deep Clustering for Unsupervised Learning of Visual Features

Mathilde Caron, Facebook Artificial Intelligence Research:

Piotr Bojanowski, Facebook:

Armand Joulin, Facebook AI Research:

Matthijs Douze, Facebook AI Research: https://research.fb.com/category/facebook-ai-research/

【Machine learning】Learning to Learn Parameterized Image Operators

Qingnan Fan, Shandong University:

Dongdong Chen, university of science and technology of china:

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

Gang Hua, Microsoft Cloud and AI: http://www.cs.stevens.edu/~ghua/

Nenghai Yu, University of Science and Technology of China:

Baoquan Chen, Shandong University: http://www.cs.sdu.edu.cn/~baoquan/

【Machine learning】Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching

Johannes Schoenberger, ETH Zurich:

Sudipta Sinha, Microsoft Research:

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

【Machine learning】Efficient Relative Attribute Learning using Graph Neural Networks

Zihang Meng, University of Wisconsin Madison:

Nagesh Adluru , WISC:

Vikas Singh, University of Wisconsin-Madison USA: http://www.biostat.wisc.edu/~vsingh/

【Machine learning】Reloc Net: Continous Metric Learning Relocalisation using Neural Nets

Vassileios Balntas, University of Oxford:

Victor Prisacariu, University of Oxford:

Shuda Li, University of Oxford:

【Machine learning】Museum Exhibit Identification Challenge for the Supervised Domain Adaptation.

Piotr Koniusz, Data61/CSIRO, ANU:

Yusuf Tas, Data61:

Hongguang Zhang, Australian National University:

Mehrtash Harandi, Monash University:

Fatih Porikli, ANU: http://www.porikli.com/

Rui Zhang, University of Canberra:

【Machine learning】LSQ++: lower runtime and higher recall in multi-codebook quantization

Julieta Martinez, University of British Columbia:

Shobhit Zakhmi, University of British Columbia:

Holger Hoos, University of British Columbia:

Jim Little, University of British Columbia, Canada: https://www.cs.ubc.ca/~little/

【Machine learning】Deep Variational Metric Learning

Xudong Lin, Tsinghua University:

Yueqi Duan, Tsinghua University:

Qiyuan Dong, Tsinghua University:

Jiwen Lu, Tsinghua University:

Jie Zhou, Tsinghua University, China: https://www.tsinghua.edu.cn/publish/auen/1713/2011/20110506105532098625469/20110506105532098625469_.html

【Machine learning】Multi-Class Model Fitting by Energy Minimization and Mode-Seeking

Dániel Baráth, MTA SZTAKI, CMP Prague:

Jiri Matas, CMP CTU FEE:

【Machine learning】Randomized Ensemble Embeddings

Hong Xuan, The George Washington University:

Robert Pless, George Washington University:

【Machine learning】Variational Wasserstein Clustering

Liang Mi, Arizona State University:

wen zhang, ASU:

Xianfeng GU, Stony Brook University:

Yalin Wang, Arizona State University:

【Machine learning】On the Solvability of Viewing Graphs

Matthew Trager, INRIA:

Brian Osserman, UC Davis:

Jean Ponce, Inria: http://www.di.ens.fr/willow/

【Machine learning】Focus on the Hard Things: Dynamic Task Prioritization for Multitask Learning

Michelle Guo, Stanford University:

Albert Haque, Stanford University:

De-An Huang, Stanford University:

Serena Yeung, Stanford University:

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

【Machine learning】Domain transfer through deep activation matching

Haoshuo Huang, Tsinghua University:

Qixing Huang, The University of Texas at Austin:

Philipp Kraehenbuehl, UT Austin: https://www.philkr.net/

【Machine learning】Dynamic Conditional Networks for Few-Shot Learning

Fang Zhao, National University of Singapore:

Jian Zhao, National University of Singapore:

Yan Shuicheng, National University of Singapore:

Jiashi Feng, NUS: https://sites.google.com/site/jshfeng/

【Machine learning】Understanding Perceptual and Conceptual Fluency at a Large Scale

Meredith Hu, Cornell University:

Ali Borji, University of Central Florida: http://ilab.usc.edu/borji/

【GAN】How good is my GAN?

Konstantin Shmelkov, Inria:

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

Karteek Alahari, Inria:

【GAN】Explain GAN: Model Explanation via Decision Boundary Crossing Transformations

Nathan Silberman, Butterfly Network:

Pouya Samangouei, Butterfly Network:

Liam Nakagawa, Butterfly Network:

Ardavan Saeedi, Butterfly Network Inc:

【GAN】A Principled Approach to Hard Triplet Generation via Adversarial Nets

Yiru Zhao, Shanghai Jiao Tong University:

Zhongming Jin, Alibaba Group:

Guo-Jun Qi, University of Central Florida:

Hongtao Lu, Shanghai Jiao Tong University:

Xian-Sheng Hua, Alibaba Group:

【GAN】Sub-GAN: An Unsupervised Generative Model via Subspaces

Jie Liang, Nankai University:

Jufeng Yang, Nankai University:

Hsin-Ying Lee, University of California, Merced:

Kai Wang, Nankai University:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

【GAN】Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders

Panna Felsen, University of California Berkeley:

Patrick Lucey, STATS:

Sujoy Ganguly, STATS:

【GAN】Modular Generative Adversarial Networks

Bo Zhao, University of British Columbia:

Bo Chang, University of British Columbia:

Zequn Jie, Tencent AI Lab:

Leonid Sigal, University of British Columbia: https://www.cs.ubc.ca/~lsigal/

【GAN】Transferable Adversarial Perturbations

Bruce Hou, Tencent:

Wen Zhou, Tencent:

【GAN】Dist-GAN: An Improved GAN using Distance Constraints

Ngoc-Trung Tran, Singapore University of Technology and Design:

Tuan Anh Bui, Singapore University of Technology and Design:

Ngai-Man Cheung, Singapore University of Technology and Design:

【GAN】Gray box adversarial training

Vivek B S, Indian Institute of Science:

Konda Reddy Mopuri, Indian Institute of Science, Bangalore:

Venkatesh Babu RADHAKRISHNAN, Indian Institute of Science:

【Deep learning】Semi-Supervised Deep Learning with Memory

Yanbei Chen, Queen Mary University of London:

Xiatian Zhu, Queen Mary University, London, UK:

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

【Deep learning】Convolutional Networks with Adaptive Computation Graphs

Andreas Veit, Cornell University:

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

【Deep learning】Value-aware Quantization for Training and Inference of Neural Networks

Eunhyeok Park, Seoul National University:

Sungjoo Yoo, Seoul National University:

Peter Vajda, Facebook:

【Deep learning】Real-Time MDNet

Ilchae Jung, POSTECH:

Jeany Son, POSTECH:

Mooyeol Baek, POSTECH:

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

【Deep learning】Conv Nets and Image Net Beyond Accuracy: Understanding Mistakes and Uncovering Biases

Pierre Stock, Facebook AI Research:

Moustapha Cisse, Facebook AI Research:

【Deep learning】Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses

Zheng Dang, Xi’an Jiaotong University:

Kwang Moo Yi, University of Victoria:

Yinlin Hu, EPFL:

Fei Wang, Xi’an Jiaotong University:

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

Mathieu Salzmann, EPFL:

【Deep learning】Self-supervised Knowledge Distillation Using Singular Value Decomposition

SEUNG HYUN LEE, Inha University:

Daeha Kim, Inha University:

Byung Cheol Song, Inha University:

【Deep learning】DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation

Zuxuan Wu, UMD:

Xintong Han, University of Maryland, USA:

Yen-Liang Lin, GE Global Research:

Gokhan Uzunbas, Avitas Systems-GE Venture:

Tom Goldstein, University of Maryland, College Park:

Ser-Nam Lim, GE Global Research:

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

【Deep learning】Wasserstein Divergence For GANs

Jiqing Wu, ETH Zurich:

Zhiwu Huang, ETH Zurich:

Janine Thoma, ETH Zurich:

Dinesh Acharya, ETH Zurich:

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

【Deep learning】Deep Metric Learning with Hierarchical Triplet Loss

Weifeng Ge, The University of Hong Kong:

【Deep learning】Transductive Semi-Supervised Deep Learning using Min-Max Features

Weiwei Shi, Xi’an Jiaotong University:

Yihong Gong, Xi’an Jiaotong University:

Chris Ding, UNIVERSITY OF TEXAS AT ARLINGTON: http://ranger.uta.edu/~chqding/

Zhiheng Ma, Xi’an Jiaotong University:

Xiaoyu Tao, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University.:

Nanning Zheng, Xi’an Jiaotong University:

【Deep learning】Quadtree Convolutional Neural Networks

Pradeep Kumar Jayaraman, Nanyang Technological University:

Jianhan Mei, Nanyang Technological University:

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

Jianmin Zheng, Nanyang Technological University:

【Deep learning】AMC: Automated Model Compression and Acceleration with Reinforcement Learning

Yihui He, Xi’an Jiaotong University:

Ji Lin, Tsinghua University:

Song Han, MIT:

【Deep learning】Optimized Quantization for Highly Accurate and Compact DNNs

Dongqing Zhang, Microsoft Research:

Jiaolong Yang, Microsoft Research Asia (MSRA):

Dongqiangzi Ye, Microsoft Research:

Gang Hua, Microsoft Cloud and AI: http://www.cs.stevens.edu/~ghua/

【Deep learning】EC-Net: an Edge-aware Point set Consolidation Network

Lequan Yu, The Chinese University of Hong Kong:

Xianzhi Li, The Chinese University of Hong Kong:

Chi-Wing Fu, The Chinese University of Hong Kong:

Danny Cohen-Or, Tel Aviv University:

Pheng-Ann Heng, The Chinese Univsersity of Hong Kong: http://www.cse.cuhk.edu.hk/~pheng/

【Deep learning】Explainable Neural Computation via Stack Neural Module Networks

Ronghang Hu, University of California, Berkeley:

Jacob Andreas, UC Berkeley:

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

Kate Saenko, Boston University:

【Deep learning】Quaternion Convolutional Neural Networks

Xuanyu Zhu, Shanghai Jiao Tong University:

Yi Xu, Shanghai Jiao Tong University:

Hongteng Xu, Duke University:

Changjian Chen, Shanghai Jiao Tong University:

【Deep learning】Constraints Matter in Deep Neural Network Compression

Changan Chen, Simon Fraser University:

Fred Tung, Simon Fraser University:

Naveen Vedula, Simon Fraser University:

Greg Mori, Simon Fraser University: http://www.cs.sfu.ca/~mori/

【Deep learning】Switchable Temporal Propagation Network

Sifei Liu, NVIDIA:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

Guangyu Zhong, Dalian University of Technology:

Jinwei Gu, Nvidia:

Shalini De Mello, NVIDIA Research:

Kautz Jan, NVIDIA:

Varun Jampani, Nvidia Research:

【Deep learning】Coreset-Based Convolutional Neural Network Compression

Abhimanyu Dubey, Massachusetts Institute of Technology:

Moitreya Chatterjee, University of Illinois at Urbana Champaign:

Ramesh Raskar, Massachusetts Institute of Technology:

Narendra Ahuja, University of Illinois at Urbana-Champaign, USA: http://vision.ai.illinois.edu/publications.htm

【Deep learning】Statistically-motivated Second-order Pooling

Kaicheng Yu, EPFL:

Mathieu Salzmann, EPFL:

【Deep learning】Superpixel Sampling Networks

Varun Jampani, Nvidia Research:

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

Ming-Yu Liu, NVIDIA:

Ming-Hsuan Yang, University of California at Merced: http://faculty.ucmerced.edu/mhyang/

Kautz Jan, NVIDIA:

【Deep learning】Towards Robust Neural Networks via Random Self-ensemble

Xuanqing Liu, UC Davis Department of Computer Science:

Minhao Cheng, University of California, Davis:

Huan Zhang, UC Davis:

Cho-Jui Hsieh, UC Davis Department of Computer Science and Statistics:

【Deep learning】Normalized Blind Deconvolution

Meiguang Jin, University of Bern:

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

Paolo Favaro, Bern University, Switzerland:

【Deep learning】Deep Bilevel Learning

Simon Jenni, Universität Bern:

Paolo Favaro, Bern University, Switzerland:

【Deep learning】Net Adapt: Platform-Aware Neural Network Adaptation for Mobile Applications

Tien-Ju Yang, Massachusetts Institute of Technology:

Andrew Howard, Google:

Bo Chen, Google:

Xiao Zhang, Google:

Alec Go, Google:

Vivienne Sze, Massachusetts Institute of Technology:

Hartwig Adam, Google:

【Deep learning】Aug GAN: Cross Domain Adaptation with GAN-based Data Augmentation

Sheng-Wei Huang, National Tsing Hua University:

Che-Tsung Lin, National Tsing Hua University:

Shu-Ping Chen, National Tsing Hua University:

Yen-Yi Wu, NTHU CS:

Po-Hao Hsu, National Tsing Hua University:

Shang-Hong Lai , National Tsing Hua University:

【Deep learning】Compressing the Input for CNNs with the First-Order Scattering Transform

Edouard Oyallon, Centrale Supélec:

Eugene Belilovsky, Inria Galen / KU Leuven:

Sergey Zagoruyko, Inria:

Michal Valko, Inria:

【Deep learning】Constrained Optimization Based Low-Rank Approximation of Deep Neural Networks

Chong Li, University of Washington:

C.J. Richard Shi, University of Washington:

【Deep learning】Adding Attentiveness to the Neurons in Recurrent Neural Networks

Pengfei Zhang, Xi’an Jiaotong University:

Jianru Xue, Xi’an Jiaotong University:

Cuiling Lan, Microsoft Research:

Wenjun Zeng, Microsoft Research:

Zhanning Gao, Xi’an Jiaotong University:

Nanning Zheng, Xi’an Jiaotong University:

【Deep learning】Sparsely Aggregated Convolutional Networks

Ligeng Zhu, Simon Fraser University:

Ruizhi Deng, Simon Fraser University:

Michael Maire, Toyota Technological Institute at Chicago: http://ttic.uchicago.edu/~mmaire/

Zhiwei Deng, Simon Fraser University:

Greg Mori, Simon Fraser University: http://www.cs.sfu.ca/~mori/

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

【Deep learning】Diverse feature visualizations reveal invariances in early layers of deep neural networks

Santiago Cadena, University of Tübingen:

Marissa Weis, University of Tübingen:

Leon A. Gatys, University of Tuebingen:

Matthias Bethge, University of Tübingen:

Alexander Ecker, University of Tübingen:

【Deep learning】DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures

Jin-Dong Dong, National Tsing-Hua University:

An-Chieh Cheng, National Tsing-Hua University:

Da-Cheng Juan, Google:

Wei Wei, Google:

Min Sun, NTHU: http://aliensunmin.github.io/

【Deep learning】CAR-Net: Clairvoyant Attentive Recurrent Network

Amir Sadeghian, Stanford:

Maxime Voisin, Stanford University:

Ferdinand Legros, Stanford University:

Ricky Vesel, Race Optimal:

Alexandre Alahi, EPFL:

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

【Deep learning】Practical Black-box Attacks on Deep Neural Networks using Efficient Query Mechanisms

Arjun Nitin Bhagoji, Princeton University:

Warren he, University of California, Berkeley:

Bo Li, University of Illinois at Urbana–Champaign:

Dawn Song, UC Berkeley: https://people.eecs.berkeley.edu/~dawnsong/

【Deep learning】Comparator Networks

Weidi Xie, University of Oxford:

Li Shen, University of Oxford:

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

【Deep learning】Zero-Shot Deep Domain Adaptation

Kuan-Chuan Peng, siemens corporation:

Ziyan Wu, Siemens Corporation:

Jan Ernst, Siemens Corporation:

【Deep learning】Conditional Image-Text Embedding Networks

Bryan Plummer, Boston University:

Paige Kordas, University of Illinois at Urbana Champaign:

Hadi Kiapour, e Bay:

Shuai Zheng, e Bay:

Robinson Piramuthu, e Bay Inc.:

Svetlana Lazebnik, UIUC: http://www.cs.illinois.edu/homes/slazebni/

【Deep learning】Learning Deep Representations with Probabilistic Knowledge Transfer

Nikolaos Passalis, Aristotle University of Thessaloniki:

Anastasios Tefas, Aristotle University of Thessaloniki:

【Deep learning】Dynamic Sampling Convolutional Neural Networks

Jialin Wu, UT Austin:

Dai Li, Tsinghua University:

Yu Yang, Tsinghua University:

Chandrajit Bajaj, University of Texas, Austin:

Xiangyang Ji, Tsinghua University:

【Deep learning】Actor-centric Relation Network

Chen Sun, Google:

Abhinav Shrivastava, UMD / Google:

Carl Vondrick, MIT:

Kevin Murphy, Google:

Rahul Sukthankar, Google:

Cordelia Schmid, Google: http://lear.inrialpes.fr/~schmid/

【Deep learning】Neural Network Encapsulation

Hongyang Li, Chinese University of Hong Kong:

Bo Dai, the Chinese University of Hong Kong:

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

Xiaoyang Guo, The Chinese University of Hong Kong:

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

【Deep learning】Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

Arslan Chaudhry, University of Oxford:

Puneet Dokania, University of Oxford:

Thalaiyasingam Ajanthan, University of Oxford:

Philip Torr, University of Oxford: http://www.robots.ox.ac.uk/~tvg/

【Deep learning】Group Normalization

Yuxin Wu, Facebook:

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

【Deep learning】Deep Expander Networks: Efficient Deep Networks from Graph Theory

Ameya Prabhu, IIIT Hyderabad:

Girish Varma, IIIT Hyderabad:

Anoop Namboodiri, IIIT Hyderbad:

【Deep learning】Learning S

Carlos Esteves, University of Pennsylvania:

Kostas Daniilidis, University of Pennsylvania:

Ameesh Makadia, Google Research:

Christine Allec-Blanchette, University of Pennsylvania:

【Deep learning】Light-weight CNN Architecture Design for Fast Inference

Ningning Ma, Tsinghua:

Xiangyu Zhang, Megvii Inc:

Hai-Tao Zheng, Tsinghua University:

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

【Deep learning】Choose Your Neuron: Incorporating Domain Knowledge through Neuron Importance

Ramprasaath Ramasamy Selvaraju, Virginia Tech:

Prithvijit Chattopadhyay, Georgia Institute of Technology:

Mohamed Elhoseiny, Facebook:

Tilak Sharma, Facebook:

Dhruv Batra, Georgia Tech & Facebook AI Research:

Devi Parikh, Georgia Tech & Facebook AI Research: https://filebox.ece.vt.edu/~parikh/

Stefan Lee, Georgia Institute of Technology:

【Deep learning】Interpolating Convolutional Neural Networks Using Batch Normalization

Gratianus Wesley Putra Data, University of Oxford:

Kirjon Ngu, University of Oxford:

David Murray, University of Oxford: http://www.robots.ox.ac.uk/~lav/

Victor Prisacariu, University of Oxford:

【Deep learning】Training Binary Weight Networks via Semi-Binary Decomposition

Qinghao Hu, Institute of Automation, Chinese Academy of Sciences:

Gang Li, Institute of Automation, Chinese Academy of Sciences:

Peisong Wang, Institute of Automation, Chinese Academy of Sciences:

yifan zhang, Institute of Automation,Chinese Academy of Sciences:

Jian Cheng, Chinese Academy of Sciences, China: http://www.nlpr.ia.ac.cn/jcheng/

【Deep learning】Skip Net: Learning Dynamic Execution in Residual Networks

Xin Wang, UC Berkeley:

Fisher Yu, UC Berkeley:

Zi-Yi Dou, Nanjing University:

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

Joseph Gonzalez, UC Berkeley:

【Deep learning】Deep Matching Autoencoder

Tanmoy Mukherjee, University of Edinburgh/QMUL:

Makoto Yamada, RIKEN AIP:

Timothy Hospedales, Edinburgh University: http://homepages.inf.ed.ac.uk/thospeda/

【Deep learning】Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

Xin Wang, University of California, Santa Barbara:

Wenhan Xiong, University of California, Santa Barbara:

Hongmin Wang, University of California, Santa Barbara:

William Wang, UC Santa Barbara:

【Deep learning】Hi DDe N: Hiding Data with Deep Networks

Jiren Zhu, Stanford University:

Russell Kaplan, Stanford University:

Justin Johnson, Stanford University:

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

【Deep learning】Broadcasting Convolutional Network for Visual Relational Reasoning

Simyung Chang, Seoul National University:

John Yang, Seoul National University:

Seonguk Park, Seoul National University:

Nojun Kwak, Seoul National University: http://mipal.snu.ac.kr/index.php/Nojun_Kwak

【Deep learning】Deep Domain Generalization via Conditional Invariant Adversarial Networks

Ya Li, USTC:

Xinmei Tian, USTC:

Mingming Gong, CMU & U Pitt:

Yajing Liu, USTC:

Tongliang Liu, The University of Sydney:

Kun Zhang, Carnegie Mellon University:

Dacheng Tao, University of Sydney:

【Deep learning】Bi-Real Net: Enhancing the Performance of 1-bit CNNs with Improved Representational Capability and Advanced Training Algorithm

zechun liu, HKUST:

Baoyuan Wu, Tencent AI Lab:

Wenhan Luo, Tencent AI Lab:

Xin Yang, Huazhong University of Science and Technology:

Wei Liu, Tencent AI Lab: http://www.ee.columbia.edu/~wliu/

Kwang-Ting Cheng, Hong Kong University of Science and Technology:

【Deep learning】Data-Driven Sparse Structure Selection for Deep Neural Networks

Zehao Huang, Tu Simple:

Naiyan Wang, Tu Simple: http://winsty.net/

【Deep learning】Deep Component Analysis via Alternating Direction Neural Networks

Calvin Murdock, Carnegie Mellon University:

Ming Fang Chang, Carnegie Mellon University:

Simon Lucey, CMU:

【PointCloud analysis】Tackling 3D To F Artifacts Through Learning and the FLAT Dataset

Qi Guo, Harvard University:

Iuri Frosio, NVIDIA:

Orazio Gallo, NVIDIA Research:

Todd Zickler, Harvard University:

Kautz Jan, NVIDIA:

【PointCloud analysis】Fully-Convolutional Point Networks for Large-Scale Point Clouds

Dario Rethage, Technical University of Munich, Germany:

Johanna Wald, Technical University of Munich:

Nassir Navab, TU Munich, Germany: http://campar.in.tum.de/Main/NassirNavab

Federico Tombari, Technical University of Munich, Germany: http://vision.deis.unibo.it/fede/

【PointCloud analysis】Proxy Clouds for Live RGB-D Stream Processing and Consolidation

Adrien Kaiser, Telecom Paris Tech:

Jose Alonso Ybanez Zepeda, Ayotle SAS:

Tamy Boubekeur, Paris Telecom:

【PointCloud analysis】Multiresolution Tree Networks for Point Cloud Procesing

Matheus Gadelha, University of Massachusetts Amherst:

Subhransu Maji, University of Massachusetts, Amherst: http://people.cs.umass.edu/~smaji/

Rui Wang, U Massachusetts:

【PointCloud analysis】3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation

Xiaoqing Ye, SIMIT:

Jiamao Li, SIMIT:

Hexiao Huang, Shanghai Opening University:

Xiaolin Zhang, SIMIT:

【PointCloud analysis】Spider CNN: Deep Learning on Point Sets with Parameterized Convolutional Filters

Yifan Xu, Tsinghua University:

Tianqi Fan, Multimedia Laboratory, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences:

Mingye Xu, Multimedia Laboratory, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences:

Long Zeng, Tsinghua University:

Yu Qiao, Multimedia Laboratory, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences: http://mmlab.siat.ac.cn/yuqiao/

【PointCloud analysis】Efficient Global Point Cloud Registration by Matching Rotation Invariant Features Through Translation Search

Yinlong Liu, Fudan University:

Wang Chen, Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Digital Medical Research Center, Fudan University:

Zhijian Song, Fudan University:

Manning Wang, Fudan University:

【PointCloud analysis】Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Lei Zhou, HKUST:

Siyu Zhu, HKUST:

Zixin Luo, HKUST:

Tianwei Shen, HKUST:

Runze Zhang, HKUST:

Tian Fang, HKUST:

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

【PointCloud analysis】Fast and Accurate Point Cloud Registration using Trees of Gaussian Mixtures

Benjamin Eckart, NVIDIA:

Kihwan Kim, NVIDIA:

Kautz Jan, NVIDIA:

【PointCloud analysis】3DFeat-Net: Weakly Supervised Local 3D Features for Rigid Point Cloud Registration

Zi Jian Yew, National University of Singapore:

Gim Hee Lee, National University of SIngapore: https://sites.google.com/site/gimheelee/home/publications