2017 ICCV

下面是2017 ICCV文章的主题标签,文章列表来源于http://iccv2017.thecvf.com/,  PDF下载地址http://openaccess.thecvf.com/content_iccv_2017/html/
Topic: Scene parsing; Object segmentation; Image segmentation; Video segmentation; Boundary detection; Contour analysis; Object tracking; Action recognition; Crowd analysis; Video analysis; Human detection; Human parsing; Face recognition; Face parsing; Object recognition; Object detection; Saliency detection; Scene recognition; Text recognition; Image retrieval; 3D modeling; Feature matching; Pose estimation; Stereo matching;Optical flow;Region matching; Image editing; Computational photography; Texture analysis; Data clustering; Space reduction; Machine learning; Deep learning;Multimodel learning;
【Scene parsing】Visual Semantic Planning Using Deep Successor Representations

Yuke Zhu:

Daniel Gordon:

Eric Kolve:

Dieter Fox:

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

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

Roozbeh Mottaghi: http://www.cs.stanford.edu/~roozbeh/

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

【Scene parsing】Increasing CNN Robustness to Occlusions by Reducing Filter Support

Elad Osherov:

Michael Lindenbaum:

【Scene parsing】Predicting Deeper Into the Future of Semantic Segmentation

Pauline Luc:

Natalia Neverova:

Camille Couprie:

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

Yann Le Cun: http://yann.lecun.com/

【Scene parsing】Fovea Net: Perspective-Aware Urban Scene Parsing

Xin Li:

Zequn Jie:

Wei Wang:

Changsong Liu:

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

Xiaohui Shen:

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

Qiang Chen:

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

Jiashi Feng:

【Scene parsing】Cascaded Feature Network for Semantic Segmentation of RGB-D Images

Di Lin:

Guangyong Chen:

Daniel Cohen-Or:

Pheng-Ann Heng:

Hui Huang:

【Scene parsing】Adversarial Examples for Semantic Segmentation and Object Detection

Cihang Xie:

Jianyu Wang:

Zhishuai Zhang:

Yuyin Zhou:

Lingxi Xie:

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

【Scene parsing】Attentive Semantic Video Generation Using Captions

Tanya Marwah:

Gaurav Mittal:

Vineeth N. Balasubramanian:

【Scene parsing】Joint Convolutional Analysis and Synthesis Sparse Representation for Single Image Layer Separation

Shuhang Gu:

Deyu Meng:

Wangmeng Zuo:

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

【Scene parsing】Weakly Supervised Manifold Learning for Dense Semantic Object Correspondence

Utkarsh Gaur:

S. Manjunath:

【Scene parsing】VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation

Chuang Gan:

Yandong Li:

Haoxiang Li:

Chen Sun:

Boqing Gong:

【Scene parsing】SCNet: Learning Semantic Correspondence

Kai Han:

Rafael S. Rezende:

Bumsub Ham:

Kwan-Yee K. Wong:

Minsu Cho:

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

Jean Ponce: http://www.di.ens.fr/willow/

【Scene parsing】Hierarchical Multimodal LSTM for Dense Visual-Semantic Embedding

Zhenxing Niu:

Mo Zhou:

Le Wang:

Xinbo Gao:

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

【Scene parsing】Learning From Noisy Labels With Distillation

Yuncheng Li:

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

Yale Song:

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

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

Li-Jia Li:

【Scene parsing】No More Discrimination: Cross City Adaptation of Road Scene Segmenters

Yi-Hsin Chen:

Wei-Yu Chen:

Yu-Ting Chen:

Bo-Cheng Tsai:

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

Min Sun:

【Scene parsing】Open Vocabulary Scene Parsing

Hang Zhao:

Xavier Puig:

Bolei Zhou:

Sanja Fidler:

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

【Scene parsing】Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes

Yang Zhang:

Philip David:

Boqing Gong:

【Scene parsing】Scale-Adaptive Convolutions for Scene Parsing

Rui Zhang:

Sheng Tang:

Yongdong Zhang:

Jintao Li:

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

【Scene parsing】Bringing Background Into the Foreground: Making All Classes Equal in Weakly-Supervised Video Semantic Segmentation

Fatemeh Sadat Saleh:

Mohammad Sadegh Aliakbarian:

Mathieu Salzmann:

Lars Petersson:

Jose M. Álvarez:

【Scene parsing】Scene Parsing With Global Context Embedding

Wei-Chih Hung:

Yi-Hsuan Tsai:

Xiaohui Shen:

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

Kalyan Sunkavalli:

Xin Lu:

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

【Scene parsing】Scene Net RGB-D: Can 5M Synthetic Images Beat Generic Image Net Pre-Training on Indoor Segmentation?

John Mc Cormac:

Ankur Handa:

Stefan Leutenegger:

Andrew J. Davison:

【Scene parsing】Deep Dual Learning for Semantic Image Segmentation

Ping Luo:

Guangrun Wang:

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

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

【Scene parsing】Universal Adversarial Perturbations Against Semantic Image Segmentation

Jan Hendrik Metzen:

Mummadi Chaithanya Kumar:

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

Volker Fischer:

【Scene parsing】Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention

Jinkyu Kim:

John Canny:

【Scene parsing】Predictor Combination at Test Time

Kwang In Kim:

James Tompkin:

Christian Richardt:

【Scene parsing】Learning Robust Visual-Semantic Embeddings

Yao-Hung Hubert Tsai:

Liang-Kang Huang:

Ruslan Salakhutdinov: http://www.cs.toronto.edu/~rsalakhu/

【Scene parsing】Semantic Video CNNs Through Representation Warping

Raghudeep Gadde:

Varun Jampani:

Peter V. Gehler:

【Scene parsing】Optimal Transformation Estimation With Semantic Cues

Danda Pani Paudel:

Adlane Habed:

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

【Scene parsing】RDFNet: RGB-D Multi-Level Residual Feature Fusion for Indoor Semantic Segmentation

Seong-Jin Park:

Ki-Sang Hong:

Seungyong Lee:

【Scene parsing】The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes

Gerhard Neuhold:

Tobias Ollmann:

Samuel Rota Bulò:

Peter Kontschieder:

【Scene parsing】3D Graph Neural Networks for RGBD Semantic Segmentation

Xiaojuan Qi:

Renjie Liao:

Jiaya Jia: http://www.cse.cuhk.edu.hk/leojia/

Sanja Fidler:

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

【Scene parsing】Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images

Aron Yu:

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

【Scene parsing】Video Scene Parsing With Predictive Feature Learning

Xiaojie Jin:

Xin Li:

Huaxin Xiao:

Xiaohui Shen:

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

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

Yunpeng Chen:

Jian Dong:

Luoqi Liu:

Zequn Jie:

Jiashi Feng:

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

【Scene parsing】3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-Scale 3D Point Clouds

Fangyu Liu:

Shuaipeng Li:

Liqiang Zhang:

Chenghu Zhou:

Rongtian Ye:

Yuebin Wang:

Jiwen Lu:

【Scene parsing】Semi Supervised Semantic Segmentation Using Generative Adversarial Network

Nasim Souly:

Concetto Spampinato:

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

【Scene parsing】Semantic Image Synthesis via Adversarial Learning

Hao Dong:

Simiao Yu:

Chao Wu:

Yike Guo:

【Object segmentation】Embedding 3D Geometric Features for Rigid Object Part Segmentation

Yafei Song:

Xiaowu Chen: http://arts.buaa.edu.cn/index.htm

Jia Li:

Qinping Zhao:

【Object segmentation】Learned Watershed: End-To-End Learning of Seeded Segmentation

Steffen Wolf:

Lukas Schott:

Ullrich Köthe:

Fred Hamprecht:

【Object segmentation】Directionally Convolutional Networks for 3D Shape Segmentation

Haotian Xu:

Ming Dong:

Zichun Zhong:

【Object segmentation】Regional Interactive Image Segmentation Networks

Jun Hao Liew:

Yunchao Wei:

Wei Xiong:

Sim-Heng Ong:

Jiashi Feng:

【Object segmentation】Mask R-CNN

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

Georgia Gkioxari:

Piotr Dollár: http://vision.ucsd.edu/~pdollar/

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

【Object segmentation】High-Quality Correspondence and Segmentation Estimation for Dual-Lens Smart-Phone Portraits

Xiaoyong Shen:

Hongyun Gao:

Xin Tao:

Chao Zhou:

Jiaya Jia: http://www.cse.cuhk.edu.hk/leojia/

【Object segmentation】SGN: Sequential Grouping Networks for Instance Segmentation

Shu Liu:

Jiaya Jia: http://www.cse.cuhk.edu.hk/leojia/

Sanja Fidler:

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

【Object segmentation】CDTS: Collaborative Detection, Tracking, and Segmentation for Online Multiple Object Segmentation in Videos

Yeong Jun Koh:

Chang-Su Kim:

【Object segmentation】Deep Free-Form Deformation Network for Object-Mask Registration

Haoyang Zhang:

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

【Object segmentation】Learning Video Object Segmentation With Visual Memory

Pavel Tokmakov:

Karteek Alahari:

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

【Object segmentation】Segmentation-Aware Convolutional Networks Using Local Attention Masks

Adam W. Harley:

Konstantinos G. Derpanis:

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

【Object segmentation】Unsupervised Object Segmentation in Video by Efficient Selection of Highly Probable Positive Features

Emanuela Haller:

Marius Leordeanu:

【Image segmentation】Recurrent Multimodal Interaction for Referring Image Segmentation

Chenxi Liu:

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

Xiaohui Shen:

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

Xin Lu:

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

【Image segmentation】Temporal Superpixels Based on Proximity-Weighted Patch Matching

Se-Ho Lee:

Won-Dong Jang:

Chang-Su Kim:

【Video segmentation】Seg Flow: Joint Learning for Video Object Segmentation and Optical Flow

Jingchun Cheng:

Yi-Hsuan Tsai:

Shengjin Wang:

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

【Video segmentation】Primary Video Object Segmentation via Complementary CNNs and Neighborhood Reversible Flow

Jia Li:

Anlin Zheng:

Xiaowu Chen: http://arts.buaa.edu.cn/index.htm

Bin Zhou:

【Video segmentation】Super-Trajectory for Video Segmentation

Wenguan Wang:

Jianbing Shen: http://cs.bit.edu.cn/shenjianbing/

Jianwen Xie:

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

【Video segmentation】Pixel-Level Matching for Video Object Segmentation Using Convolutional Neural Networks

Jae Shin Yoon:

Francois Rameau:

Junsik Kim:

Seokju Lee:

Seunghak Shin:

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

【Video segmentation】Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation

Margret Keuper:

【Video segmentation】Dynamics Enhanced Multi-Camera Motion Segmentation From Unsynchronized Videos

Xikang Zhang:

Bengisu Ozbay:

Mario Sznaier:

Octavia Camps:

【Video segmentation】A Multilayer-Based Framework for Online Background Subtraction With Freely Moving Cameras

Yizhe Zhu:

Ahmed Elgammal:

【Boundary detection】Multi-Stage Multi-Recursive-Input Fully Convolutional Networks for Neuronal Boundary Detection

Wei Shen:

Bin Wang:

Yuan Jiang:

Yan Wang:

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

【Contour analysis】Multi-View Dynamic Shape Refinement Using Local Temporal Integration

Vincent Leroy:

Jean-Sebastien Franco:

Edmond Boyer:

【Contour analysis】Detailed Surface Geometry and Albedo Recovery From RGB-D Video Under Natural Illumination

Xinxin Zuo:

Sen Wang:

Jiangbin Zheng:

Ruigang Yang: http://vis.uky.edu/~ryang/

【Contour analysis】Semantic Line Detection and Its Applications

Jun-Tae Lee:

Han-Ul Kim:

Chul Lee:

Chang-Su Kim:

【Contour analysis】Deep Road Mapper: Extracting Road Topology From Aerial Images

Gellért Máttyus:

Wenjie Luo:

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

【Contour analysis】Semantically Informed Multiview Surface Refinement

Maroš Bláha:

Mathias Rothermel:

Martin R. Oswald:

Torsten Sattler:

Audrey Richard:

Jan D. Wegner:

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

Konrad Schindler:

【Contour analysis】Active Decision Boundary Annotation With Deep Generative Models

Miriam Huijser:

Jan C. van Gemert:

【Contour analysis】Deep Functional Maps: Structured Prediction for Dense Shape Correspondence

Or Litany:

Tal Remez:

Emanuele Rodolà:

Alex Bronstein:

Michael Bronstein:

【Object tracking】Learning Policies for Adaptive Tracking With Deep Feature Cascades

Chen Huang:

Simon Lucey:

Deva Ramanan: http://www.ics.uci.edu/~dramanan/

【Object tracking】Learning Policies for Adaptive Tracking With Deep Feature Cascades

Chen Huang:

Simon Lucey:

Deva Ramanan: http://www.ics.uci.edu/~dramanan/

【Object tracking】A Geometric Framework for Statistical Analysis of Trajectories With Distinct Temporal Spans

Rudrasis Chakraborty:

Vikas Singh: http://www.biostat.wisc.edu/~vsingh/

Nagesh Adluru:

Baba C. Vemuri:

【Object tracking】Tracking the Untrackable: Learning to Track Multiple Cues With Long-Term Dependencies

Amir Sadeghian:

Alexandre Alahi:

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

【Object tracking】Tracking as Online Decision-Making: Learning a Policy From Streaming Videos With Reinforcement Learning

James Supančič, III:

Deva Ramanan: http://www.ics.uci.edu/~dramanan/

【Object tracking】Need for Speed: A Benchmark for Higher Frame Rate Object Tracking

Hamed Kiani Galoogahi:

Ashton Fagg:

Chen Huang:

Deva Ramanan: http://www.ics.uci.edu/~dramanan/

Simon Lucey:

【Object tracking】Learning Background-Aware Correlation Filters for Visual Tracking

Hamed Kiani Galoogahi:

Ashton Fagg:

Simon Lucey:

【Object tracking】Robust Object Tracking Based on Temporal and Spatial Deep Networks

Zhu Teng:

Junliang Xing:

Qiang Wang:

Congyan Lang:

Songhe Feng:

Yi Jin:

【Object tracking】Real-Time Hand Tracking Under Occlusion From an Egocentric RGB-D Sensor

Franziska Mueller:

Dushyant Mehta:

Oleksandr Sotnychenko:

Srinath Sridhar:

Dan Casas:

Christian Theobalt:

【Object tracking】Learning Dynamic Siamese Network for Visual Object Tracking

Qing Guo:

Wei Feng:

Ce Zhou:

Rui Huang:

Liang Wan:

Song Wang:

【Object tracking】Non-Markovian Globally Consistent Multi-Object Tracking

Andrii Maksai:

Xinchao Wang:

François Fleuret:

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

【Object tracking】CREST: Convolutional Residual Learning for Visual Tracking

Yibing Song:

Chao Ma:

Lijun Gong:

Jiawei Zhang:

Rynson W. H. Lau:

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

【Object tracking】Detect to Track and Track to Detect

Christoph Feichtenhofer:

Axel Pinz:

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

【Object tracking】Beyond Standard Benchmarks: Parameterizing Performance Evaluation in Visual Object Tracking

Luka Čehovin Zajc:

Alan Lukežič:

Aleš Leonardis:

Matej Kristan:

【Object tracking】FCN-r LSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

Shanghang Zhang:

Guanhang Wu:

João P. Costeira:

José M. F. Moura:

【Object tracking】Drone-Based Object Counting by Spatially Regularized Regional Proposal Network

Meng-Ru Hsieh:

Yen-Liang Lin:

Winston H. Hsu:

【Object tracking】Online Multi-Object Tracking Using CNN-Based Single Object Tracker With Spatial-Temporal Attention Mechanism

Qi Chu:

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

Hongsheng Li:

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

Bin Liu:

Nenghai Yu:

【Object tracking】Mutual Enhancement for Detection of Multiple Logos in Sports Videos

Yuan Liao:

Xiaoqing Lu:

Chengcui Zhang:

Yongtao Wang:

Zhi Tang:

【Object tracking】Intrinsic 3D Dynamic Surface Tracking Based on Dynamic Ricci Flow and Teichmüller Map

Xiaokang Yu:

Na Lei:

Yalin Wang:

Xianfeng Gu:

【Object tracking】Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking

Heng Fan:

Haibin Ling: http://www.dabi.temple.edu/~hbling/

【Object tracking】Non-Rigid Object Tracking via Deformable Patches Using Shape-Preserved KCF and Level Sets

Xin Sun:

Ngai-Man Cheung:

Hongxun Yao:

Yiluan Guo:


【Action recognition】Encouraging LSTMs to Anticipate Actions Very Early

Mohammad Sadegh Aliakbarian:

Fatemeh Sadat Saleh:

Mathieu Salzmann:

Basura Fernando:

Lars Petersson:

Lars Andersson:

【Action recognition】Recurrent Models for Situation Recognition

Arun Mallya:

Svetlana Lazebnik: http://www.cs.illinois.edu/homes/slazebni/

【Action recognition】Towards Context-Aware Interaction Recognition for Visual Relationship Detection

Bohan Zhuang:

Lingqiao Liu:

Chunhua Shen:

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

【Action recognition】Unsupervised Action Discovery and Localization in Videos

Khurram Soomro:

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

【Action recognition】Dense-Captioning Events in Videos

Ranjay Krishna:

Kenji Hata:

Frederic Ren:

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

Juan Carlos Niebles:

【Action recognition】Learning Long-Term Dependencies for Action Recognition With a Biologically-Inspired Deep Network

Yemin Shi:

Yonghong Tian:

Yaowei Wang:

Wei Zeng:

Tiejun Huang: http://www.jdl.ac.cn/~tjhuang/index-en.html

【Action recognition】Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks

Inwoong Lee:

Doyoung Kim:

Seoungyoon Kang:

Sanghoon Lee:

【Action recognition】Predicting Human Activities Using Stochastic Grammar

Siyuan Qi:

Siyuan Huang:

Ping Wei:

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

【Action recognition】Adaptive RNN Tree for Large-Scale Human Action Recognition

Wenbo Li:

Longyin Wen:

Ming-Ching Chang:

Ser Nam Lim:

Siwei Lyu:

【Action recognition】Visual Relationship Detection With Internal and External Linguistic Knowledge Distillation

Ruichi Yu:

Ang Li:

Vlad I. Morariu:

Larry S. Davis: http://www.umiacs.umd.edu/~lsd/

【Action recognition】View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition From Skeleton Data

Pengfei Zhang:

Cuiling Lan:

Junliang Xing:

Wenjun Zeng:

Jianru Xue:

Nanning Zheng:

【Action recognition】Joint Discovery of Object States and Manipulation Actions

Jean-Baptiste Alayrac:

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

Josef Sivic:

Simon Lacoste-Julien:

【Action recognition】What Actions Are Needed for Understanding Human Actions in Videos?

Gunnar A. Sigurdsson:

Olga Russakovsky:

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

【Action recognition】Lattice Long Short-Term Memory for Human Action Recognition

Lin Sun:

Kui Jia:

Kevin Chen:

Dit-Yan Yeung: http://www.cse.ust.hk/~dyyeung/www/Home.html

Bertram E. Shi:

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

【Action recognition】Common Action Discovery and Localization in Unconstrained Videos

Jiong Yang:

Junsong Yuan:

【Action recognition】Am I a Baller? Basketball Performance Assessment From First-Person Videos

Gedas Bertasius:

Hyun Soo Park:

Stella X. Yu:

Jianbo Shi: http://www.cis.upenn.edu/~jshi/

【Action recognition】Playing for Benchmarks

Stephan R. Richter:

Zeeshan Hayder:

Vladlen Koltun: http://vladlen.info/publications/

【Action recognition】Dual-Glance Model for Deciphering Social Relationships

Junnan Li:

Yongkang Wong:

Qi Zhao:

Mohan S. Kankanhalli:

【Action recognition】SBGAR: Semantics Based Group Activity Recognition

Xin Li:

Mooi Choo Chuah:

【Action recognition】Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video

Davide Moltisanti:

Michael Wray:

Walterio Mayol-Cuevas:

Dima Damen:

【Action recognition】Unmasking the Abnormal Events in Video

Radu Tudor Ionescu:

Sorina Smeureanu:

Bogdan Alexe:

Marius Popescu:

【Action recognition】Chained Multi-Stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection

Mohammadreza Zolfaghari:

Gabriel L. Oliveira:

Nima Sedaghat:

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

【Action recognition】Temporal Action Detection With Structured Segment Networks

Yue Zhao:

Yuanjun Xiong:

Limin Wang:

Zhirong Wu:

Xiaoou Tang: http://mmlab.ie.cuhk.edu.hk/

Dahua Lin: http://dahua.me/

【Action recognition】Sub UNets: End-To-End Hand Shape and Continuous Sign Language Recognition

Necati Cihan Camgoz:

Simon Hadfield:

Oscar Koller:

Richard Bowden:

【Action recognition】Learning Hand Articulations by Hallucinating Heat Distribution

Chiho Choi:

Sangpil Kim:

Karthik Ramani:

【Action recognition】Robust Hand Pose Estimation During the Interaction With an Unknown Object

Chiho Choi:

Sang Ho Yoon:

Chin-Ning Chen:

Karthik Ramani:

【Action recognition】What Will Happen Next? Forecasting Player Moves in Sports Videos

Panna Felsen:

Pulkit Agrawal:

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

【Action recognition】Single Image Action Recognition Using Semantic Body Part Actions

Zhichen Zhao:

Huimin Ma:

Shaodi You:

【Action recognition】Monocular 3D Human Pose Estimation by Predicting Depth on Joints

Bruce Xiaohan Nie:

Ping Wei:

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

【Action recognition】Hide-And-Seek: Forcing a Network to Be Meticulous for Weakly-Supervised Object and Action Localization

Krishna Kumar Singh:

Yong Jae Lee:

【Action recognition】Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks

Swami Sankaranarayanan:

Arpit Jain:

Ser Nam Lim:

【Action recognition】Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge

Ryota Hinami:

Tao Mei:

Shin’ichi Satoh:

【Action recognition】TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals

Jiyang Gao:

Zhenheng Yang:

Kan Chen:

Chen Sun:

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

【Action recognition】Online Real-Time Multiple Spatiotemporal Action Localisation and Prediction

Gurkirt Singh:

Suman Saha:

Michael Sapienza:

Philip H. S. Torr: http://www.robots.ox.ac.uk/~tvg/

Fabio Cuzzolin:

【Action recognition】Leveraging Weak Semantic Relevance for Complex Video Event Classification

Chao Li:

Jiewei Cao:

Zi Huang:

Lei Zhu:

Heng Tao Shen:

【Action recognition】Weakly Supervised Summarization of Web Videos

Rameswar Panda:

Abir Das:

Ziyan Wu:

Jan Ernst:

Amit K. Roy-Chowdhury:

【Action recognition】First-Person Activity Forecasting With Online Inverse Reinforcement Learning

Nicholas Rhinehart:

Kris M. Kitani:

【Action recognition】RPAN: An End-To-End Recurrent Pose-Attention Network for Action Recognition in Videos

Wenbin Du:

Yali Wang:

Yu Qiao:

【Action recognition】Egocentric Gesture Recognition Using Recurrent 3D Convolutional Neural Networks With Spatiotemporal Transformer Modules

Congqi Cao:

Yifan Zhang:

Yi Wu:

Hanqing Lu: http://people.ucas.ac.cn/~luhanqing

Jian Cheng: http://www.nlpr.ia.ac.cn/jcheng/

【Action recognition】Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation

Bugra Tekin:

Pablo Márquez-Neila:

Mathieu Salzmann:

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

【Action recognition】Deep Facial Action Unit Recognition From Partially Labeled Data

Shan Wu:

Shangfei Wang:

Bowen Pan:

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

【Action recognition】Recognition of Action Units in the Wild With Deep Nets and a New Global-Local Loss
Fabian Benitez-Quiroz:

Yan Wang:

Aleix M. Martinez:

【Action recognition】PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN

Hanwang Zhang:

Zawlin Kyaw:

Jinyang Yu:

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

【Action recognition】Active Learning for Human Pose Estimation

Buyu Liu:

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

【Action recognition】Action Tubelet Detector for Spatio-Temporal Action Localization

Vicky Kalogeiton:

Philippe Weinzaepfel:

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

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

【Action recognition】AMTnet: Action-Micro-Tube Regression by End-To-End Trainable Deep Architecture

Suman Saha:

Gurkirt Singh:

Fabio Cuzzolin:

【Action recognition】Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions

Pascal Mettes:

Cees G. M. Snoek:

【Action recognition】TALL: Temporal Activity Localization via Language Query

Jiyang Gao:

Chen Sun:

Zhenheng Yang:

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

【Action recognition】Joint Prediction of Activity Labels and Starting Times in Untrimmed Videos

Tahmida Mahmud:

Mahmudul Hasan:

Amit K. Roy-Chowdhury:

【Action recognition】R-C3D: Region Convolutional 3D Network for Temporal Activity Detection

Huijuan Xu:

Abir Das:

Kate Saenko:

【Action recognition】Temporal Context Network for Activity Localization in Videos

Xiyang Dai:

Bharat Singh:

Guyue Zhang:

Larry S. Davis: http://www.umiacs.umd.edu/~lsd/

Yan Qiu Chen:

【Action recognition】Tube Convolutional Neural Network

Rui Hou:

Chen Chen:

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

【Action recognition】Learning Action Recognition Model From Depth and Skeleton Videos

Hossein Rahmani:

Mohammed Bennamoun: http://www.csse.uwa.edu.au/~bennamou/

【Crowd analysis】Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs

Vishwanath A. Sindagi:

Vishal M. Patel:

【Video analysis】Temporal Tessellation: A Unified Approach for Video Analysis

Dotan Kaufman:

Gil Levi:

Tal Hassner:

Lior Wolf:

【Video analysis】Temporal Tessellation: A Unified Approach for Video Analysis

Dotan Kaufman:

Gil Levi:

Tal Hassner:

Lior Wolf:

【Video analysis】A Read-Write Memory Network for Movie Story Understanding

Seil Na:

Sangho Lee:

Jisung Kim:

Gunhee Kim: http://www.cs.cmu.edu/~gunhee/index.html

【Video analysis】Complex Event Detection by Identifying Reliable Shots From Untrimmed Videos

Hehe Fan:

Xiaojun Chang:

De Cheng:

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

Dong Xu: http://www.ntu.edu.sg/home/dongxu/

Alexander G. Hauptmann:

【Video analysis】Video Fill in the Blank Using LR/RL LSTMs With Spatial-Temporal Attentions

Amir Mazaheri:

Dong Zhang:

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

【Video analysis】Unsupervised Video Understanding by Reconciliation of Posture Similarities

Timo Milbich:

Miguel Bautista:

Ekaterina Sutter:

Björn Ommer:

【Video analysis】Dynamic Label Graph Matching for Unsupervised Video Re-Identification

Mang Ye:

Andy J. Ma:

Liang Zheng:

Jiawei Li:

Pong C. Yuen:

【Video analysis】Spatiotemporal Modeling for Crowd Counting in Videos

Feng Xiong:

Xingjian Shi:

Dit-Yan Yeung: http://www.cse.ust.hk/~dyyeung/www/Home.html


【Human detection】Hydra Plus-Net: Attentive Deep Features for Pedestrian Analysis

Xihui Liu:

Haiyu Zhao:

Maoqing Tian:

Lu Sheng:

Jing Shao:

Shuai Yi:

Junjie Yan:

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

【Human detection】Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification

Zhongdao Wang:

Luming Tang:

Xihui Liu:

Zhuliang Yao:

Shuai Yi:

Jing Shao:

Junjie Yan:

Shengjin Wang:

Hongsheng Li:

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

【Human detection】Cross-View Asymmetric Metric Learning for Unsupervised Person Re-Identification

Hong-Xing Yu:

Ancong Wu:

Wei-Shi Zheng:

【Human detection】SHa PE: A Novel Graph Theoretic Algorithm for Making Consensus-Based Decisions in Person Re-Identification Systems

Arko Barman:

Shishir K. Shah:

【Human detection】A Two Stream Siamese Convolutional Neural Network for Person Re-Identification

Dahjung Chung:

Khalid Tahboub:

Edward J. Delp:

【Human detection】Joint Learning of Object and Action Detectors

Vicky Kalogeiton:

Philippe Weinzaepfel:

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

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

【Human detection】Efficient Online Local Metric Adaptation via Negative Samples for Person Re-Identification

Jiahuan Zhou:

Pei Yu:

Wei Tang:

Ying Wu:

【Human detection】Stepwise Metric Promotion for Unsupervised Video Person Re-Identification

Zimo Liu:

Dong Wang:

Huchuan Lu: http://ice.dlut.edu.cn/lu/index.html

【Human detection】Learning View-Invariant Features for Person Identification in Temporally Synchronized Videos Taken by Wearable Cameras

Kang Zheng:

Xiaochuan Fan:

Yuewei Lin:

Hao Guo:

Hongkai Yu:

Dazhou Guo:

Song Wang:

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

Liming Zhao:

Xi Li:

Yueting Zhuang:

Jingdong Wang:

【Human detection】Learning Visual Attention to Identify People With Autism Spectrum Disorder

Ming Jiang:

Qi Zhao:

【Human detection】Multi-Label Learning of Part Detectors for Heavily Occluded Pedestrian Detection

Chunluan Zhou:

Junsong Yuan:

【Human detection】Unlabeled Samples Generated by GAN Improve the Person Re-Identification Baseline in Vitro

Zhedong Zheng:

Liang Zheng:

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

【Human detection】Pose-Driven Deep Convolutional Model for Person Re-Identification

Chi Su:

Jianing Li:

Shiliang Zhang:

Junliang Xing:

Wen Gao: http://www.jdl.ac.cn/

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

【Human detection】Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-Identification

Shuangjie Xu:

Yu Cheng:

Kang Gu:

Yang Yang:

Shiyu Chang:

Pan Z

【Human detection】Illuminating Pedestrians via Simultaneous Detection & Segmentation

Garrick Brazil:

Xi Yin:

Xiaoming Liu:

【Human detection】RGB-Infrared Cross-Modality Person Re-Identification

Ancong Wu:

Wei-Shi Zheng:

Hong-Xing Yu:

Shaogang Gong: http://www.eecs.qmul.ac.uk/~sgg/

Jianhuang Lai:

【Human parsing】Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation

Matteo Ruggero Ronchi:

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

【Human parsing】Fashion Forward: Forecasting Visual Style in Fashion

Ziad Al-Halah:

Rainer Stiefelhagen:

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

【Human parsing】Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach

Xingyi Zhou:

Qixing Huang:

Xiao Sun:

Xiangyang Xue:

Yichen Wei:

【Human parsing】A Generative Model of People in Clothing

Christoph Lassner:

Gerard Pons-Moll:

Peter V. Gehler:

【Human parsing】Adversarial Pose Net: A Structure-Aware Convolutional Network for Human Pose Estimation

Yu Chen:

Chunhua Shen:

Xiu-Shen Wei:

Lingqiao Liu:

Jian Yang:

【Human parsing】Learning Feature Pyramids for Human Pose Estimation

Wei Yang:

Shuang Li:

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

Hongsheng Li:

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

【Human parsing】Automatic Spatially-Aware Fashion Concept Discovery

Xintong Han:

Zuxuan Wu:

Phoenix X. Huang:

Xiao Zhang:

Menglong Zhu:

Yuan Li:

Yang Zhao:

Larry S. Davis: http://www.umiacs.umd.edu/~lsd/

【Human parsing】Click Here: Human-Localized Keypoints as Guidance for Viewpoint Estimation

Ryan Szeto:

Jason J. Corso:

【Human parsing】Visual Transformation Aided Contrastive Learning for Video-Based Kinship Verification

Hamdi Dibeklioğlu:

【Human parsing】Compositional Human Pose Regression

Xiao Sun:

Jiaxiang Shang:

Shuang Liang:

Yichen Wei:

【Human parsing】A Simple yet Effective Baseline for 3D Human Pose Estimation

Julieta Martinez:

Rayat Hossain:

Javier Romero:

James J. Little:

【Human parsing】Transferring Objects: Joint Inference of Container and Human Pose

Hanqing Wang:

Wei Liang:

Lap-Fai Yu:

【Human parsing】Deep Globally Constrained MRFs for Human Pose Estimation

Ioannis Marras:

Petar Palasek:

Ioannis Patras:

【Human parsing】Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment With Limited Resources

Adrian Bulat:

Georgios Tzimiropoulos:

【Human parsing】SVDNet for Pedestrian Retrieval

Yifan Sun:

Liang Zheng:

Weijian Deng:

Shengjin Wang:

【Human parsing】Learning the Latent “Look”: Unsupervised Discovery of a Style-Coherent Embedding From Fashion Images

Wei-Lin Hsiao:

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

【Human parsing】Learning to Estimate 3D Hand Pose From Single RGB Images

Christian Zimmermann:

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

【Human parsing】Human Pose Estimation Using Global and Local Normalization

Ke Sun:

Cuiling Lan:

Junliang Xing:

Wenjun Zeng:

Dong Liu:

Jingdong Wang:

【Face recognition】Reconstruction-Based Disentanglement for Pose-Invariant Face Recognition

Xi Peng:

Xiang Yu:

Kihyuk Sohn:

Dimitris N. Metaxas:

Manmohan Chandraker:

【Face recognition】Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos

Kihyuk Sohn:

Sifei Liu:

Guangyu Zhong:

Xiang Yu:

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

Manmohan Chandraker:

【Face recognition】Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition

Chi Nhan Duong:

Kha Gia Quach:

Khoa Luu:

Ngan Le:

Marios Savvides:

【Face recognition】Attribute-Enhanced Face Recognition With Neural Tensor Fusion Networks

Guosheng Hu:

Yang Hua:

Yang Yuan:

Zhihong Zhang:

Zheng Lu:

Sankha S. Mukherjee:

Timothy M. Hospedales:

Neil M. Robertson:

Yongxin Yang:

【Face recognition】Recursive Spatial Transformer

Wanglong Wu:

Meina Kan:

Xin Liu:

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

Shiguang Shan: http://vipl.ict.ac.cn/members/sgshan

Xilin Chen:

【Face recognition】Learning Discriminative Aggregation Network for Video-Based Face Recognition

Yongming Rao:

Ji Lin:

Jiwen Lu:

Jie Zhou:

【Face recognition】Attention-Aware Deep Reinforcement Learning for Video Face Recognition

Yongming Rao:

Jiwen Lu:

Jie Zhou:

【Face recognition】Range Loss for Deep Face Recognition With Long-Tailed Training Data

Xiao Zhang:

Zhiyuan Fang:

Yandong Wen:

Zhifeng Li:

Yu Qiao:

【Face detection】S3FD: Single Shot Scale-Invariant Face Detector

Shifeng Zhang:

Xiangyu Zhu:

Zhen Lei:

Hailin Shi:

Xiaobo Wang:

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

【Face detection】Detecting Faces Using Inside Cascaded Contextual CNN

Kaipeng Zhang:

Zhanpeng Zhang:

Hao Wang:

Zhifeng Li:

Yu Qiao:

Wei Liu:

【Face detection】SSH: Single Stage Headless Face Detector

Mahyar Najibi:

Pouya Samangouei:

Rama Chellappa:

Larry S. Davis: http://www.umiacs.umd.edu/~lsd/

【Face detection】End-To-End Face Detection and Cast Grouping in Movies Using Erdős-Rényi Clustering

Sou Young Jin:

Hang Su:

Chris Stauffer:

Erik Learned-Miller:

【Face parsing】Real Time Eye Gaze Tracking With 3D Deformable Eye-Face Model

Kang Wang:

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

【Face parsing】How Far Are We From Solving the 2D & 3D Face Alignment Problem?

Adrian Bulat:

Georgios Tzimiropoulos:

【Face parsing】Following Gaze in Video

Adrià Recasens:

Carl Vondrick:

Aditya Khosla:

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

【Face parsing】Recurrent 3D-2D Dual Learning for Large-Pose Facial Landmark Detection

Shengtao Xiao:

Jiashi Feng:

Luoqi Liu:

Xuecheng Nie:

Wei Wang:

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

Ashraf Kassim:

【Face parsing】Monocular Free-Head 3D Gaze Tracking With Deep Learning and Geometry Constraints

Wangjiang Zhu:

Haoping Deng:

【Face parsing】A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition

Anis Kacem:

Mohamed Daoudi:

Boulbaba Ben Amor:

Juan Carlos Alvarez-Paiva:

【Face parsing】Deep Coder: Semi-Parametric Variational Autoencoders for Automatic Facial Action Coding

Dieu Linh Tran:

Robert Walecki:

Ognjen (Oggi) Rudovic:

Stefanos Eleftheriadis:

Björn Schuller:

Maja Pantic: http://ibug.doc.ic.ac.uk/research

【Face parsing】Pose-Invariant Face Alignment With a Single CNN

Amin Jourabloo:

Mao Ye:

Xiaoming Liu:

Liu Ren:

【Face parsing】Fast Face-Swap Using Convolutional Neural Networks

Iryna Korshunova:

Wenzhe Shi:

Joni Dambre:

Lucas Theis:

【Face parsing】Synergy Between Face Alignment and Tracking via Discriminative Global Consensus Optimization

Muhammad Haris Khan:

John Mc Donagh:

Georgios Tzimiropoulos:

【Face parsing】Towards More Accurate Iris Recognition Using Deeply Learned Spatially Corresponding Features

Zijing Zhao:

Ajay Kumar:

【Face parsing】Faster Than Real-Time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses

Chandrasekhar Bhagavatula:

Chenchen Zhu:

Khoa Luu:

Marios Savvides:

【Face parsing】Towards Large-Pose Face Frontalization in the Wild

Xi Yin:

Xiang Yu:

Kihyuk Sohn:

Xiaoming Liu:

Manmohan Chandraker:

【Face parsing】Learning Dense Facial Correspondences in Unconstrained Images

Ronald Yu:

Shunsuke Saito:

Haoxiang Li:

Duygu Ceylan:

Hao Li:

【Face parsing】Referring Expression Generation and Comprehension via Attributes

Jingyu Liu:

Liang Wang:

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

【Face parsing】Face Sketch Matching via Coupled Deep Transform Learning

Shruti Nagpal:

Maneet Singh:

Richa Singh:

Mayank Vatsa:

Afzel Noore:

Angshul Majumdar:

【Object recognition】Veg Fru: A Domain-Specific Dataset for Fine-Grained Visual Categorization

Saihui Hou:

Yushan Feng:

Zilei Wang:

【Object recognition】BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

Michael J. Wilber:

Chen Fang:

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

Aaron Hertzmann:

John Collomosse:

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

【Object recognition】Transitive Invariance for Self-Supervised Visual Representation Learning

Xiaolong Wang:

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

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

【Object recognition】Fine-Grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach

Timnit Gebru:

Judy Hoffman:

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

【Object recognition】SORT: Second-Order Response Transform for Visual Recognition

Yan Wang:

Lingxi Xie:

Chenxi Liu:

Siyuan Qiao:

Ya Zhang:

Wenjun Zhang:

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

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

【Object recognition】Infant Footprint Recognition

Eryun Liu:

【Object recognition】Learning Deep Neural Networks for Vehicle Re-ID With Visual-Spatio-Temporal Path Proposals

Yantao Shen:

Tong Xiao:

Hongsheng Li:

Shuai Yi:

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

【Object recognition】Jointly Recognizing Object Fluents and Tasks in Egocentric Videos

Yang Liu:

Ping Wei:

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

【Object recognition】Low-Shot Visual Recognition by Shrinking and Hallucinating Features

Bharath Hariharan:

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

【Object recognition】Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition

Anders Glent Buch:

Lilita Kiforenko:

Dirk Kraft:

【Object recognition】Weakly-Supervised Learning of Visual Relations

Julia Peyre:

Josef Sivic:

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

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

【Object recognition】Learning 3D Object Categories by Looking Around Them

David Novotny:

Diane Larlus:

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

【Object recognition】Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings

James Thewlis:

Hakan Bilen:

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

【Object detection】Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

Pierre Baqué:

François Fleuret:

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

【Object detection】A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework

Weixin Luo:

Wen Liu:

Shenghua Gao:

【Object detection】Flow-Guided Feature Aggregation for Video Object Detection

Xizhou Zhu:

Yujie Wang:

Jifeng Dai:

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

Yichen Wei:

【Object detection】De Net: Scalable Real-Time Object Detection With Directed Sparse Sampling

Lachlan Tychsen-Smith:

Lars Petersson:

【Object detection】Safety Net: Detecting and Rejecting Adversarial Examples Robustly

Jiajun Lu:

Theerasit Issaranon:

David Forsyth: http://luthuli.cs.uiuc.edu/~daf/

【Object detection】Recurrent Scale Approximation for Object Detection in CNN

Yu Liu:

Hongyang Li:

Junjie Yan:

Fangyin Wei:

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

Xiaoou Tang: http://mmlab.ie.cuhk.edu.hk/

【Object detection】Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection

Debidatta Dwibedi:

Ishan Misra:

Martial Hebert: http://www.cs.cmu.edu/~hebert/

【Object detection】Temporal Dynamic Graph LSTM for Action-Driven Video Object Detection

Yuan Yuan:

Xiaodan Liang:

Xiaolong Wang:

Dit-Yan Yeung: http://www.cse.ust.hk/~dyyeung/www/Home.html

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

【Object detection】Soft Proposal Networks for Weakly Supervised Object Localization

Yi Zhu:

Yanzhao Zhou:

Qixiang Ye:

Qiang Qiu:

Jianbin Jiao:

【Object detection】DSOD: Learning Deeply Supervised Object Detectors From Scratch

Zhiqiang Shen:

Zhuang Liu:

Jianguo Li:

Yu-Gang Jiang:

Yurong Chen:

Xiangyang Xue:

【Object detection】Phrase Localization and Visual Relationship Detection With Comprehensive Image-Language Cues

Bryan A. Plummer:

Arun Mallya:

Christopher M. Cervantes:

Julia Hockenmaier:

Svetlana Lazebnik: http://www.cs.illinois.edu/homes/slazebni/

【Object detection】Chained Cascade Network for Object Detection

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

Kun Wang:

Xin Zhu:

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

【Object detection】VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition

Seokju Lee:

Junsik Kim:

Jae Shin Yoon:

Seunghak Shin:

Oleksandr Bailo:

Namil Kim:

Tae-Hee Lee:

Hyun Seok Hong:

Seung-Hoon Han:

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

【Object detection】Online Video Object Detection Using Association LSTM

Yongyi Lu:

Cewu Lu:

Chi-Keung Tang:

【Object detection】Focal Loss for Dense Object Detection

Tsung-Yi Lin:

Priya Goyal:

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

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

Piotr Dollár: http://vision.ucsd.edu/~pdollar/

【Object detection】Weakly Supervised Object Localization Using Things and Stuff Transfer

Miaojing Shi:

Holger Caesar:

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

【Object detection】Incremental Learning of Object Detectors Without Catastrophic Forgetting

Konstantin Shmelkov:

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

Karteek Alahari:

【Object detection】Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors

Hong-Yu Zhou:

Bin-Bin Gao:

Jianxin Wu:

【Object detection】Two-Phase Learning for Weakly Supervised Object Localization

Dahun Kim:

Donghyeon Cho:

Donggeun Yoo:

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

【Object detection】Spatial Memory for Context Reasoning in Object Detection

Xinlei Chen:

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

【Object detection】Couple Net: Coupling Global Structure With Local Parts for Object Detection

Yousong Zhu:

Chaoyang Zhao:

Jinqiao Wang:

Xu Zhao:

Yi Wu:

Hanqing Lu: http://people.ucas.ac.cn/~luhanqing

【Object detection】2D-Driven 3D Object Detection in RGB-D Images

Jean Lahoud:

Bernard Ghanem:

【Object detection】Extreme Clicking for Efficient Object Annotation

Dim P. Papadopoulos:

Jasper R. R. Uijlings:

Frank Keller:

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

【Object detection】Exploiting Spatial Structure for Localizing Manipulated Image Regions

Jawadul H. Bappy:

Amit K. Roy-Chowdhury:

Jason Bunk:

Lakshmanan Nataraj:

S. Manjunath:

【Object detection】Moving Object Detection in Time-Lapse or Motion Trigger Image Sequences Using Low-Rank and Invariant Sparse Decomposition

Moein Shakeri:

Hong Zhang:

【Object detection】Multi-Scale Deep Learning Architectures for Person Re-Identification

Xuelin Qian:

Yanwei Fu:

Yu-Gang Jiang:

Tao Xiang:

Xiangyang Xue:

【Object detection】Monocular Video-Based Trailer Coupler Detection Using Multiplexer Convolutional Neural Network

Yousef Atoum:

Joseph Roth:

Michael Bliss:

Wende Zhang:

Xiaoming Liu:

【Object detection】Soft-NMS — Improving Object Detection With One Line of Code

Navaneeth Bodla:

Bharat Singh:

Rama Chellappa:

Larry S. Davis: http://www.umiacs.umd.edu/~lsd/

【Object detection】Adversarial Examples Detection in Deep Networks With Convolutional Filter Statistics

Xin Li:

Fuxin Li: http://www.cc.gatech.edu/~fli/

【Object detection】Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings

James Thewlis:

Hakan Bilen:

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

【Saliency detection】Amulet: Aggregating Multi-Level Convolutional Features for Salient Object Detection

Pingping Zhang:

Dong Wang:

Huchuan Lu: http://ice.dlut.edu.cn/lu/index.html

Hongyu Wang:

Xiang Ruan:

【Saliency detection】Learning Uncertain Convolutional Features for Accurate Saliency Detection

Pingping Zhang:

Dong Wang:

Huchuan Lu: http://ice.dlut.edu.cn/lu/index.html

Hongyu Wang:

Baocai Yin:

【Saliency detection】Look, Perceive and Segment: Finding the Salient Objects in Images via Two-Stream Fixation-Semantic CNNs

Xiaowu Chen: http://arts.buaa.edu.cn/index.htm

Anlin Zheng:

Jia Li:

Feng Lu:

【Saliency detection】Delving Into Salient Object Subitizing and Detection

Shengfeng He:

Jianbo Jiao:

Xiaodan Zhang:

Guoqiang Han:

Rynson W.H. Lau:

【Saliency detection】Learning Gaze Transitions From Depth to Improve Video Saliency Estimation

George Leifman:

Dmitry Rudoy:

Tristan Swedish:

Eduardo Bayro-Corrochano:

Ramesh Raskar:

【Saliency detection】Scale Net: Guiding Object Proposal Generation in Supermarkets and Beyond

Siyuan Qiao:

Wei Shen:

Weichao Qiu:

Chenxi Liu:

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

【Saliency detection】Unsupervised Learning of Important Objects From First-Person Videos

Gedas Bertasius:

Hyun Soo Park:

Stella X. Yu:

Jianbo Shi: http://www.cis.upenn.edu/~jshi/

【Saliency detection】Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images

Tribhuvanesh Orekondy:

Bernt Schiele: http://www.d2.mpi-inf.mpg.de/schiele/

Mario Fritz: https://scalable.mpi-inf.mpg.de/

【Saliency detection】A Stagewise Refinement Model for Detecting Salient Objects in Images

Tiantian Wang:

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

Lihe Zhang:

Pingping Zhang:

Huchuan Lu: http://ice.dlut.edu.cn/lu/index.html

【Saliency detection】Supervision by Fusion: Towards Unsupervised Learning of Deep Salient Object Detector

Dingwen Zhang:

Junwei Han:

Yu Zhang: http://www.comp.hkbu.edu.hk/~yuzhang/

【Saliency detection】Unsupervised Learning From Video to Detect Foreground Objects in Single Images

Ioana Croitoru:

Simion-Vlad Bogolin:

Marius Leordeanu:

【Saliency detection】Structure-Measure: A New Way to Evaluate Foreground Maps

Deng-Ping Fan:

Ming-Ming Cheng:

Yun Liu:

Tao Li:

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

【Saliency detection】Understanding Low- and High-Level Contributions to Fixation Prediction

Matthias Kümmerer:

Thomas S. A. Wallis:

Leon A. Gatys:

Matthias Bethge:

【Saliency detection】Object-Level Proposals

Jianxiang Ma:

Anlong Ming:

Zilong Huang:

Xinggang Wang:

Yu Zhou:

【Saliency detection】Generalized Orderless Pooling Performs Implicit Salient Matching

Marcel Simon:

Yang Gao:

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

Joachim Denzler:

Erik Rodner:

【Saliency detection】Saliency Pattern Detection by Ranking Structured Trees

Lei Zhu:

Haibin Ling: http://www.dabi.temple.edu/~hbling/

Jin Wu:

Huiping Deng:

Jin Liu:

【Saliency detection】Localizing Moments in Video With Natural Language

Lisa Anne Hendricks:

Oliver Wang:

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

Josef Sivic:

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

Bryan Russell:

【Saliency detection】TORNADO: A Spatio-Temporal Convolutional Regression Network for Video Action Proposal

Hongyuan Zhu:

Romain Vial:

Shijian Lu:

【Scene recognition】Multi-Label Image Recognition by Recurrently Discovering Attentional Regions

Zhouxia Wang:

Tianshui Chen:

Guanbin Li:

Ruijia Xu:

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

【Scene recognition】Deep Determinantal Point Process for Large-Scale Multi-Label Classification

Pengtao Xie:

Ruslan Salakhutdinov: http://www.cs.toronto.edu/~rsalakhu/

Luntian Mou:

Eric P. Xing:

【Scene recognition】Dual Net: Learn Complementary Features for Image Recognition

Saihui Hou:

Xu Liu:

Zilei Wang:

【Scene recognition】Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization

Sijia Cai:

Wangmeng Zuo:

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

【Scene recognition】Show, Adapt and Tell: Adversarial Training of Cross-Domain Image Captioner

Tseng-Hung Chen:

Yuan-Hong Liao:

Ching-Yao Chuang:

Wan-Ting Hsu:

Jianlong Fu:

Min Sun:

【Scene recognition】Attribute Recognition by Joint Recurrent Learning of Context and Correlation

Jingya Wang:

Xiatian Zhu:

Shaogang Gong: http://www.eecs.qmul.ac.uk/~sgg/

Wei Li:

【Scene recognition】Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization

Ramprasaath R. Selvaraju:

Michael Cogswell:

Abhishek Das:

Ramakrishna Vedantam:

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

Dhruv Batra:

【Scene recognition】Image-Based Localization Using LSTMs for Structured Feature Correlation

Florian Walch:

Caner Hazirbas:

Laura Leal-Taixé:

Torsten Sattler:

Sebastian Hilsenbeck:

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

【Scene recognition】Learning to Reason: End-To-End Module Networks for Visual Question Answering

Ronghang Hu:

Jacob Andreas:

Marcus Rohrbach:

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

Kate Saenko:

【Scene recognition】Improved Image Captioning via Policy Gradient Optimization of SPIDEr

Siqi Liu:

Zhenhai Zhu:

Ning Ye:

Sergio Guadarrama:

Kevin Murphy:

【Scene recognition】Deep Context: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding

Yinda Zhang:

Mingru Bai:

Pushmeet Kohli: http://research.microsoft.com/en-us/um/people/pkohli/

Shahram Izadi:

Jianxiong Xiao: http://vision.princeton.edu/people/xj/

【Scene recognition】An Empirical Study of Language CNN for Image Captioning

Jiuxiang Gu:

Gang Wang:

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

Tsuhan Chen: http://chenlab.ece.cornell.edu/

【Scene recognition】Attributes2Classname: A Discriminative Model for Attribute-Based Unsupervised Zero-Shot Learning

Berkan Demirel:

Ramazan Gokberk Cinbis:

Nazli Ikizler-Cinbis:

【Scene recognition】Areas of Attention for Image Captioning

Marco Pedersoli:

Thomas Lucas:

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

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

【Scene recognition】Structured Attentions for Visual Question Answering

Chen Zhu:

Yanpeng Zhao:

Shuaiyi Huang:

Kewei Tu:

Yi Ma: http://yima.csl.illinois.edu/

【Scene recognition】Multi-Modal Factorized Bilinear Pooling With Co-Attention Learning for Visual Question Answering

Zhou Yu:

Jun Yu:

Jianping Fan:

Dacheng Tao:

【Scene recognition】Identity-Aware Textual-Visual Matching With Latent Co-Attention

Shuang Li:

Tong Xiao:

Hongsheng Li:

Wei Yang:

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

【Scene recognition】An Analysis of Visual Question Answering Algorithms

Kushal Kafle:

Christopher Kanan:

【Scene recognition】Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption

Ryo Yonetani:

Vishnu Naresh Boddeti:

Kris M. Kitani:

Yoichi Sato:

【Scene recognition】Is Second-Order Information Helpful for Large-Scale Visual Recognition?

Peihua Li:

Jiangtao Xie:

Qilong Wang:

Wangmeng Zuo:

【Scene recognition】Factorized Bilinear Models for Image Recognition

Yanghao Li:

Naiyan Wang:

Jiaying Liu:

Xiaodi Hou: http://www.its.caltech.edu/~xhou/

【Scene recognition】Paying Attention to Descriptions Generated by Image Captioning Models

Hamed R. Tavakoli:

Rakshith Shetty:

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

Jorma Laaksonen:

【Scene recognition】MUTAN: Multimodal Tucker Fusion for Visual Question Answering

Hedi Ben-younes:

Remi Cadene:

Matthieu Cord:

Nicolas Thome:

【Scene recognition】Revisiting IM2GPS in the Deep Learning Era

Nam Vo:

Nathan Jacobs:

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

【Scene recognition】Mario QA: Answering Questions by Watching Gameplay Videos

Jonghwan Mun:

Paul Hongsuck Seo:

Ilchae Jung:

Bohyung Han: http://cvlab.postech.ac.kr/~bhhan/

【Scene recognition】Learning Cooperative Visual Dialog Agents With Deep Reinforcement Learning

Abhishek Das:

Satwik Kottur:

José M. F. Moura:

Stefan Lee:

Dhruv Batra:

【Scene recognition】Towards Diverse and Natural Image Descriptions via a Conditional GAN

Bo Dai:

Sanja Fidler:

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

Dahua Lin: http://dahua.me/

【Scene recognition】Learning to Disambiguate by Asking Discriminative Questions

Yining Li:

Chen Huang:

Xiaoou Tang: http://mmlab.ie.cuhk.edu.hk/

Chen Change Loy: http://www.eecs.qmul.ac.uk/~ccloy/

【Scene recognition】Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training

Rakshith Shetty:

Marcus Rohrbach:

Lisa Anne Hendricks:

Mario Fritz: https://scalable.mpi-inf.mpg.de/

Bernt Schiele: http://www.d2.mpi-inf.mpg.de/schiele/

【Scene recognition】Blitz Net: A Real-Time Deep Network for Scene Understanding

Nikita Dvornik:

Konstantin Shmelkov:

Julien Mairal:

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

【Scene recognition】Situation Recognition With Graph Neural Networks

Ruiyu Li:

Makarand Tapaswi:

Renjie Liao:

Jiaya Jia: http://www.cse.cuhk.edu.hk/leojia/

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

Sanja Fidler:

【Scene recognition】Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks

Tanmay Gupta:

Kevin Shih:

Saurabh Singh:

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

【Scene recognition】Learning Discriminative Latent Attributes for Zero-Shot Classification

Huajie Jiang:

Ruiping Wang:

Shiguang Shan: http://vipl.ict.ac.cn/members/sgshan

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

Xilin Chen:

【Scene recognition】Summarization and Classification of Wearable Camera Streams by Learning the Distributions Over Deep Features of Out-Of-Sample Image Sequences

Alessandro Perina:

Sadegh Mohammadi:

Nebojsa Jojic:

Vittorio Murino:

【Scene recognition】Boosting Image Captioning With Attributes

Ting Yao:

Yingwei Pan:

Yehao Li:

Zhaofan Qiu:

Tao Mei:

【Scene recognition】Nonparametric Variational Auto-Encoders for Hierarchical Representation Learning

Prasoon Goyal:

Zhiting Hu:

Xiaodan Liang:

Chenyu Wang:

Eric P. Xing:

【Scene recognition】A Multimodal Deep Regression Bayesian Network for Affective Video Content Analyses

Quan Gan:

Shangfei Wang:

Longfei Hao:

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

【Scene recognition】Learning Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition

Heliang Zheng:

Jianlong Fu:

Tao Mei:

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

【Scene recognition】Scene Categorization With Spectral Features

Salman H. Khan:

Munawar Hayat:

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

【Scene recognition】Deep Scene Image Classification With the MFAFVNet

Yunsheng Li:

Mandar Dixit:

Nuno Vasconcelos: http://www.svcl.ucsd.edu/

【Text recognition】Deep Direct Regression for Multi-Oriented Scene Text Detection

Wenhao He:

Xu-Yao Zhang:

Fei Yin:

Cheng-Lin Liu:

【Text recognition】We Text: Scene Text Detection Under Weak Supervision

Shangxuan Tian:

Shijian Lu:

Chongshou Li:

【Text recognition】Deep Text Spotter: An End-To-End Trainable Scene Text Localization and Recognition Framework

Michal Bušta:

Lukáš Neumann:

Jiří Matas:

【Text recognition】Single Shot Text Detector With Regional Attention

Pan He:

Weilin Huang:

Tong He:

Qile Zhu:

Yu Qiao:

Xiaolin Li:

【Text recognition】Neural Ctrl-F: Segmentation-Free Query-By-String Word Spotting in Handwritten Manuscript Collections

Tomas Wilkinson:

Jonas Lindström:

Anders Brun:

【Text recognition】Word Sup: Exploiting Word Annotations for Character Based Text Detection

Han Hu:

Chengquan Zhang:

Yuxuan Luo:

Yuzhuo Wang:

Junyu Han:

Errui Ding:

【Text recognition】Self-Organized Text Detection With Minimal Post-Processing via Border Learning

Yue Wu:

Prem Natarajan:

【Text recognition】Focusing Attention: Towards Accurate Text Recognition in Natural Images

Zhanzhan Cheng:

Fan Bai:

Yunlu Xu:

Gang Zheng:

Shiliang Pu:

Shuigeng Zhou:

【Text recognition】Towards End-To-End Text Spotting With Convolutional Recurrent Neural Networks

Hui Li:

Peng Wang:

Chunhua Shen:

【Text recognition】Offline Handwritten Signature Modeling and Verification Based on Archetypal Analysis

Elias N. Zois:

Ilias Theodorakopoulos:

George Economou:

【Image retrieval】MIHash: Online Hashing With Mutual Information

Fatih Cakir:

Kun He:

Sarah Adel Bargal:

Stan Sclaroff:

【Image retrieval】Neural Person Search Machines

Hao Liu:

Jiashi Feng:

Zequn Jie:

Karlekar Jayashree:

Bo Zhao:

Meibin Qi:

Jianguo Jiang:

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

【Image retrieval】Exploiting Multi-Grain Ranking Constraints for Precisely Searching Visually-Similar Vehicles

Ke Yan:

Yonghong Tian:

Yaowei Wang:

Wei Zeng:

Tiejun Huang: http://www.jdl.ac.cn/~tjhuang/index-en.html

【Image retrieval】Compressive Quantization for Fast Object Instance Search in Videos

Tan Yu:

Zhenzhen Wang:

Junsong Yuan:

【Image retrieval】Ensemble Diffusion for Retrieval

Song Bai:

Zhichao Zhou:

Jingdong Wang:

Xiang Bai:

Longin Jan Latecki:

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

【Image retrieval】SUBIC: A Supervised, Structured Binary Code for Image Search

Himalaya Jain:

Joaquin Zepeda:

Patrick Pérez:

Rémi Gribonval:

【Image retrieval】Spatio-Temporal Person Retrieval via Natural Language Queries

Masataka Yamaguchi:

Kuniaki Saito:

Yoshitaka Ushiku:

Tatsuya Harada:

【Image retrieval】Sketching With Style: Visual Search With Sketches and Aesthetic Context

John Collomosse:

Tu Bui:

Michael J. Wilber:

Chen Fang:

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

【Image retrieval】Toronto City: Seeing the World With a Million Eyes

Shenlong Wang:

Min Bai:

Gellért Máttyus:

Hang Chu:

Wenjie Luo:

Bin Yang:

Justin Liang:

Joel Cheverie:

Sanja Fidler:

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

【Image retrieval】Large-Scale Image Retrieval With Attentive Deep Local Features

Hyeonwoo Noh:

Andre Araujo:

Jack Sim:

Tobias Weyand:

Bohyung Han: http://cvlab.postech.ac.kr/~bhhan/

【Image retrieval】Low Compute and Fully Parallel Computer Vision With Hash Match

Sean Ryan Fanello:

Julien Valentin:

Adarsh Kowdle:

Christoph Rhemann:

Vladimir Tankovich:

Carlo Ciliberto:

Philip Davidson:

Shahram Izadi:

【Image retrieval】Fast Multi-Image Matching via Density-Based Clustering

Roberto Tron:

Xiaowei Zhou:

Carlos Esteves:

Kostas Daniilidis:

【Image retrieval】Cross-Modal Deep Variational Hashing

Venice Erin Liong:

Jiwen Lu:

Yap-Peng Tan:

Jie Zhou:

【Image retrieval】Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval

Yuming Shen:

Li Liu:

Ling Shao: http://lshao.staff.shef.ac.uk/

Jingkuan Song:

【Image retrieval】Learning a Recurrent Residual Fusion Network for Multimodal Matching

Yu Liu:

Yanming Guo:

Erwin M. Bakker:

Michael S. Lew:

【Image retrieval】Learning Visual N-Grams From Web Data

Ang Li:

Allan Jabri:

Armand Joulin:

Laurens van der Maaten:

【Image retrieval】Learning From Video and Text via Large-Scale Discriminative Clustering

Antoine Miech:

Jean-Baptiste Alayrac:

Piotr Bojanowski:

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

Josef Sivic:

【Image retrieval】Deep Adaptive Image Clustering

Jianlong Chang:

Lingfeng Wang:

Gaofeng Meng: http://www.escience.cn/people/menggaofeng/index.html

Shiming Xiang:

Chunhong Pan: http://people.gucas.ac.cn/~panchunhong

【Image retrieval】Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval

Jifei Song:

Qian Yu:

Yi-Zhe Song:

Tao Xiang:

Timothy M. Hospedales:

【Image retrieval】Hash Net: Deep Learning to Hash by Continuation

Zhangjie Cao:

Mingsheng Long:

Jianmin Wang:

Philip S. Yu:

【Image retrieval】Image2song: Song Retrieval via Bridging Image Content and Lyric Words

Xuelong Li:

Di Hu:

Xiaoqiang Lu:

【3D modeling】Rethinking Reprojection: Closing the Loop for Pose-Aware Shape Reconstruction From a Single Image

Rui Zhu:

Hamed Kiani Galoogahi:

Chaoyang Wang:

Simon Lucey:

【3D modeling】High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

Xiaoguang Han:

Zhen Li:

Haibin Huang:

Evangelos Kalogerakis:

Yizhou Yu: http://i.cs.hku.hk/~yzyu/

【3D modeling】CAD Priors for Accurate and Flexible Instance Reconstruction

Tolga Birdal:

Slobodan Ilic:

【3D modeling】Joint Layout Estimation and Global Multi-View Registration for Indoor Reconstruction

Jeong-Kyun Lee:

Jaewon Yea:

Min-Gyu Park:

Kuk-Jin Yoon: https://cvl.gist.ac.kr/introduction.html

【3D modeling】Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild

Christopher Funk:

Yanxi Liu:

【3D modeling】Escape From Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models

Roman Klokov:

Victor Lempitsky:

【3D modeling】Local-To-Global Point Cloud Registration Using a Dictionary of Viewpoint Descriptors

David Avidar:

David Malah:

Meir Barzohar:

【3D modeling】Body Fusion: Real-Time Capture of Human Motion and Surface Geometry Using a Single Depth Camera

Tao Yu:

Kaiwen Guo:

Feng Xu:

Yuan Dong:

Zhaoqi Su:

Jianhui Zhao:

Jianguo Li:

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

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

【3D modeling】Quasiconvex Plane Sweep for Triangulation With Outliers

Qianggong Zhang:

Tat-Jun Chin:

David Suter:

【3D modeling】”Maximizing Rigidity” Revisited: A Convex Programming Approach for Generic 3D Shape Reconstruction From Multiple Perspective Views

Pan Ji:

Hongdong Li:

Yuchao Dai:

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

【3D modeling】Large Pose 3D Face Reconstruction From a Single Image via Direct Volumetric CNN Regression

Aaron S. Jackson:

Adrian Bulat:

Vasileios Argyriou:

Georgios Tzimiropoulos:

【3D modeling】Generative Modeling of Audible Shapes for Object Perception

Zhoutong Zhang:

Jiajun Wu:

Qiujia Li:

Zhengjia Huang:

James Traer:

Josh H. Mc Dermott:

Joshua B. Tenenbaum:

William T. Freeman: http://people.csail.mit.edu/billf/

【3D modeling】Unsupervised Creation of Parameterized Avatars

Lior Wolf:

Yaniv Taigman:

Adam Polyak:

【3D modeling】Learning for Active 3D Mapping

Karel Zimmermann:

Tomáš Petříček:

Vojtěch Šalanský:

Tomáš Svoboda:

【3D modeling】Surface Normals in the Wild

Weifeng Chen:

Donglai Xiang:

Jia Deng:

【3D modeling】Unrestricted Facial Geometry Reconstruction Using Image-To-Image Translation

Matan Sela:

Elad Richardson:

Ron Kimmel:

【3D modeling】Raster-To-Vector: Revisiting Floorplan Transformation

Chen Liu:

Jiajun Wu:

Pushmeet Kohli: http://research.microsoft.com/en-us/um/people/pkohli/

Yasutaka Furukawa:

【3D modeling】Poly Fit: Polygonal Surface Reconstruction From Point Clouds

Liangliang Nan:

Peter Wonka:

【3D modeling】Multi-View Non-Rigid Refinement and Normal Selection for High Quality 3D Reconstruction

Sk. Mohammadul Haque:

Venu Madhav Govindu:

【3D modeling】Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization With Spatially-Varying Lighting

Robert Maier:

Kihwan Kim:

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

Jan Kautz:

Matthias Nießner:

【3D modeling】Mo FA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction

Ayush Tewari:

Michael Zollhöfer:

Hyeongwoo Kim:

Pablo Garrido:

Florian Bernard:

Patrick Pérez:

Christian Theobalt:

【3D modeling】Modeling Urban Scenes From Pointclouds

William Nguatem:

Helmut Mayer:

【3D modeling】From Point Clouds to Mesh Using Regression

Ľubor Ladický:

Olivier Saurer:

So Hyeon Jeong:

Fabio Maninchedda:

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

【3D modeling】Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks

Feihu Zhang:

Benjamin W. Wah:

【3D modeling】Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings

Sara Shaheen:

Lama Affara:

Bernard Ghanem:

【3D modeling】Monocular Dense 3D Reconstruction of a Complex Dynamic Scene From Two Perspective Frames

Suryansh Kumar:

Yuchao Dai:

Hongdong Li:

【3D modeling】Taking the Scenic Route to 3D: Optimising Reconstruction From Moving Cameras

Oscar Mendez:

Simon Hadfield:

Nicolas Pugeault:

Richard Bowden:

【3D modeling】Room Net: End-To-End Room Layout Estimation

Chen-Yu Lee:

Vijay Badrinarayanan:

Tomasz Malisiewicz: http://people.csail.mit.edu/tomasz/

Andrew Rabinovich:

【Feature matching】Learning Compact Geometric Features

Marc Khoury:

Qian-Yi Zhou:

Vladlen Koltun: http://vladlen.info/publications/

【Feature matching】Chroma Tag: A Colored Marker and Fast Detection Algorithm

Joseph De Gol:

Timothy Bretl:

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

【Feature matching】A Coarse-Fine Network for Keypoint Localization

Shaoli Huang:

Mingming Gong:

Dacheng Tao:

【Feature matching】Learning Spread-Out Local Feature Descriptors

Xu Zhang:

Felix X. Yu:

Sanjiv Kumar:

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

【Motion estimation】Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence

Dylan Campbell:

Lars Petersson:

Laurent Kneip:

Hongdong Li:

【Motion estimation】Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus

Runze Zhang:

Siyu Zhu:

Tian Fang:

Long Quan: http://visgraph.cs.ust.hk/index.html

【Motion estimation】Practical Projective Structure From Motion

Ludovic Magerand:

Alessio Del Bue:

【Motion estimation】Anticipating Daily Intention Using On-Wrist Motion Triggered Sensing

Tz-Ying Wu:

Ting-An Chien:

Cheng-Sheng Chan:

Chan-Wei Hu:

Min Sun:

【Motion estimation】Using Sparse Elimination for Solving Minimal Problems in Computer Vision

Janne Heikkilä:

【Motion estimation】Real-Time Monocular Pose Estimation of 3D Objects Using Temporally Consistent Local Color Histograms

Henning Tjaden:

Ulrich Schwanecke:

Elmar Schömer:

【Motion estimation】Colored Point Cloud Registration Revisited

Jaesik Park:

Qian-Yi Zhou:

Vladlen Koltun: http://vladlen.info/publications/

【Motion estimation】Path Track: Fast Trajectory Annotation With Path Supervision

Santiago Manen:

Michael Gygli:

Dengxin Dai:

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

【Motion estimation】Surface Registration via Foliation

Xiaopeng Zheng:

Chengfeng Wen:

Na Lei:

Ming Ma:

Xianfeng Gu:

【Motion estimation】Rolling-Shutter-Aware Differential Sf M and Image Rectification

Bingbing Zhuang:

Loong-Fah Cheong:

Gim Hee Lee:

【Motion estimation】Corner-Based Geometric Calibration of Multi-Focus Plenoptic Cameras

Sotiris Nousias:

François Chadebecq:

Jonas Pichat:

Pearse Keane:

Sébastien Ourselin:

Christos Bergeles:

【Motion estimation】SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again

Wadim Kehl:

Fabian Manhardt:

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

Slobodan Ilic:

Nassir Navab: http://campar.in.tum.de/Main/NassirNavab

【Motion estimation】Learned Multi-Patch Similarity

Wilfried Hartmann:

Silvano Galliani:

Michal Havlena:

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

Konrad Schindler:

【Motion estimation】Modelling the Scene Dependent Imaging in Cameras With a Deep Neural Network

Seonghyeon Nam:

Seon Joo Kim:

【Motion estimation】Making Minimal Solvers for Absolute Pose Estimation Compact and Robust

Viktor Larsson:

Zuzana Kukelova:

Yinqiang Zheng:

【Motion estimation】RMPE: Regional Multi-Person Pose Estimation

Hao-Shu Fang:

Shuqin Xie:

Yu-Wing Tai:

Cewu Lu:

【Motion estimation】Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map

Liu Liu:

Hongdong Li:

Yuchao Dai:

【Motion estimation】Low-Dimensionality Calibration Through Local Anisotropic Scaling for Robust Hand Model Personalization

Edoardo Remelli:

Anastasia Tkach:

Andrea Tagliasacchi:

Mark Pauly:

【Motion estimation】Probabilistic Structure From Motion With Objects

Paul Gay:

Cosimo Rubino:

Vaibhav Bansal:

Alessio Del Bue:

【Motion estimation】BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects Without Using Depth

Mahdi Rad:

Vincent Lepetit: http://cvlabwww.epfl.ch/~lepetit/

【Motion estimation】Parameter-Free Lens Distortion Calibration of Central Cameras

Filippo Bergamasco:

Luca Cosmo:

Andrea Gasparetto:

Andrea Albarelli:

Andrea Torsello:

【Motion estimation】Pose Guided RGBD Feature Learning for 3D Object Pose Estimation

Vassileios Balntas:

Andreas Doumanoglou:

Caner Sahin:

Juil Sock:

Rigas Kouskouridas:

Tae-Kyun Kim:

【Motion estimation】Dense Non-Rigid Structure-From-Motion and Shading With Unknown Albedos

Mathias Gallardo:

Toby Collins:

Adrien Bartoli:

【Motion estimation】Stereo DSO: Large-Scale Direct Sparse Visual Odometry With Stereo Cameras

Rui Wang:

Martin Schwörer:

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

【Motion estimation】Space-Time Localization and Mapping

Minhaeng Lee:

Charless C. Fowlkes:

【Motion estimation】Self-Supervised Learning of Pose Embeddings From Spatiotemporal Relations in Videos

Ömer Sümer:

Tobias Dencker:

Björn Ommer:

【Motion estimation】Visual Odometry for Pixel Processor Arrays

Laurie Bose:

Jianing Chen:

Stephen J. Carey:

Piotr Dudek:

Walterio Mayol-Cuevas:

【Motion estimation】Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution From a Blurred Image Sequence

Haesol Park:

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

【Motion estimation】Ray Space Features for Plenoptic Structure-From-Motion

Yingliang Zhang:

Peihong Yu:

Wei Yang:

Yuanxi Ma:

Jingyi Yu:

【Motion estimation】Efficient Algorithms for Moral Lineage Tracing

Markus Rempfler:

Jan-Hendrik Lange:

Florian Jug:

Corinna Blasse:

Eugene W. Myers:

Bjoern H. Menze:

Bjoern Andres:

【Motion estimation】Refractive Structure-From-Motion Through a Flat Refractive Interface

François Chadebecq:

Francisco Vasconcelos:

George Dwyer:

René Lacher:

Sébastien Ourselin:

Tom Vercauteren:

Danail Stoyanov:

【Motion estimation】Submodular Trajectory Optimization for Aerial 3D Scanning

Mike Roberts:

Debadeepta Dey:

Anh Truong:

Sudipta Sinha:

Shital Shah:

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

Pat Hanrahan:

Neel Joshi:

【Motion estimation】Camera Calibration by Global Constraints on the Motion of Silhouettes

Gil Ben-Artzi:

【Motion estimation】Deltille Grids for Geometric Camera Calibration

Hyowon Ha:

Michal Perdoch:

Hatem Alismail:

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

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

【Motion estimation】A Lightweight Single-Camera Polarization Compass With Covariance Estimation

Wolfgang Stürzl:

【Motion estimation】Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization

Huseyin Coskun:

Felix Achilles:

Robert Di Pietro:

Nassir Navab: http://campar.in.tum.de/Main/NassirNavab

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

【Motion estimation】Flip-Invariant Motion Representation

Takumi Kobayashi: https://staff.aist.go.jp/takumi.kobayashi/index.html

【Stereo matching】Robust Pseudo Random Fields for Light-Field Stereo Matching

Chao-Tsung Huang:

【Stereo matching】End-To-End Learning of Geometry and Context for Deep Stereo Regression

Alex Kendall:

Hayk Martirosyan:

Saumitro Dasgupta:

Peter Henry:

Ryan Kennedy:

Abraham Bachrach:

Adam Bry:

【Stereo matching】Weakly Supervised Learning of Deep Metrics for Stereo Reconstruction

Stepan Tulyakov:

Anton Ivanov:

François Fleuret:

【Stereo matching】Toward Perceptually-Consistent Stereo: A Scanline Study

Jialiang Wang:

Daniel Glasner:

Todd Zickler:

【Stereo matching】Unsupervised Learning of Stereo Matching

Chao Zhou:

Hong Zhang:

Xiaoyong Shen:

Jiaya Jia: http://www.cse.cuhk.edu.hk/leojia/

【Stereo matching】Unsupervised Adaptation for Deep Stereo

Alessio Tonioni:

Matteo Poggi:

Stefano Mattoccia:

Luigi Di Stefano:

【Stereo matching】Composite Focus Measure for High Quality Depth Maps

Parikshit Sakurikar:

J. Narayanan:

【Stereo matching】Depth and Image Restoration From Light Field in a Scattering Medium

Jiandong Tian:

Zachary Murez:

Tong Cui:

Zhen Zhang:

David Kriegman:

Ravi Ramamoorthi:

【Stereo matching】A Two-Streamed Network for Estimating Fine-Scaled Depth Maps From Single RGB Images

Jun Li:

Reinhard Klein:

Angela Yao:

【Stereo matching】Depth Estimation Using Structured Light Flow — Analysis of Projected Pattern Flow on an Object’s Surface

Ryo Furukawa:

Ryusuke Sagawa:

Hiroshi Kawasaki:

【Stereo matching】Scale Recovery for Monocular Visual Odometry Using Depth Estimated With Deep Convolutional Neural Fields

Xiaochuan Yin:

Xiangwei Wang:

Xiaoguo Du:

Qijun Chen:

【Optical flow】Mirror Flow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation

Junhwa Hur:

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

【Optical flow】Prob Flow: Joint Optical Flow and Uncertainty Estimation

Anne S. Wannenwetsch:

Margret Keuper:

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

【Optical flow】Volumetric Flow Estimation for Incompressible Fluids Using the Stationary Stokes Equations

Katrin Lasinger:

Christoph Vogel:

Konrad Schindler:

【Optical flow】Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?

Aseem Behl:

Omid Hosseini Jafari:

Siva Karthik Mustikovela:

Hassan Abu Alhaija:

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

Andreas Geiger:

【Optical flow】Video Frame Synthesis Using Deep Voxel Flow

Ziwei Liu:

Raymond A. Yeh:

Xiaoou Tang: http://mmlab.ie.cuhk.edu.hk/

Yiming Liu:

Aseem Agarwala:

【Optical flow】DCTM: Discrete-Continuous Transformation Matching for Semantic Flow

Seungryong Kim:

Dongbo Min:

Stephen Lin:

Kwanghoon Sohn:

【Optical flow】Learning Spatio-Temporal Representation With Pseudo-3D Residual Networks

Zhaofan Qiu:

Ting Yao:

Tao Mei:

【Region matching】Online Robust Image Alignment via Subspace Learning From Gradient Orientations

Qingqing Zheng:

Yi Wang:

Pheng-Ann Heng:

【Region matching】Progressive Large Scale-Invariant Image Matching in Scale Space

Lei Zhou:

Siyu Zhu:

Tianwei Shen:

Jinglu Wang:

Tian Fang:

Long Quan: http://visgraph.cs.ust.hk/index.html

【Region matching】Point Set Registration With Global-Local Correspondence and Transformation Estimation

Su Zhang:

Yang Yang:

Kun Yang:

Yi Luo:

Sim-Heng Ong:

【Region matching】Deep CD: Learning Deep Complementary Descriptors for Patch Representations

Tsun-Yi Yang:

Jo-Han Hsu:

Yen-Yu Lin:

Yung-Yu Chuang:

【Region matching】Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering

Matteo Denitto:

Simone Melzi:

Manuele Bicego:

Umberto Castellani:

Alessandro Farinelli:

Mário A. T. Figueiredo:

Yanir Kleiman:

Maks Ovsjanikov: http://www.lix.polytechnique.fr/~maks/publications.html

【Region matching】Practical and Efficient Multi-View Matching

Eleonora Maset:

Federica Arrigoni:

Andrea Fusiello:

【Image editing】Temporal Shape Super-Resolution by Intra-Frame Motion Encoding Using High-Fps Structured Light

Yuki Shiba:

Satoshi Ono:

Ryo Furukawa:

Shinsaku Hiura:

Hiroshi Kawasaki:

【Image editing】Temporal Shape Super-Resolution by Intra-Frame Motion Encoding Using High-Fps Structured Light

Yuki Shiba:

Satoshi Ono:

Ryo Furukawa:

Shinsaku Hiura:

Hiroshi Kawasaki:

【Image editing】Zero-Order Reverse Filtering

Xin Tao:

Chao Zhou:

Xiaoyong Shen:

Jue Wang: http://www.juew.org/

Jiaya Jia: http://www.cse.cuhk.edu.hk/leojia/

【Image editing】Learning Blind Motion Deblurring

Patrick Wieschollek:

Michael Hirsch:

Bernhard Schölkopf:

Hendrik P. A. Lensch:

【Image editing】Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising

Bihan Wen:

Yanjun Li:

Luke Pfister:

Yoram Bresler:

【Image editing】Learning to Super-Resolve Blurry Face and Text Images

Xiangyu Xu:

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

Jinshan Pan:

Yujin Zhang:

Hanspeter Pfister:

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

【Image editing】Video Frame Interpolation via Adaptive Separable Convolution

Simon Niklaus:

Long Mai:

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

【Image editing】Rank IQA: Learning From Rankings for No-Reference Image Quality Assessment

Xialei Liu:

Joost van de Weijer:

Andrew D. Bagdanov:

【Image editing】Learning Discriminative Data Fitting Functions for Blind Image Deblurring

Jinshan Pan:

Jiangxin Dong:

Yu-Wing Tai:

Zhixun Su:

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

【Image editing】On-Demand Learning for Deep Image Restoration

Ruohan Gao:

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

【Image editing】Multi-Channel Weighted Nuclear Norm Minimization for Real Color Image Denoising

Jun Xu:

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

David Zhang: http://www4.comp.polyu.edu.hk/~csdzhang/

Xiangchu Feng:

【Image editing】Coherent Online Video Style Transfer

Dongdong Chen:

Jing Liao:

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

Nenghai Yu:

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

【Image editing】Photographic Image Synthesis With Cascaded Refinement Networks

Qifeng Chen:

Vladlen Koltun: http://vladlen.info/publications/

【Image editing】Anchored Regression Networks Applied to Age Estimation and Super Resolution

Eirikur Agustsson:

Radu Timofte:

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

【Image editing】Self-Paced Kernel Estimation for Robust Blind Image Deblurring

Dong Gong:

Mingkui Tan:

Yanning Zhang:

Anton van den Hengel:

Qinfeng Shi:

【Image editing】Wavelet-SRNet: A Wavelet-Based CNN for Multi-Scale Face Super Resolution

Huaibo Huang:

Ran He:

Zhenan Sun:

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

【Image editing】Transformed Low-Rank Model for Line Pattern Noise Removal

Yi Chang:

Luxin Yan:

Sheng Zhong:

【Image editing】Shape Inpainting Using 3D Generative Adversarial Network and Recurrent Convolutional Networks

Weiyue Wang:

Qiangui Huang:

Suya You:

Chao Yang:

Ulrich Neumann:

【Image editing】Surface Net: An End-To-End 3D Neural Network for Multiview Stereopsis

Mengqi Ji:

Juergen Gall: http://www.iai.uni-bonn.de/~gall/

Haitian Zheng:

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

Lu Fang: http://staff.ustc.edu.cn/~fanglu/

【Image editing】3D Surface Detail Enhancement From a Single Normal Map

Wuyuan Xie:

Miaohui Wang:

Xianbiao Qi:

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

【Image editing】Blind Image Deblurring With Outlier Handling

Jiangxin Dong:

Jinshan Pan:

Zhixun Su:

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

【Image editing】Fast Image Processing With Fully-Convolutional Networks

Qifeng Chen:

Jia Xu:

Vladlen Koltun: http://vladlen.info/publications/

【Image editing】Robust Video Super-Resolution With Learned Temporal Dynamics

Ding Liu:

Zhaowen Wang:

Yuchen Fan:

Xianming Liu:

Zhangyang Wang:

Shiyu Chang:

Thomas Huang:

【Image editing】Filter Selection for Hyperspectral Estimation

Boaz Arad:

Ohad Ben-Shahar:

【Image editing】Non-Uniform Blind Deblurring by Reblurring

Yuval Bahat:

Netalee Efrat:

Michal Irani: http://www.wisdom.weizmann.ac.il/~irani/

【Image editing】Misalignment-Robust Joint Filter for Cross-Modal Image Pairs

Takashi Shibata:

Masayuki Tanaka:

Masatoshi Okutomi:

【Image editing】Makeup-Go: Blind Reversion of Portrait Edit

Ying-Cong Chen:

Xiaoyong Shen:

Jiaya Jia: http://www.cse.cuhk.edu.hk/leojia/

【Image editing】Shadow Detection With Conditional Generative Adversarial Networks

Vu Nguyen:

Tomas F. Yago Vicente:

Maozheng Zhao:

Minh Hoai:

Dimitris Samaras:

【Image editing】Mem Net: A Persistent Memory Network for Image Restoration

Ying Tai:

Jian Yang:

Xiaoming Liu:

Chunyan Xu:

【Image editing】Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting

Donghyeon Cho:

Jinsun Park:

Tae-Hyun Oh:

Yu-Wing Tai:

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

【Image editing】Learning to Push the Limits of Efficient FFT-Based Image Deconvolution

Jakob Kruse:

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

Uwe Schmidt:

【Image editing】Automatic Content-Aware Projection for 360° Videos

Yeong Won Kim:

Chang-Ryeol Lee:

Dae-Yong Cho:

Yong Hoon Kwon:

Hyeok-Jae Choi:

Kuk-Jin Yoon: https://cvl.gist.ac.kr/introduction.html

【Image editing】Blur-Invariant Deep Learning for Blind-Deblurring

M. Nimisha:

Akash Kumar Singh:

N. Rajagopalan:

【Image editing】Non-Linear Convolution Filters for CNN-Based Learning

Georgios Zoumpourlis:

Alexandros Doumanoglou:

Nicholas Vretos:

Petros Daras:

【Image editing】AOD-Net: All-In-One Dehazing Network

Boyi Li:

Xiulian Peng:

Zhangyang Wang:

Jizheng Xu:

Dan Feng:

【Image editing】Simultaneous Detection and Removal of High Altitude Clouds From an Image

Tushar Sandhan:

Jin Young Choi:

【Image editing】Image Super-Resolution Using Dense Skip Connections

Tong Tong:

Gen Li:

Xiejie Liu:

Qinquan Gao:

【Image editing】Blob Reconstruction Using Unilateral Second Order Gaussian Kernels With Application to High-ISO Long-Exposure Image Denoising

Gang Wang:

Carlos Lopez-Molina:

Bernard De Baets:

【Image editing】Deep Generative Adversarial Compression Artifact Removal

Leonardo Galteri:

Lorenzo Seidenari:

Marco Bertini:

Alberto Del Bimbo:

【Image editing】Estimating Defocus Blur via Rank of Local Patches

Guodong Xu:

Yuhui Quan:

Hui Ji:

【Image editing】Pixel Recursive Super Resolution

Ryan Dahl:

Mohammad Norouzi:

Jonathon Shlens:

【Image editing】Semi-Global Weighted Least Squares in Image Filtering

Wei Liu:

Xiaogang Chen:

Chuanhua Shen:

Zhi Liu:

Jie Yang:

【Computational photography】A Lightweight Approach for On-The-Fly Reflectance Estimation

Kihwan Kim:

Jinwei Gu:

Stephen Tyree:

Pavlo Molchanov:

Matthias Nießner:

Jan Kautz:

【Computational photography】An Optimal Transportation Based Univariate Neuroimaging Index

Liang Mi:

Wen Zhang:

Junwei Zhang:

Yonghui Fan:

Dhruman Goradia:

Kewei Chen:

Eric M. Reiman:

Xianfeng Gu:

Yalin Wang:

【Computational photography】Reasoning About Fine-Grained Attribute Phrases Using Reference Games

Jong-Chyi Su:

Chenyun Wu:

Huaizu Jiang:

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

【Computational photography】Personalized Image Aesthetics

Jian Ren:

Xiaohui Shen:

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

Radomír Měch:

David J. Foran:

【Computational photography】Rolling Shutter Correction in Manhattan World

Pulak Purkait:

Christopher Zach:

Aleš Leonardis:

【Computational photography】3D-PRNN: Generating Shape Primitives With Recurrent Neural Networks

Chuhang Zou:

Ersin Yumer:

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

Duygu Ceylan:

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

【Computational photography】Focal Track: Depth and Accommodation With Oscillating Lens Deformation

Qi Guo:

Emma Alexander:

Todd Zickler:

【Computational photography】Reconfiguring the Imaging Pipeline for Computer Vision

Mark Buckler:

Suren Jayasuriya:

Adrian Sampson:

【Computational photography】Catadioptric Hyper Spectral Light Field Imaging

Yujia Xue:

Kang Zhu:

Qiang Fu:

Xilin Chen:

Jingyi Yu:

【Computational photography】Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel

Wenqi Ren:

Jinshan Pan:

Xiaochun Cao:

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

【Computational photography】Scene Graph Generation From Objects, Phrases and Region Captions

Yikang Li:

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

Bolei Zhou:

Kun Wang:

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

【Computational photography】Arbitrary Style Transfer in Real-Time With Adaptive Instance Normalization

Xun Huang:

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

【Computational photography】Be Your Own Prada: Fashion Synthesis With Structural Coherence

Shizhan Zhu:

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

Sanja Fidler:

Dahua Lin: http://dahua.me/

Chen Change Loy: http://www.eecs.qmul.ac.uk/~ccloy/

【Computational photography】Pan Net: A Deep Network Architecture for Pan-Sharpening

Junfeng Yang:

Xueyang Fu:

Yuwen Hu:

Yue Huang:

Xinghao Ding:

John Paisley:

【Computational photography】Dual Motion GAN for Future-Flow Embedded Video Prediction

Xiaodan Liang:

Lisa Lee:

Wei Dai:

Eric P. Xing:

【Computational photography】Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems

Tim Meinhardt:

Michael Möller:

Caner Hazirbas:

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

【Computational photography】See the Glass Half Full: Reasoning About Liquid Containers, Their Volume and Content

Roozbeh Mottaghi: http://www.cs.stanford.edu/~roozbeh/

Connor Schenck:

Dieter Fox:

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

【Computational photography】Deep Cropping via Attention Box Prediction and Aesthetics Assessment

Wenguan Wang:

Jianbing Shen: http://cs.bit.edu.cn/shenjianbing/

【Computational photography】Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks

Jun-Yan Zhu:

Taesung Park:

Phillip Isola:

Alexei A. Efros:

【Computational photography】GANs for Biological Image Synthesis

Anton Osokin:

Anatole Chessel:

Rafael E. Carazo Salas:

Federico Vaggi:

【Computational photography】Learning to Synthesize a 4D RGBD Light Field From a Single Image

Pratul P. Srinivasan:

Tongzhou Wang:

Ashwin Sreelal:

Ravi Ramamoorthi:

Ren Ng:

【Computational photography】Neural EPI-Volume Networks for Shape From Light Field

Stefan Heber:

Wei Yu:

Thomas Pock:

【Computational photography】Material Editing Using a Physically Based Rendering Network

Guilin Liu:

Duygu Ceylan:

Ersin Yumer:

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

Jyh-Ming Lien:

【Computational photography】Turning Corners Into Cameras: Principles and Methods

Katherine L. Bouman:

Vickie Ye:

Adam B. Yedidia:

Frédo Durand: http://people.csail.mit.edu/fredo/

Gregory W. Wornell:

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

William T. Freeman: http://people.csail.mit.edu/billf/

【Computational photography】Linear Differential Constraints for Photo-Polarimetric Height Estimation

Silvia Tozza:

William A. P. Smith:

Dizhong Zhu:

Ravi Ramamoorthi:

Edwin R. Hancock:

【Computational photography】Polynomial Solvers for Saturated Ideals

Viktor Larsson:

Kalle Åström:

Magnus Oskarsson:

【Computational photography】Video Reflection Removal Through Spatio-Temporal Optimization

Ajay Nandoriya:

Mohamed Elgharib:

Changil Kim:

Mohamed Hefeeda:

Wojciech Matusik:

【Computational photography】Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis

Rui Huang:

Shu Zhang:

Tianyu Li:

Ran He:

【Computational photography】Decoder Network Over Lightweight Reconstructed Feature for Fast Semantic Style Transfer

Ming Lu:

Hao Zhao:

Anbang Yao:

Feng Xu:

Yurong Chen:

Li Zhang: http://pages.cs.wisc.edu/~lizhang/

【Computational photography】Should We Encode Rain Streaks in Video as Deterministic or Stochastic?

Wei Wei:

Lixuan Yi:

Qi Xie:

Qian Zhao:

Deyu Meng:

Zongben Xu:

【Computational photography】Joint Bi-Layer Optimization for Single-Image Rain Streak Removal

Lei Zhu:

Chi-Wing Fu:

Dani Lischinski: http://www.cs.huji.ac.il/~danix/

Pheng-Ann Heng:

【Computational photography】A Unified Model for Near and Remote Sensing

Scott Workman:

Menghua Zhai:

David J. Crandall:

Nathan Jacobs:

【Computational photography】AMAT: Medial Axis Transform for Natural Images

Stavros Tsogkas:

Sven Dickinson:

【Computational photography】CVAE-GAN: Fine-Grained Image Generation Through Asymmetric Training

Jianmin Bao:

Dong Chen:

Fang Wen:

Houqiang Li:

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

【Computational photography】Dual GAN: Unsupervised Dual Learning for Image-To-Image Translation

Zili Yi:

Hao Zhang:

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

Minglun Gong:

【Computational photography】Visual Forecasting by Imitating Dynamics in Natural Sequences

Kuo-Hao Zeng:

William B. Shen:

De-An Huang:

Min Sun:

Juan Carlos Niebles:

【Computational photography】A Microfacet-Based Reflectance Model for Photometric Stereo With Highly Specular Surfaces

Lixiong Chen:

Yinqiang Zheng:

Boxin Shi:

Art Subpa-Asa:

Imari Sato:

【Computational photography】A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing

Qingnan Fan:

Jiaolong Yang:

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

Baoquan Chen:

David Wipf:

【Computational photography】Revisiting Cross-Channel Information Transfer for Chromatic Aberration Correction

Tiancheng Sun:

Yifan Peng:

Wolfgang Heidrich:

【Computational photography】DSLR-Quality Photos on Mobile Devices With Deep Convolutional Networks

Andrey Ignatov:

Nikolay Kobyshev:

Radu Timofte:

Kenneth Vanhoey:

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

【Computational photography】The Pose Knows: Video Forecasting by Generating Pose Futures

Jacob Walker:

Kenneth Marino:

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

Martial Hebert: http://www.cs.cmu.edu/~hebert/

【Computational photography】Recurrent Topic-Transition GAN for Visual Paragraph Generation

Xiaodan Liang:

Zhiting Hu:

Hao Zhang:

Chuang Gan:

Eric P. Xing:

【Computational photography】Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning

Soravit Changpinyo:

Wei-Lun Chao:

Fei Sha:

【Computational photography】Aesthetic Critiques Generation for Photos

Kuang-Yu Chang:

Kung-Hung Lu:

Chu-Song Chen:

【Computational photography】Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses

Christian Rupprecht:

Iro Laina:

Robert Di Pietro:

Maximilian Baust:

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

Nassir Navab: http://campar.in.tum.de/Main/NassirNavab

Gregory D. Hager:

【Computational photography】Efficient Global Illumination for Morphable Models

Andreas Schneider:

Sandro Schönborn:

Lavrenti Frobeen:

Bernhard Egger:

Thomas Vetter:

【Computational photography】Benchmarking Single-Image Reflection Removal Algorithms

Renjie Wan:

Boxin Shi:

Ling-Yu Duan:

Ah-Hwee Tan:

Alex C. Kot:

【Computational photography】A Joint Intrinsic-Extrinsic Prior Model for Retinex

Bolun Cai:

Xianming Xu:

Kailing Guo:

Kui Jia:

Bin Hu:

Dacheng Tao:

【Computational photography】Going Unconstrained With Rolling Shutter Deblurring

Mahesh Mohan M. R.:

N. Rajagopalan:

Gunasekaran Seetharaman:

【Computational photography】Online Video Deblurring via Dynamic Temporal Blending Network

Tae Hyun Kim:

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

Bernhard Schölkopf:

Michael Hirsch:

【Computational photography】Characterizing and Improving Stability in Neural Style Transfer

Agrim Gupta:

Justin Johnson:

Alexandre Alahi:

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

【Computational photography】Adversarial Inverse Graphics Networks: Learning 2D-To-3D Lifting and Image-To-Image Translation From Unpaired Supervision

Hsiao-Yu Fish Tung:

Adam W. Harley:

William Seto:

Katerina Fragkiadaki:

【Computational photography】Detail-Revealing Deep Video Super-Resolution

Xin Tao:

Hongyun Gao:

Renjie Liao:

Jue Wang: http://www.juew.org/

Jiaya Jia: http://www.cse.cuhk.edu.hk/leojia/

【Computational photography】Enhance Net: Single Image Super-Resolution Through Automated Texture Synthesis

Mehdi S. M. Sajjadi:

Bernhard Schölkopf:

Michael Hirsch:

【Computational photography】Learning High Dynamic Range From Outdoor Panoramas

Jinsong Zhang:

Jean-François Lalonde:

【Computational photography】From RGB to Spectrum for Natural Scenes via Manifold-Based Mapping

Yan Jia:

Yinqiang Zheng:

Lin Gu:

Art Subpa-Asa:

Antony Lam:

Yoichi Sato:

Imari Sato:

【Computational photography】Deep Fuse: A Deep Unsupervised Approach for Exposure Fusion With Extreme Exposure Image Pairs
Ram Prabhakar:

V Sai Srikar:

Venkatesh Babu:

【Computational photography】Convergence Analysis of MAP Based Blur Kernel Estimation

Sunghyun Cho:

Seungyong Lee:

【Computational photography】Personalized Cinemagraphs Using Semantic Understanding and Collaborative Learning

Tae-Hyun Oh:

Kyungdon Joo:

Neel Joshi:

Baoyuan Wang:

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

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

【Computational photography】What Is Around the Camera?

Stamatios Georgoulis:

Konstantinos Rematas:

Tobias Ritschel:

Mario Fritz: https://scalable.mpi-inf.mpg.de/

Tinne Tuytelaars: http://homes.esat.kuleuven.be/~tuytelaa/

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

【Computational photography】Stack GAN: Text to Photo-Realistic Image Synthesis With Stacked Generative Adversarial Networks

Han Zhang:

Tao Xu:

Hongsheng Li:

Shaoting Zhang: http://webpages.uncc.edu/~szhang16/

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

Xiaolei Huang: http://www.cse.lehigh.edu/~huang/

Dimitris N. Metaxas:

【Computational photography】Editable Parametric Dense Foliage From 3D Capture

Gaurav Chaurasia:

Paul Beardsley:

【Computational photography】Reflectance Capture Using Univariate Sampling of BRDFs

Zhuo Hui:

Kalyan Sunkavalli:

Joon-Young Lee:

Sunil Hadap:

Jian Wang:

Aswin C. Sankaranarayanan:

【Computational photography】Realistic Dynamic Facial Textures From a Single Image Using GANs

Kyle Olszewski:

Zimo Li:

Chao Yang:

Yi Zhou:

Ronald Yu:

Zeng Huang:

Sitao Xiang:

Shunsuke Saito:

Pushmeet Kohli: http://research.microsoft.com/en-us/um/people/pkohli/

Hao Li:

【Computational photography】Recurrent Color Constancy

Yanlin Qian:

Ke Chen:

Jarno Nikkanen:

Joni-Kristian Kämäräinen:

Jiří Matas:

【Computational photography】Understanding and Mapping Natural Beauty

Scott Workman:

Richard Souvenir:

Nathan Jacobs:

【Computational photography】Stack GAN: Text to Photo-Realistic Image Synthesis With Stacked Generative Adversarial Networks

Han Zhang:

Tao Xu:

Hongsheng Li:

Shaoting Zhang: http://webpages.uncc.edu/~szhang16/

Xiaogang Wang: http://www.ee.cuhk.edu.hk/~xgwang/

Xiaolei Huang: http://www.cse.lehigh.edu/~huang/

Dimitris N. Metaxas:

【Texture analysis】A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition

Isma Hadji:

Richard P. Wildes:

【Texture analysis】A 3D Morphable Model of Craniofacial Shape and Texture Variation

Hang Dai:

Nick Pears:

William A. P. Smith:

Christian Duncan:

【Texture analysis】From Square Pieces to Brick Walls: The Next Challenge in Solving Jigsaw Puzzles

Shir Gur:

Ohad Ben-Shahar:

【Texture analysis】Learning-Based Cloth Material Recovery From Video

Shan Yang:

Junbang Liang:

Ming C. Lin:

【Texture analysis】Locally-Transferred Fisher Vectors for Texture Classification

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

Fan Zhang:

Qing Li:

Heng Huang:

Lauren J. O’Donnell:

Weidong Cai:

【Machine learning】No Fuss Distance Metric Learning Using Proxies

Yair Movshovitz-Attias:

Alexander Toshev:

Thomas K. Leung:

Sergey Ioffe:

Saurabh Singh:

【Machine learning】When Unsupervised Domain Adaptation Meets Tensor Representations

Hao Lu:

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

Zhiguo Cao:

Wei Wei:

Ke Xian:

Chunhua Shen:

Anton van den Hengel:

【Machine learning】Look, Listen and Learn

Relja Arandjelović:

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

【Machine learning】Unsupervised Representation Learning by Sorting Sequences

Hsin-Ying Lee:

Jia-Bin Huang:

Maneesh Singh:

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

【Machine learning】Open Set Domain Adaptation

Pau Panareda Busto:

Juergen Gall: http://www.iai.uni-bonn.de/~gall/

【Machine learning】Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems

Thomas Möllenhoff:

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

【Machine learning】Encoder Based Lifelong Learning

Amal Rannen:

Rahaf Aljundi:

Matthew B. Blaschko:

Tinne Tuytelaars: http://homes.esat.kuleuven.be/~tuytelaa/

【Machine learning】Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach

Giorgio Roffo:

Simone Melzi:

Umberto Castellani:

Alessandro Vinciarelli:

【Machine learning】Adversarial Image Perturbation for Privacy Protection — A Game Theory Perspective

Seong Joon Oh:

Mario Fritz: https://scalable.mpi-inf.mpg.de/

Bernt Schiele: http://www.d2.mpi-inf.mpg.de/schiele/

【Machine learning】Multi-Task Self-Supervised Visual Learning

Carl Doersch:

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

【Machine learning】A Self-Balanced Min-Cut Algorithm for Image Clustering

Xiaojun Chen:

Joshua Zhexue Haung:

Feiping Nie:

Renjie Chen:

Qingyao Wu:

【Machine learning】Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding

Giuseppe Lisanti:

Niki Martinel:

Alberto Del Bimbo:

Gian Luca Foresti:

【Machine learning】Deep Metric Learning With Angular Loss

Jian Wang:

Feng Zhou:

Shilei Wen:

Xiao Liu:

Yuanqing Lin:

【Machine learning】Associative Domain Adaptation

Philip Haeusser:

Thomas Frerix:

Alexander Mordvintsev:

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

【Machine learning】Introspective Neural Networks for Generative Modeling

Justin Lazarow:

Long Jin:

Zhuowen Tu: http://pages.ucsd.edu/~ztu/

【Machine learning】Towards a Unified Compositional Model for Visual Pattern Modeling

Wei Tang:

Pei Yu:

Jiahuan Zhou:

Ying Wu:

【Machine learning】Inferring and Executing Programs for Visual Reasoning

Justin Johnson:

Bharath Hariharan:

Laurens van der Maaten:

Judy Hoffman:

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

Lawrence Zitnick:

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

【Machine learning】Low-Rank Tensor Completion: A Pseudo-Bayesian Learning Approach

Wei Chen:

Nan Song:

【Machine learning】Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling

Mehdi Bahri:

Yannis Panagakis:

Stefanos Zafeiriou:

【Machine learning】Interpretable Explanations of Black Boxes by Meaningful Perturbation

Ruth C. Fong:

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

【Machine learning】PUn DA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories

Behnam Gholami:

Ognjen (Oggi) Rudovic:

Vladimir Pavlovic:

【Machine learning】Learning Discriminative ab-Divergences for Positive Definite Matrices

Anoop Cherian:

Panagiotis Stanitsas:

Mehrtash Harandi:

Vassilios Morellas:

Nikolaos Papanikolopoulos:

【Machine learning】Consensus Convolutional Sparse Coding

Biswarup Choudhury:

Robin Swanson:

Felix Heide:

Gordon Wetzstein:

Wolfgang Heidrich:

【Machine learning】Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning

Calvin Murdock:

Fernando De la Torre:

【Machine learning】Side Information in Robust Principal Component Analysis: Algorithms and Applications

Niannan Xue:

Yannis Panagakis:

Stefanos Zafeiriou:

【Machine learning】Unrolled Memory Inner-Products: An Abstract GPU Operator for Efficient Vision-Related Computations

Yu-Sheng Lin:

Wei-Chao Chen:

Shao-Yi Chien:

【Machine learning】FLa ME: Fast Lightweight Mesh Estimation Using Variational Smoothing on Delaunay Graphs

Nicholas Greene:

Nicholas Roy:

【Machine learning】Ann Arbor: Approximate Nearest Neighbors Using Arborescence Coding

Artem Babenko:

Victor Lempitsky:

【Machine learning】Sparse Exact PGA on Riemannian Manifolds

Monami Banerjee:

Rudrasis Chakraborty:

Baba C. Vemuri:

【Machine learning】Tensor RPCA by Bayesian CP Factorization With Complex Noise

Qiong Luo:

Zhi Han:

Xi’ai Chen:

Yao Wang:

Deyu Meng:

Dong Liang:

Yandong Tang:

【Machine learning】Multimodal Gaussian Process Latent Variable Models With Harmonization

Guoli Song:

Shuhui Wang:

Qingming Huang:

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

【Machine learning】Dense and Low-Rank Gaussian CRFs Using Deep Embeddings

Siddhartha Chandra:

Nicolas Usunier:

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

【Machine learning】BIER – Boosting Independent Embeddings Robustly

Michael Opitz:

Georg Waltner:

Horst Possegger:

Horst Bischof: http://www.icg.tugraz.at/Members/bischof

【Machine learning】Quantitative Evaluation of Confidence Measures in a Machine Learning World

Matteo Poggi:

Fabio Tosi:

Stefano Mattoccia:

【Machine learning】Representation Learning by Learning to Count

Mehdi Noroozi:

Hamed Pirsiavash:

Paolo Favaro:

【Machine learning】A Discriminative View of MRF Pre-Processing Algorithms

Chen Wang:

Charles Herrmann:

Ramin Zabih: http://www.cs.cornell.edu/~rdz/

【Machine learning】Deeper, Broader and Artier Domain Generalization

Da Li:

Yongxin Yang:

Yi-Zhe Song:

Timothy M. Hospedales:

【Machine learning】Efficient Low Rank Tensor Ring Completion

Wenqi Wang:

Vaneet Aggarwal:

Shuchin Aeron:

【Machine learning】Unified Deep Supervised Domain Adaptation and Generalization

Saeid Motiian:

Marco Piccirilli:

Donald A. Adjeroh:

Gianfranco Doretto:

【Machine learning】Interpretable Transformations With Encoder-Decoder Networks

Daniel E. Worrall:

Stephan J. Garbin:

Daniyar Turmukhambetov:

Gabriel J. Brostow:

【Machine learning】The “Something Something” Video Database for Learning and Evaluating Visual Common Sense

Raghav Goyal:

Samira Ebrahimi Kahou:

Vincent Michalski:

Joanna Materzyńska:

Susanne Westphal:

Heuna Kim:

Valentin Haenel:

Ingo Fruend:

Peter Yianilos:

Moritz Mueller-Freitag:

Florian Hoppe:

Christian Thurau:

Ingo Bax:

Roland Memisevic:

【Machine learning】GPLAC: Generalizing Vision-Based Robotic Skills Using Weakly Labeled Images

Avi Singh:

Larry Yang:

Sergey Levine:

【Machine learning】Deep Adaptive Image Clustering

Jianlong Chang:

Lingfeng Wang:

Gaofeng Meng: http://www.escience.cn/people/menggaofeng/index.html

Shiming Xiang:

Chunhong Pan: http://people.gucas.ac.cn/~panchunhong

【Deep learning】Non-Convex Rank/Sparsity Regularization and Local Minima

Carl Olsson:

Marcus Carlsson:

Fredrik Andersson:

Viktor Larsson:

【Deep learning】Coordinating Filters for Faster Deep Neural Networks

Wei Wen:

Cong Xu:

Chunpeng Wu:

Yandan Wang:

Yiran Chen:

Hai Li:

【Deep learning】Deformable Convolutional Networks

Jifeng Dai:

Haozhi Qi:

Yuwen Xiong:

Yi Li: http://users.cecs.anu.edu.au/~yili/

Guodong Zhang:

Han Hu:

Yichen Wei:

【Deep learning】Hard-Aware Deeply Cascaded Embedding

Yuhui Yuan:

Kuiyuan Yang:

Chao Zhang:

【Deep learning】Query-Guided Regression Network With Context Policy for Phrase Grounding

Kan Chen:

Rama Kovvuri:

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

【Deep learning】Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

Chen Sun:

Abhinav Shrivastava:

Saurabh Singh:

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

【Deep learning】Genetic CNN

Lingxi Xie:

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

【Deep learning】Channel Pruning for Accelerating Very Deep Neural Networks

Yihui He:

Xiangyu Zhang:

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

【Deep learning】High Order Tensor Formulation for Convolutional Sparse Coding

Adel Bibi:

Bernard Ghanem:

【Deep learning】Class Rectification Hard Mining for Imbalanced Deep Learning

Qi Dong:

Shaogang Gong: http://www.eecs.qmul.ac.uk/~sgg/

Xiatian Zhu:

【Deep learning】Octree Generating Networks: Efficient Convolutional Architectures for High-Resolution 3D Outputs

Maxim Tatarchenko:

Alexey Dosovitskiy:

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

【Deep learning】Truncating Wide Networks Using Binary Tree Architectures

Yan Zhang:

Mete Ozay:

Shuohao Li:

Takayuki Okatani:

【Deep learning】Performance Guaranteed Network Acceleration via High-Order Residual Quantization

Zefan Li:

Bingbing Ni:

Wenjun Zhang:

Xiaokang Yang:

Wen Gao: http://www.jdl.ac.cn/

【Deep learning】Learning Efficient Convolutional Networks Through Network Slimming

Zhuang Liu:

Jianguo Li:

Zhiqiang Shen:

Gao Huang:

Shoumeng Yan:

Changshui Zhang:

【Deep learning】Least Squares Generative Adversarial Networks

Xudong Mao:

Qing Li:

Haoran Xie:

Raymond Y.K. Lau:

Zhen Wang:

Stephen Paul Smolley:

【Deep learning】Centered Weight Normalization in Accelerating Training of Deep Neural Networks

Lei Huang:

Xianglong Liu:

Yang Liu:

Bo Lang:

Dacheng Tao:

【Deep learning】Deep Growing Learning

Guangcong Wang:

Xiaohua Xie:

Jianhuang Lai:

Jiaxuan Zhuo:

【Deep learning】Smart Mining for Deep Metric Learning

Ben Harwood:

Vijay Kumar B G:

Gustavo Carneiro: http://cs.adelaide.edu.au/~carneiro/research.html

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

Tom Drummond:

【Deep learning】Temporal Generative Adversarial Nets With Singular Value Clipping

Masaki Saito:

Eiichi Matsumoto:

Shunta Saito:

【Deep learning】Sampling Matters in Deep Embedding Learning

Chao-Yuan Wu:

Manmatha:

Alexander J. Smola:

Philipp Krähenbühl:

【Deep learning】Generative Adversarial Networks Conditioned by Brain Signals

Simone Palazzo:

Concetto Spampinato:

Isaak Kavasidis:

Daniela Giordano:

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

【Deep learning】Curriculum Dropout

Pietro Morerio:

Jacopo Cavazza:

Riccardo Volpi:

René Vidal:

Vittorio Murino:

【Deep learning】Domain-Adaptive Deep Network Compression

Marc Masana:

Joost van de Weijer:

Luis Herranz:

Andrew D. Bagdanov:

Jose M. Álvarez:

【Deep learning】Interleaved Group Convolutions

Ting Zhang:

Guo-Jun Qi:

Bin Xiao:

Jingdong Wang:

【Deep learning】Rotation Equivariant Vector Field Networks

Diego Marcos:

Michele Volpi:

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

Devis Tuia:

【Deep learning】Thi Net: A Filter Level Pruning Method for Deep Neural Network Compression

Jian-Hao Luo:

Jianxin Wu:

Weiyao Lin:

【Deep learning】Auto DIAL: Automatic Doma In Alignment Layers

Fabio Maria Carlucci:

Lorenzo Porzi:

Barbara Caputo:

Elisa Ricci:

Samuel Rota Bulò:

【Deep learning】Deep Set Net: Predicting Sets With Deep Neural Networks

Hamid Rezatofighi:

Vijay Kumar B G:

Anton Milan:

Ehsan Abbasnejad:

Anthony Dick:

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

【Deep learning】Convolutional Dictionary Learning via Local Processing

Vardan Papyan:

Yaniv Romano:

Jeremias Sulam:

Michael Elad:

【Deep learning】One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models

H. Rick Chang:

Chun-Liang Li:

Barnabás Póczos:

V. K. Vijaya Kumar:

Aswin C. Sankaranarayanan:

【Deep learning】Scaling the Scattering Transform: Deep Hybrid Networks

Edouard Oyallon:

Eugene Belilovsky:

Sergey Zagoruyko:

【Deep learning】Training Deep Networks to Be Spatially Sensitive

Nicholas Kolkin:

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

Gregory Shakhnarovich:

【Deep learning】Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization

Kamran Ghasedi Dizaji:

Amirhossein Herandi:

Cheng Deng:

Weidong Cai:

Heng Huang:

【Deep learning】Learning Bag-Of-Features Pooling for Deep Convolutional Neural Networks

Nikolaos Passalis:

Anastasios Tefas:

【Deep learning】One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models\

H. Rick Chang:

Chun-Liang Li:

Barnabás Póczos:

V. K. Vijaya Kumar:

Aswin C. Sankaranarayanan:

【Deep learning】Representation Learning by Learning to Count

Mehdi Noroozi:

Hamed Pirsiavash:

Paolo Favaro:

【Multimodel learning】Attention-Based Multimodal Fusion for Video Description

Chiori Hori:

Takaaki Hori:

Teng-Yok Lee:

Ziming Zhang:

Bret Harsham:

John R. Hershey:

Tim K. Marks:

Kazuhiko Sumi:

发表评论