2016 CVPR

下面是2016 CVPR文章的主题标签,文章列表来源于http://cvpr2016.thecvf.com/program/main_conference

PDF的网址:http://www.cv-foundation.org/openaccess/CVPR2016.py

Topic: Scene parsing; Object segmentation; Image segmentation; Video segmentation; Boundary detection; Contour analysis; Object tracking; Action recognition; Crowd 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;

【Scene parsing】Stacked Attention Networks for Image Question Answering.

Zichao Yang:

Xiaodong He:

Jianfeng Gao:

Li Deng:

Alex Smola:

【Scene parsing】Image Question Answering Using Convolutional Neural Network With Dynamic Parameter Prediction.

Hyeonwoo Noh:

Paul Hongsuck Seo:

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

【Scene parsing】Layered Scene Decomposition via the Occlusion-CRF.

Chen Liu:

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

Yasutaka Furukawa:

【Scene parsing】What Value Do Explicit High Level Concepts Have in Vision to Language Problems?.

Qi Wu:

Chunhua Shen:

Lingqiao Liu:

Anthony Dick:

Anton van den Hengel:

【Scene parsing】Inter Active: Inter-Layer Activeness Propagation.

Lingxi Xie:

Liang Zheng:

Jingdong Wang:

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

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

【Scene parsing】Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering.

Mohammad Najafi:

Sarah Taghavi Namin:

Mathieu Salzmann:

Lars Petersson:

【Scene parsing】De Lay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes.

Saumitro Dasgupta:

Kuan Fang:

Kevin Chen:

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

【Scene parsing】3D Semantic Parsing of Large-Scale Indoor Spaces.

Iro Armeni:

Ozan Sener:

Amir R. Zamir:

Helen Jiang:

Ioannis Brilakis:

Martin Fischer:

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

【Scene parsing】Hierarchically Gated Deep Networks for Semantic Segmentation.

Guo-Jun Qi:

【Scene parsing】Deep Structured Scene Parsing by Learning With Image Descriptions.

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

Guangrun Wang:

Rui Zhang:

Ruimao Zhang:

Xiaodan Liang:

Wangmeng Zuo:

【Scene parsing】Seeing Through the Human Reporting Bias: Visual Classifiers From Noisy Human-Centric Labels.

Ishan Misra:

  1. Lawrence Zitnick:

Margaret Mitchell:

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

【Scene parsing】Harnessing Object and Scene Semantics for Large-Scale Video Understanding.

Zuxuan Wu:

Yanwei Fu:

Yu-Gang Jiang:

Leonid Sigal:

【Scene parsing】Instance-Aware Semantic Segmentation via Multi-Task Network Cascades.

Jifeng Dai:

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

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

【Scene parsing】Scribble Sup: Scribble-Supervised Convolutional Networks for Semantic Segmentation.

Di Lin:

Jifeng Dai:

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

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

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

【Scene parsing】Feature Space Optimization for Semantic Video Segmentation.

Abhijit Kundu:

Vibhav Vineet:

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

【Scene parsing】Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation.

Guosheng Lin:

Chunhua Shen:

Anton van den Hengel:

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

【Scene parsing】Learning Transferrable Knowledge for Semantic Segmentation With Deep Convolutional Neural Network.

Seunghoon Hong:

Junhyuk Oh:

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

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

【Scene parsing】The Cityscapes Dataset for Semantic Urban Scene Understanding.

Marius Cordts:

Mohamed Omran:

Sebastian Ramos:

Timo Rehfeld:

Markus Enzweiler:

Rodrigo Benenson: http://rodrigob.github.io/

Uwe Franke:

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

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

【Scene parsing】Gaussian Conditional Random Field Network for Semantic Segmentation.

Raviteja Vemulapalli:

Oncel Tuzel:

Ming-Yu Liu:

Rama Chellapa:

【Scene parsing】The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes.

German Ros:

Laura Sellart:

Joanna Materzynska:

David Vazquez:

Antonio M. López:

【Scene parsing】Semantic Segmentation With Boundary Neural Fields.

Gedas Bertasius:

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

Lorenzo Torresani:

【Scene parsing】DAG-Recurrent Neural Networks For Scene Labeling.

Bing Shuai:

Zhen Zuo:

Bing Wang:

Gang Wang:

【Scene parsing】Saliency Guided Dictionary Learning for Weakly-Supervised Image Parsing.

Baisheng Lai:

Xiaojin Gong:

【Scene parsing】Attention to Scale: Scale-Aware Semantic Image Segmentation.

Liang-Chieh Chen:

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

Jiang Wang:

Wei Xu:

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

【Scene parsing】Scene Labeling Using Sparse Precision Matrix.

Nasim Souly:

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

【Scene parsing】Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer.

Jun Xie:

Martin Kiefel:

Ming-Ting Sun:

Andreas Geiger:

【Scene parsing】Semantic Image Segmentation With Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform.

Liang-Chieh Chen:

Jonathan T. Barron:

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

Kevin Murphy:

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

【Scene parsing】Structured Prediction of Unobserved Voxels From a Single Depth Image.

Michael Firman:

Oisin Mac Aodha:

Simon Julier:

Gabriel J. Brostow:

【Scene parsing】Situation Recognition: Visual Semantic Role Labeling for Image Understanding .

Mark Yatskar:

Luke Zettlemoyer:

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

【Scene parsing】Memory Efficient Max Flow for Multi-Label Submodular MRFs.

Thalaiyasingam Ajanthan:

Richard Hartley: http://users.cecs.anu.edu.au/~hartley/

Mathieu Salzmann:

【Scene parsing】PPP: Joint Pointwise and Pairwise Image Label Prediction.

Yilin Wang:

Suhang Wang:

Jiliang Tang:

Huan Liu:

Baoxin Li:

【Object segmentation】Interactive Segmentation on RGBD Images via Cue Selection.

Jie Feng:

Brian Price:

Scott Cohen:

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

【Object segmentation】Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding.

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

Takuya Narihira:

Stella X. Yu:

【Object segmentation】Deep Interactive Object Selection.

Ning Xu:

Brian Price:

Scott Cohen:

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

Thomas S. Huang:

【Object segmentation】In the Shadows, Shape Priors Shine: Using Occlusion to Improve Multi-Region Segmentation.

Yuka Kihara:

Matvey Soloviev:

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

【Object segmentation】Reversible Recursive Instance-Level Object Segmentation.

Xiaodan Liang:

Yunchao Wei:

Xiaohui Shen:

Zequn Jie:

Jiashi Feng:

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

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

【Object segmentation】Coherent Parametric Contours for Interactive Video Object Segmentation.

Yao Lu:

Xue Bai:

Linda Shapiro: http://homes.cs.washington.edu/~shapiro/

Jue Wang: http://www.juew.org/

【Object segmentation】Instance-Level Segmentation for Autonomous Driving With Deep Densely Connected MRFs.

Ziyu Zhang:

Sanja Fidler:

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

【Object segmentation】Object Co-Segmentation via Graph Optimized-Flexible Manifold Ranking.

Rong Quan:

Junwei Han:

Dingwen Zhang:

Feiping Nie:

【Object segmentation】Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions.

Won-Dong Jang:

Chulwoo Lee:

Chang-Su Kim:

【Object segmentation】Automatic Fence Segmentation in Videos of Dynamic Scenes.

Renjiao Yi:

Jue Wang: http://www.juew.org/

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

【Object segmentation】Discovering the Physical Parts of an Articulated Object Class From Multiple Videos.

Luca Del Pero:

Susanna Ricco:

Rahul Sukthankar:

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

【Object segmentation】A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation.

Federico Perazzi:

Jordi Pont-Tuset:

Brian Mc Williams:

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

Markus Gross:

Alexander Sorkine-Hornung:

【Object segmentation】Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals.

Fanyi Xiao:

Yong Jae Lee:

【Object segmentation】POD: Discovering Primary Objects in Videos Based on Evolutionary Refinement of Object Recurrence, Background, and Primary Object Models.

Yeong Jun Koh:

Won-Dong Jang:

Chang-Su Kim:

【Object segmentation】Hedgehog Shape Priors for Multi-Object Segmentation.

Hossam Isack:

Olga Veksler: http://www.csd.uwo.ca/faculty/olga/

Milan Sonka:

Yuri Boykov: http://www.csd.uwo.ca/~yuri/

【Object segmentation】DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation.

Hao Chen:

Xiaojuan Qi:

Lequan Yu:

Pheng-Ann Heng:

【Object segmentation】Multi-Scale Patch Aggregation

Shu Liu:

Xiaojuan Qi:

Jianping Shi:

Hong Zhang:

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

【Object segmentation】Semantic Object Parsing With Local-Global Long Short-Term Memory.

Xiaodan Liang:

Xiaohui Shen:

Donglai Xiang:

Jiashi Feng:

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

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

【Object segmentation】HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images.

Gellért Máttyus:

Shenlong Wang:

Sanja Fidler:

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

【Object segmentation】Iterative Instance Segmentation.

Ke Li:

Bharath Hariharan:

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

【Object segmentation】Instance-Level Video Segmentation From Object Tracks.

Guillaume Seguin:

Piotr Bojanowski:

Rémi Lajugie:

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

【Object segmentation】Optical Flow With Semantic Segmentation and Localized Layers.

Laura Sevilla-Lara:

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

Varun Jampani:

Michael J. Black: http://ps.is.tue.mpg.de/person/black

【Image segmentation】A New Finsler Minimal Path Model With Curvature Penalization for Image Segmentation and Closed Contour Detection.

Da Chen:

Jean-Marie Mirebeau:

Laurent D. Cohen:

【Image segmentation】Scale-Aware Alignment of Hierarchical Image Segmentation.

Yuhua Chen:

Dengxin Dai:

Jordi Pont-Tuset:

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

【Image segmentation】Pull the Plug? Predicting If Computers or Humans Should Segment Images.

Danna Gurari:

Suyog Jain:

Margrit Betke:

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

【Image segmentation】Convexity Shape Constraints for Image Segmentation.

Loic A. Royer:

David L. Richmond:

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

Bjoern Andres:

Dagmar Kainmueller:

【Image segmentation】MCMC Shape Sampling for Image Segmentation With Nonparametric Shape Priors.

Ertunc Erdil:

Sinan Yildirim:

Müjdat Cetin:

Tolga Tasdizen: http://www.sci.utah.edu/~tolga/

【Image segmentation】Manifold SLIC: A Fast Method to Compute Content-Sensitive Superpixels.

Yong-Jin Liu:

Cheng-Chi Yu:

Min-Jing Yu:

Ying He:

【Image segmentation】Active Image Segmentation Propagation.

Suyog Dutt Jain:

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

【Video segmentation】Learning Temporal Regularity in Video Sequences.

Mahmudul Hasan:

Jonghyun Choi:

Jan Neumann:

Amit K. Roy-Chowdhury:

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

【Video segmentation】Bilateral Space Video Segmentation.

Nicolas Maerki:

Federico Perazzi:

Oliver Wang:

Alexander Sorkine-Hornung:

【Video segmentation】Re D-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering.

Zhang Zhang:

Kaiqi Huang:

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

Peipei Yang:

Jun Li:

【Video segmentation】Recognizing Car Fluents From Video.

Bo Li:

Tianfu Wu:

Caiming Xiong:

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

【Video segmentation】Pairwise Decomposition of Image Sequences for Active Multi-View Recognition.

Edward Johns:

Stefan Leutenegger:

Andrew J. Davison:

【Video segmentation】Video Segmentation via Object Flow.

Yi-Hsuan Tsai:

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

Michael J. Black: http://ps.is.tue.mpg.de/person/black

【Boundary detection】Weakly Supervised Object Boundaries.

Anna Khoreva:

Rodrigo Benenson: http://rodrigob.github.io/

Mohamed Omran:

Matthias Hein:

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

【Boundary detection】Fast Detection of Curved Edges at Low SNR.

Nati Ofir:

Meirav Galun:

Boaz Nadler:

Ronen Basri: http://www.wisdom.weizmann.ac.il/~ronen/

【Boundary detection】Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs.

Wei Shen:

Kai Zhao:

Yuan Jiang:

Yan Wang:

Zhijiang Zhang:

Xiang Bai:

【Boundary detection】Learning Relaxed Deep Supervision for Better Edge Detection.

Yu Liu:

Michael S. Lew:

【Boundary detection】Occlusion Boundary Detection via Deep Exploration of Context.

Huan Fu:

Chaohui Wang:

Dacheng Tao:

Michael J. Black: http://ps.is.tue.mpg.de/person/black

【Boundary detection】Unsupervised Learning of Edges.

Yin Li:

Manohar Paluri:

James M. Rehg:

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

【Boundary detection】Active Learning for Delineation of Curvilinear Structures.

Agata Mosinska-Domanska:

Raphael Sznitman:

Przemyslaw Glowacki:

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

【Contour analysis】Object Contour Detection With a Fully Convolutional Encoder-Decoder Network.

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

Brian Price:

Scott Cohen:

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

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

【Contour analysis】Semi Contour: A Semi-Supervised Learning Approach for Contour Detection.

Zizhao Zhang:

Fuyong Xing:

Xiaoshuang Shi:

Lin Yang:

【Contour analysis】3D Shape Attributes.

David F. Fouhey:

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

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

【Contour analysis】Contour Detection in Unstructured 3D Point Clouds.

Timo Hackel:

Jan D. Wegner:

Konrad Schindler:

【Contour analysis】Efficient Globally Optimal 2D-To-3D Deformable Shape Matching.

Zorah Lähner:

Emanuele Rodolà:

Frank R. Schmidt:

Michael M. Bronstein:

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

【Contour analysis】Consistency of Silhouettes and Their Duals.

Matthew Trager:

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

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

【Contour analysis】Similarity Metric For Curved Shapes In Euclidean Space.

Girum G. Demisse:

Djamila Aouada:

Björn Ottersten:

【Contour analysis】Shape Analysis With Hyperbolic Wasserstein Distance.

Jie Shi:

Wen Zhang:

Yalin Wang:

【Contour analysis】Fits Like a Glove: Rapid and Reliable Hand Shape Personalization.

David Joseph Tan:

Thomas Cashman:

Jonathan Taylor:

Andrew Fitzgibbon:

Daniel Tarlow:

Sameh Khamis:

Shahram Izadi:

Jamie Shotton: http://research.microsoft.com/en-us/groups/vision/default.aspx

【Contour analysis】Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification.

Chunyuan Li:

Andrew Stevens:

Changyou Chen:

Yunchen Pu:

Zhe Gan:

Lawrence Carin:

【Object tracking】Beyond Local Search: Tracking Objects Everywhere With Instance-Specific Proposals.

Gao Zhu:

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

Hongdong Li:

【Object tracking】Groupwise Tracking of Crowded Similar-Appearance Targets From Low-Continuity Image Sequences.

Hongkai Yu:

Youjie Zhou:

Jeff Simmons:

Craig P. Przybyla:

Yuewei Lin:

Xiaochuan Fan:

Yang Mi:

Song Wang:

【Object tracking】STCT: Sequentially Training Convolutional Networks for Visual Tracking.

Lijun Wang:

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

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

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

【Object tracking】Online Multi-Object Tracking via Structural Constraint Event Aggregation.

Ju Hong Yoon:

Chang-Ryeol Lee:

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

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

【Object tracking】Staple: Complementary Learners for Real-Time Tracking.

Luca Bertinetto:

Jack Valmadre:

Stuart Golodetz:

Ondrej Miksik:

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

【Object tracking】Siamese Instance Search for Tracking.

Ran Tao:

Efstratios Gavves:

Arnold W.M. Smeulders:

【Object tracking】Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking.

Martin Danelljan:

Gustav Häger:

Fahad Shahbaz Khan:

Michael Felsberg:

【Object tracking】3D Part-Based Sparse Tracker With Automatic Synchronization and Registration.

Adel Bibi:

Tianzhu Zhang:

Bernard Ghanem:

【Object tracking】Recurrently Target-Attending Tracking.

Zhen Cui:

Shengtao Xiao:

Jiashi Feng:

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

【Object tracking】Robust Multi-Body Feature Tracker: A Segmentation-Free Approach.

Pan Ji:

Hongdong Li:

Mathieu Salzmann:

Yiran Zhong:

【Object tracking】Volumetric 3D Tracking by Detection.

Chun-Hao Huang:

Benjamin Allain:

Jean-Sébastien Franco:

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

Slobodan Ilic:

Edmond Boyer:

【Object tracking】The Solution Path Algorithm for Identity-Aware Multi-Object Tracking.

Shoou-I Yu:

Deyu Meng:

Wangmeng Zuo:

Alexander Hauptmann:

【Object tracking】In Defense of Sparse Tracking: Circulant Sparse Tracker.

Tianzhu Zhang:

Adel Bibi:

Bernard Ghanem:

【Object tracking】Multi-View People Tracking via Hierarchical Trajectory Composition.

Yuanlu Xu:

Xiaobai Liu:

Yang Liu:

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

【Object tracking】Object Tracking via Dual Linear Structured SVM and Explicit Feature Map.

Jifeng Ning:

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

Shaojie Jiang:

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

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

【Object tracking】Robust, Real-Time 3D Tracking of Multiple Objects With Similar Appearances.

Taiki Sekii:

【Object tracking】Learning Multi-Domain Convolutional Neural Networks for Visual Tracking.

Hyeonseob Nam:

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

【Object tracking】Hedged Deep Tracking.

Yuankai Qi:

Shengping Zhang:

Lei Qin:

Hongxun Yao:

Qingming Huang:

Jongwoo Lim:

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

【Object tracking】Structural Correlation Filter for Robust Visual Tracking.

Si Liu:

Tianzhu Zhang:

Xiaochun Cao:

Changsheng Xu:

【Object tracking】Visual Tracking Using Attention-Modulated Disintegration and Integration.

Jongwon Choi:

Hyung Jin Chang:

Jiyeoup Jeong:

Yiannis Demiris:

Jin Young Choi:

【Object tracking】Virtual Worlds as Proxy for Multi-Object Tracking Analysis.

Adrien Gaidon:

Qiao Wang:

Yohann Cabon:

Eleonora Vig:

【Object tracking】Egocentric Future Localization.

Hyun Soo Park:

Jyh-Jing Hwang:

Yedong Niu:

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

【Action recognition】NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis.

Amir Shahroudy:

Jun Liu:

Tian-Tsong Ng:

Gang Wang:

【Action recognition】Progressively Parsing Interactional Objects for Fine Grained Action Detection.

Bingbing Ni:

Xiaokang Yang:

Shenghua Gao:

【Action recognition】Temporal Action Localization in Untrimmed Videos via Multi-Stage CNNs.

Zheng Shou:

Dongang Wang:

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

【Action recognition】What If We Do Not Have Multiple Videos of the Same Action? — Video Action Localization Using Web Images.

Waqas Sultani:

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

【Action recognition】3D Action Recognition From Novel Viewpoints.

Hossein Rahmani:

Ajmal Mian:

【Action recognition】Going Deeper into First-Person Activity Recognition.

Minghuang Ma:

Haoqi Fan:

Kris M. Kitani:

【Action recognition】Cascaded Interactional Targeting Network for Egocentric Video Analysis.

Yang Zhou:

Bingbing Ni:

Richang Hong:

Xiaokang Yang:

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

【Action recognition】Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos.

Fabian Caba Heilbron:

Juan Carlos Niebles:

Bernard Ghanem:

【Action recognition】Discriminative Hierarchical Rank Pooling for Activity Recognition.

Basura Fernando:

Peter Anderson:

Marcus Hutter:

Stephen Gould: http://users.cecs.anu.edu.au/~sgould/index.html

【Action recognition】Convolutional Two-Stream Network Fusion for Video Action Recognition.

Christoph Feichtenhofer:

Axel Pinz:

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

【Action recognition】Learning Activity Progression in LSTMs for Activity Detection and Early Detection.

Shugao Ma:

Leonid Sigal:

Stan Sclaroff:

【Action recognition】VLAD3: Encoding Dynamics of Deep Features for Action Recognition.

Yingwei Li:

Weixin Li:

Vijay Mahadevan:

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

【Action recognition】A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection.

Bharat Singh:

Tim K. Marks:

Michael Jones:

Oncel Tuzel:

Ming Shao:

【Action recognition】A Hierarchical Deep Temporal Model for Group Activity Recognition.

Mostafa S. Ibrahim:

Srikanth Muralidharan:

Zhiwei Deng:

Arash Vahdat:

Greg Mori: http://www.cs.sfu.ca/~mori/

【Action recognition】A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets.

Ivan Lillo:

Juan Carlos Niebles:

Alvaro Soto:

【Action recognition】A Key Volume Mining Deep Framework for Action Recognition.

Wangjiang Zhu:

Jie Hu:

Gang Sun:

Xudong Cao:

Yu Qiao:

【Action recognition】Eye Tracking for Everyone.

Kyle Krafka:

Aditya Khosla:

Petr Kellnhofer:

Suchendra Bhandarkar:

Wojciech Matusik:

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

【Action recognition】First Person Action Recognition Using Deep Learned Descriptors.

Suriya Singh:

Chetan Arora:

  1. V. Jawahar:

【Action recognition】Recognizing Micro-Actions and Reactions From Paired Egocentric Videos.

Ryo Yonetani:

Kris M. Kitani:

Yoichi Sato:

【Action recognition】Mining 3D Key-Pose-Motifs for Action Recognition.

Chunyu Wang:

Yizhou Wang:

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

【Action recognition】Predicting the Where and What of Actors and Actions Through Online Action Localization.

Khurram Soomro:

Haroon Idrees:

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

【Action recognition】Actions ~ Transformations.

Xiaolong Wang:

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

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

【Action recognition】End-To-End Learning of Action Detection From Frame Glimpses in Videos.

Serena Yeung:

Olga Russakovsky:

Greg Mori: http://www.cs.sfu.ca/~mori/

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

【Action recognition】Action Recognition in Video Using Sparse Coding and Relative Features.

Analí Alfaro:

Domingo Mery:

Alvaro Soto:

【Action recognition】Improving Human Action Recognition by Non-Action Classification.

Yang Wang:

Minh Hoai:

【Action recognition】Actionness Estimation Using Hybrid Fully Convolutional Networks.

Limin Wang:

Yu Qiao:

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

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

【Action recognition】Real-Time Action Recognition With Enhanced Motion Vector CNNs.

Bowen Zhang:

Limin Wang:

Zhe Wang:

Yu Qiao:

Hanli Wang:

【Action recognition】Dynamic Image Networks for Action Recognition.

Hakan Bilen:

Basura Fernando:

Efstratios Gavves:

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

Stephen Gould: http://users.cecs.anu.edu.au/~sgould/index.html

【Action recognition】Detecting Events and Key Actors in Multi-Person Videos.

Vignesh Ramanathan:

Jonathan Huang:

Sami Abu-El-Haija:

Alexander Gorban:

Kevin Murphy:

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

【Action recognition】Regularizing Long Short Term Memory With 3D Human-Skeleton Sequences for Action Recognition.

Behrooz Mahasseni:

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

【Action recognition】Personalizing Human Video Pose Estimation.

James Charles:

Tomas Pfister:

Derek Magee:

David Hogg:

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

【Action recognition】Actor-Action Semantic Segmentation With Grouping Process Models.

Chenliang Xu:

Jason J. Corso:

【Action recognition】Temporal Action Localization With Pyramid of Score Distribution Features.

Jun Yuan:

Bingbing Ni:

Xiaokang Yang:

Ashraf A. Kassim:

【Action recognition】Recognizing Activities of Daily Living With a Wrist-Mounted Camera.

Katsunori Ohnishi:

Atsushi Kanehira:

Asako Kanezaki: http://www.mi.t.u-tokyo.ac.jp/

Tatsuya Harada:

【Action recognition】Temporal Action Detection Using a Statistical Language Model.

Alexander Richard:

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

【Action recognition】Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis.

Zheng Zhang:

Jeff M. Girard:

Yue Wu:

Xing Zhang:

Peng Liu:

Umur Ciftci:

Shaun Canavan:

Michael Reale:

Andy Horowitz:

Huiyuan Yang:

Jeffrey F. Cohn:

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

Lijun Yin:

【Action recognition】Deep CAMP: Deep Convolutional Action & Attribute Mid-Level Patterns.

Ali Diba:

Ali Mohammad Pazandeh:

Hamed Pirsiavash:

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

【Action recognition】Rolling Rotations for Recognizing Human Actions From 3D Skeletal Data.

Raviteja Vemulapalli:

Rama Chellapa:

【Action recognition】Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition.

Zhiwei Deng:

Arash Vahdat:

Hexiang Hu:

Greg Mori: http://www.cs.sfu.ca/~mori/

【Crowd analysis】Single-Image Crowd Counting via Multi-Column Convolutional Neural Network.

Yingying Zhang:

Desen Zhou:

Siqin Chen:

Shenghua Gao:

Yi Ma: http://yima.csl.illinois.edu/

【Crowd analysis】Social LSTM: Human Trajectory Prediction in Crowded Spaces.

Alexandre Alahi:

Kratarth Goel:

Vignesh Ramanathan:

Alexandre Robicquet:

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

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

【Crowd analysis】What Players Do With the Ball: A Physically Constrained Interaction Modeling.

Andrii Maksai:

Xinchao Wang:

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

【Crowd analysis】Beyond F-Formations: Determining Social Involvement in Free Standing Conversing Groups From Static Images.

Lu Zhang:

Hayley Hung:

【Crowd analysis】Ambiguity Helps: Classification With Disagreements in Crowdsourced Annotations.

Viktoriia Sharmanska:

Daniel Hernández-Lobato:

José Miguel Hernández-Lobato:

Novi Quadrianto:

【Crowd analysis】End-To-End People Detection in Crowded Scenes.

Russell Stewart:

Mykhaylo Andriluka:

Andrew Y. Ng: http://cs.stanford.edu/people/ang/

【Crowd analysis】Visual Path Prediction in Complex Scenes With Crowded Moving Objects.

Young Joon Yoo:

Kimin Yun:

Sangdoo Yun:

Jong Hee Hong:

Hawook Jeong:

Jin Young Choi:

【Crowd analysis】Highway Vehicle Counting in Compressed Domain.

Xu Liu:

Zilei Wang:

Jiashi Feng:

Hongsheng Xi:

【Crowd analysis】Slicing Convolutional Neural Network for Crowd Video Understanding.

Jing Shao:

Chen-Change Loy:

Kai Kang:

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

【Human detection】Recurrent Attention Models for Depth-Based Person Identification.

Albert Haque:

Alexandre Alahi:

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

【Human detection】Learning a Discriminative Null Space for Person Re-Identification.

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

Tao Xiang:

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

【Human detection】Learning Deep Feature Representations With Domain Guided Dropout for Person Re-Identification.

Tong Xiao:

Hongsheng Li:

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

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

【Human detection】How Far Are We From Solving Pedestrian Detection?.

Shanshan Zhang:

Rodrigo Benenson: http://rodrigob.github.io/

Mohamed Omran:

Jan Hosang:

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

【Human detection】Similarity Learning With Spatial Constraints for Person Re-Identification.

Dapeng Chen:

Zejian Yuan:

Badong Chen: http://gr.xjtu.edu.cn/web/chenbd/home

Nanning Zheng:

【Human detection】Sample-Specific SVM Learning for Person Re-Identification.

Ying Zhang:

Baohua Li:

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

Atshushi Irie:

Xiang Ruan:

【Human detection】Joint Learning of Single-Image and Cross-Image Representations for Person Re-Identification.

Faqiang Wang:

Wangmeng Zuo:

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

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

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

【Human detection】A Multi-Level Contextual Model For Person Recognition in Photo Albums.

Haoxiang Li:

Jonathan Brandt:

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

Xiaohui Shen:

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

【Human detection】Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification.

Peixi Peng:

Tao Xiang:

Yaowei Wang:

Massimiliano Pontil:

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

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

Yonghong Tian:

【Human detection】Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry.

Jiale Cao:

Yanwei Pang:

Xuelong Li:

【Human detection】Recurrent Convolutional Network for Video-Based Person Re-Identification.

Niall Mc Laughlin:

Jesus Martinez del Rincon:

Paul Miller:

【Human detection】Person Re-Identification by Multi-Channel Parts-Based CNN With Improved Triplet Loss Function.

De Cheng:

Yihong Gong:

Sanping Zhou:

Jinjun Wang:

Nanning Zheng:

【Human detection】Top-Push Video-Based Person Re-Identification.

Jinjie You:

Ancong Wu:

Xiang Li:

Wei-Shi Zheng:

【Human detection】Improving Person Re-Identification via Pose-Aware Multi-Shot Matching.

Yeong-Jun Cho:

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

【Human detection】Hierarchical Gaussian Descriptor for Person Re-Identification.

Tetsu Matsukawa:

Takahiro Okabe:

Einoshin Suzuki:

Yoichi Sato:

【Human detection】Semantic Channels for Fast Pedestrian Detection.

Arthur Daniel Costea:

Sergiu Nedevschi:

【Human parsing】Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction.

Edgar Simo-Serra:

Hiroshi Ishikawa:

【Human parsing】Sketch Me That Shoe.

Qian Yu:

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

Yi-Zhe Song:

Tao Xiang:

Timothy M. Hospedales:

Chen-Change Loy:

【Human parsing】Direct Prediction of 3D Body Poses From Motion Compensated Sequences.

Bugra Tekin:

Artem Rozantsev:

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

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

【Human parsing】Deep Fashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations.

Ziwei Liu:

Ping Luo:

Shi Qiu:

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

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

【Human parsing】Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning With Humans in the Loop.

Yin Cui:

Feng Zhou:

Yuanqing Lin:

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

【Human parsing】Dense Human Body Correspondences Using Convolutional Networks.

Lingyu Wei:

Qixing Huang:

Duygu Ceylan:

Etienne Vouga:

Hao Li:

【Human parsing】End-To-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation.

Wei Yang:

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

Hongsheng Li:

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

【Human parsing】Augmented Blendshapes for Real-Time Simultaneous 3D Head Modeling and Facial Motion Capture.

Diego Thomas:

Rin-ichiro Taniguchi:

【Human parsing】Inferring Forces and Learning Human Utilities From Videos.

Yixin Zhu:

Chenfanfu Jiang:

Yibiao Zhao:

Demetri Terzopoulos:

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

【Human parsing】Deep Hand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features.

Ayan Sinha:

Chiho Choi:

Karthik Ramani:

【Human parsing】Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network.

Pavlo Molchanov:

Xiaodong Yang:

Shalini Gupta:

Kihwan Kim:

Stephen Tyree:

Jan Kautz:

【Human parsing】Human Pose Estimation With Iterative Error Feedback.

João Carreira:

Pulkit Agrawal:

Katerina Fragkiadaki:

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

【Human parsing】Deep Cut: Joint Subset Partition and Labeling for Multi Person Pose Estimation.

Leonid Pishchulin:

Eldar Insafutdinov:

Siyu Tang:

Bjoern Andres:

Mykhaylo Andriluka:

Peter V. Gehler:

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

【Human parsing】Thin-Slicing for Pose: Learning to Understand Pose Without Explicit Pose Estimation.

Suha Kwak:

Minsu Cho:

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

【Human parsing】A Dual-Source Approach for 3D Pose Estimation From a Single Image.

Hashim Yasin:

Umar Iqbal:

Björn Krüger:

Andreas Weber:

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

【Human parsing】Efficiently Creating 3D Training Data for Fine Hand Pose Estimation.

Markus Oberweger:

Gernot Riegler:

Paul Wohlhart:

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

【Human parsing】Sparseness Meets Deepness: 3D Human Pose Estimation From Monocular Video.

Xiaowei Zhou:

Menglong Zhu:

Spyridon Leonardos:

Konstantinos G. Derpanis:

Kostas Daniilidis:

【Face recognition】Walk and Learn: Facial Attribute Representation Learning From Egocentric Video and Contextual Data.

Jing Wang:

Yu Cheng:

Rogerio Schmidt Feris:

【Face recognition】Pose-Aware Face Recognition in the Wild.

Iacopo Masi:

Stephen Rawls:

Gérard Medioni:

Prem Natarajan:

【Face recognition】Sparsifying Neural Network Connections for Face Recognition.

Yi Sun:

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

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

【Face recognition】The Mega Face Benchmark: 1 Million Faces for Recognition at Scale .

Ira Kemelmacher-Shlizerman:

Steven M. Seitz: http://homes.cs.washington.edu/~seitz/

Daniel Miller:

Evan Brossard:

【Face recognition】Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition.

Yandong Wen:

Zhifeng Li:

Yu Qiao:

【Face recognition】A Robust Multilinear Model Learning Framework for 3D Faces.

Timo Bolkart:

Stefanie Wuhrer:

【Face recognition】Ordinal Regression With Multiple Output CNN for Age Estimation.

Zhenxing Niu:

Mo Zhou:

Le Wang:

Xinbo Gao:

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

【Face recognition】A 3D Morphable Model Learnt From 10,000 Faces.

James Booth:

Anastasios Roussos:

Stefanos Zafeiriou:

Allan Ponniah:

David Dunaway:

【Face recognition】Discriminative Invariant Kernel Features: A Bells-and-Whistles-Free Approach to Unsupervised Face Recognition and Pose Estimation.

Dipan K. Pal:

Felix Juefei-Xu:

Marios Savvides:

【Face detection】Joint Training of Cascaded CNN for Face Detection.

Hongwei Qin:

Junjie Yan:

Xiu Li:

Xiaolin Hu:

【Face detection】WIDER FACE: A Face Detection Benchmark.

Shuo Yang:

Ping Luo:

Chen-Change Loy:

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

【Face parsing】Face Alignment Across Large Poses: A 3D Solution.

Xiangyu Zhu:

Zhen Lei:

Xiaoming Liu:

Hailin Shi:

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

【Face parsing】Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions.

Sergey Tulyakov:

Xavier Alameda-Pineda:

Elisa Ricci:

Lijun Yin:

Jeffrey F. Cohn:

Nicu Sebe:

【Face parsing】Deep Region and Multi-Label Learning for Facial Action Unit Detection.

Kaili Zhao:

Wen-Sheng Chu:

Honggang Zhang:

【Face parsing】Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection.

Yue Wu:

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

【Face parsing】Unconstrained Face Alignment via Cascaded Compositional Learning.

Shizhan Zhu:

Cheng Li:

Chen-Change Loy:

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

【Face parsing】Learning Reconstruction-Based Remote Gaze Estimation.

Pei Yu:

Jiahuan Zhou:

Ying Wu:

【Face parsing】Facial Expression Intensity Estimation Using Ordinal Information.

Rui Zhao:

Quan Gan:

Shangfei Wang:

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

【Face parsing】Mnemonic Descent Method: A Recurrent Process Applied for End-To-End Face Alignment.

George Trigeorgis:

Patrick Snape:

Mihalis A. Nicolaou:

Epameinondas Antonakos:

Stefanos Zafeiriou:

【Face parsing】Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting.

Amin Jourabloo:

Xiaoming Liu:

【Face parsing】Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity.

Robert Walecki:

Ognjen Rudovic:

Vladimir Pavlovic:

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

【Face parsing】Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix Completion.

Xavier Alameda-Pineda:

Elisa Ricci:

Yan Yan:

Nicu Sebe:

【Face parsing】Emotio Net: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild.

  1. Fabian Benitez-Quiroz:

Ramprakash Srinivasan:

Aleix M. Martinez:

【Face parsing】Forget Me Not: Memory-Aware Forensic Facial Sketch Matching.

Shuxin Ouyang:

Timothy M. Hospedales:

Yi-Zhe Song:

Xueming Li:

【Face parsing】LOMo: Latent Ordinal Model for Facial Analysis in Videos.

Karan Sikka:

Gaurav Sharma:

Marian Bartlett:

【Object recognition】Deep Compositional Captioning: Describing Novel Object Categories Without Paired Training Data.

Lisa Anne Hendricks:

Subhashini Venugopalan:

Marcus Rohrbach:

Raymond Mooney:

Kate Saenko:

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

【Object recognition】Generation and Comprehension of Unambiguous Object Descriptions.

Junhua Mao:

Jonathan Huang:

Alexander Toshev:

Oana Camburu:

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

Kevin Murphy:

【Object recognition】Learning Deep Representations of Fine-Grained Visual Descriptions.

Scott Reed:

Zeynep Akata:

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

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

【Object recognition】Exploit Bounding Box Annotations for Multi-Label Object Recognition.

Hao Yang:

Joey Tianyi Zhou:

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

Bin-Bin Gao:

Jianxin Wu:

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

【Object recognition】SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition.

Han Zhang:

Tao Xu:

Mohamed Elhoseiny:

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

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

Ahmed Elgammal:

Dimitris Metaxas:

【Object recognition】Mining Discriminative Triplets of Patches for Fine-Grained Classification.

Yaming Wang:

Jonghyun Choi:

Vlad Morariu:

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

【Object recognition】Part-Stacked CNN for Fine-Grained Visual Categorization.

Shaoli Huang:

Zhe Xu:

Dacheng Tao:

Ya Zhang:

【Object recognition】Information Bottleneck Learning Using Privileged Information for Visual Recognition.

Saeid Motiian:

Marco Piccirilli:

Donald A. Adjeroh:

Gianfranco Doretto:

【Object recognition】What’s Wrong With That Object? Identifying Images of Unusual Objects by Modelling the Detection Score Distribution.

Peng Wang:

Lingqiao Liu:

Chunhua Shen:

Zi Huang:

Anton van den Hengel:

Heng Tao Shen:

【Object recognition】Do Computational Models Differ Systematically From Human Object Perception?.

T. Pramod:

P. Arun:

【Object recognition】A Task-Oriented Approach for Cost-Sensitive Recognition.

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

Hannaneh Hajishirzi:

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

【Object recognition】i Lab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning.

Ali Borji: http://ilab.usc.edu/borji/

Saeed Izadi:

Laurent Itti: http://ilab.usc.edu/itti/

【Object recognition】BORDER: An Oriented Rectangles Approach to Texture-Less Object Recognition.

Jacob Chan:

Jimmy Addison Lee:

Qian Kemao:

【Object recognition】Box Cars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition.

Jakub Sochor:

Adam Herout:

Jiří Havel:

【Object recognition】Identifying Good Training Data for Self-Supervised Free Space Estimation.

Ali Harakeh:

Daniel Asmar:

Elie Shammas:

【Object recognition】Studying Very Low Resolution Recognition Using Deep Networks.

Zhangyang Wang:

Shiyu Chang:

Yingzhen Yang:

Ding Liu:

Thomas S. Huang:

【Object recognition】Inverting Visual Representations With Convolutional Networks.

Alexey Dosovitskiy:

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

【Object recognition】Multi-View Deep Network for Cross-View Classification.

Meina Kan:

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

Xilin Chen:

【Object recognition】Yin and Yang: Balancing and Answering Binary Visual Questions.

Peng Zhang:

Yash Goyal:

Douglas Summers-Stay:

Dhruv Batra:

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

【Object recognition】Symmetry re CAPTCHA.

Chris Funk:

Yanxi Liu:

【Object recognition】Unsupervised Learning of Discriminative Attributes and Visual Representations.

Chen Huang:

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

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

【Object recognition】Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification.

Hà Quang Minh:

Marco San Biagio:

Loris Bazzani:

Vittorio Murino:

【Object recognition】Some Like It Hot – Visual Guidance for Preference Prediction.

Rasmus Rothe:

Radu Timofte:

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

【Object recognition】Volumetric and Multi-View CNNs for Object Classification on 3D Data.

Charles R. Qi:

Hao Su:

Matthias Niessner:

Angela Dai:

Mengyuan Yan:

Leonidas J. Guibas:

【Object recognition】Recovering the Missing Link: Predicting Class-Attribute Associations for Unsupervised Zero-Shot Learning.

Ziad Al-Halah:

Makarand Tapaswi:

Rainer Stiefelhagen:

【Object recognition】Cataloging Public Objects Using Aerial and Street-Level Images – Urban Trees.

Jan D. Wegner:

Steven Branson:

David Hall:

Konrad Schindler:

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

【Object recognition】CRAFT Objects From Images.

Bin Yang:

Junjie Yan:

Zhen Lei:

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

【Object detection】Training Region-Based Object Detectors With Online Hard Example Mining.

Abhinav Shrivastava:

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

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

【Object detection】You Only Look Once: Unified, Real-Time Object Detection.

Joseph Redmon:

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

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

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

【Object detection】Loc Net: Improving Localization Accuracy for Object Detection.

Spyros Gidaris:

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

【Object detection】Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images.

Shuran Song:

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

【Object detection】Object Detection From Video Tubelets With Convolutional Neural Networks.

Kai Kang:

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

Hongsheng Li:

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

【Object detection】Learning With Side Information Through Modality Hallucination.

Judy Hoffman:

Saurabh Gupta:

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

【Object detection】Object-Proposal Evaluation Protocol is ‘Gameable’.

Neelima Chavali:

Harsh Agrawal:

Aroma Mahendru:

Dhruv Batra:

【Object detection】Hyper Net: Towards Accurate Region Proposal Generation and Joint Object Detection.

Tao Kong:

Anbang Yao:

Yurong Chen:

Fuchun Sun:

【Object detection】We Don’t Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification.

Dim P. Papadopoulos:

Jasper R. R. Uijlings:

Frank Keller:

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

【Object detection】Factors in Finetuning Deep Model for Object Detection With Long-Tail Distribution.

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

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

Cong Zhang:

Xiaokang Yang:

【Object detection】Highlight Detection With Pairwise Deep Ranking for First-Person Video Summarization.

Ting Yao:

Tao Mei:

Yong Rui:

【Object detection】Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients.

Zhile Ren:

Erik B. Sudderth:

【Object detection】Detecting Migrating Birds at Night.

Jia-Bin Huang:

Rich Caruana:

Andrew Farnsworth:

Steve Kelling:

Narendra Ahuja: http://vision.ai.illinois.edu/publications.htm

【Object detection】Large Scale Semi-Supervised Object Detection Using Visual and Semantic Knowledge Transfer.

Yuxing Tang:

Josiah Wang:

Boyang Gao:

Emmanuel Dellandréa:

Robert Gaizauskas:

Liming Chen:

【Object detection】Exploit All the Layers: Fast and Accurate CNN Object Detector With Scale Dependent Pooling and Cascaded Rejection Classifiers.

Fan Yang:

Wongun Choi:

Yuanqing Lin:

【Object detection】Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection.

Keze Wang:

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

Wangmeng Zuo:

Shuhang Gu:

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

【Object detection】Monocular 3D Object Detection for Autonomous Driving.

Xiaozhi Chen:

Kaustav Kundu:

Ziyu Zhang:

Huimin Ma:

Sanja Fidler:

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

【Object detection】Local Background Enclosure for RGB-D Salient Object Detection.

David Feng:

Nick Barnes:

Shaodi You:

Chris Mc Carthy:

【Object detection】Adaptive Object Detection Using Adjacency and Zoom Prediction.

Yongxi Lu:

Tara Javidi:

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

【Object detection】G-CNN: An Iterative Grid Based Object Detector.

Mahyar Najibi:

Mohammad Rastegari:

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

【Object detection】Efficient Point Process Inference for Large-Scale Object Detection.

Trung T. Pham:

Seyed Hamid Rezatofighi:

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

Tat-Jun Chin:

【Object detection】Weakly Supervised Deep Detection Networks.

Hakan Bilen:

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

【Object detection】Inside-Outside Net: Detecting Objects in Context With Skip Pooling and Recurrent Neural Networks.

Sean Bell:

  1. Lawrence Zitnick:

Kavita Bala:

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

【Object detection】RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection.

Gong Cheng:

Peicheng Zhou:

Junwei Han:

【Object detection】Reinforcement Learning for Visual Object Detection.

Stefan Mathe:

Aleksis Pirinen:

Cristian Sminchisescu:

【Object detection】Detecting Repeating Objects Using Patch Correlation Analysis.

Inbar Huberman:

Raanan Fattal: http://www.cs.huji.ac.il/~raananf/

【Object detection】Weakly Supervised Object Localization With Progressive Domain Adaptation.

Dong Li:

Jia-Bin Huang:

Yali Li:

Shengjin Wang:

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

【Object detection】Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection.

Krishna Kumar Singh:

Fanyi Xiao:

Yong Jae Lee:

【Object detection】Deep Exemplar 2D-3D Detection by Adapting From Real to Rendered Views.

Francisco Massa:

Bryan C. Russell:

Mathieu Aubry:

【Saliency detection】Deep Contrast Learning for Salient Object Detection.

Guanbin Li:

Yizhou Yu: http://i.cs.hku.hk/~yzyu/

【Saliency detection】A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond.

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

Christopher Catton:

Sasa Janjic:

【Saliency detection】Spatially Binned ROC: A Comprehensive Saliency Metric.

Calden Wloka:

John Tsotsos:

【Saliency detection】Gra B: Visual Saliency via Novel Graph Model and Background Priors.

Qiaosong Wang:

Wen Zheng:

Robinson Piramuthu:

【Saliency detection】Predicting When Saliency Maps Are Accurate and Eye Fixations Consistent.

Anna Volokitin:

Michael Gygli:

Xavier Boix:

【Saliency detection】Shallow and Deep Convolutional Networks for Saliency Prediction.

Junting Pan:

Elisa Sayrol:

Xavier Giro-i-Nieto:

Kevin Mc Guinness:

Noel E. O’Connor:

【Saliency detection】Deep Saliency With Encoded Low Level Distance Map and High Level Features.

Gayoung Lee:

Yu-Wing Tai:

Junmo Kim:

【Saliency detection】DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection.

Nian Liu:

Junwei Han:

【Saliency detection】Real-Time Salient Object Detection With a Minimum Spanning Tree.

Wei-Chih Tu:

Shengfeng He:

Qingxiong Yang: http://www.cs.cityu.edu.hk/~qiyang/

Shao-Yi Chien:

【Saliency detection】Learning to Co-Generate Object Proposals With a Deep Structured Network.

Zeeshan Hayder:

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

Mathieu Salzmann:

【Saliency detection】Pro Net: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks.

Chen Sun:

Manohar Paluri:

Ronan Collobert:

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

Lubomir Bourdev:

【Saliency detection】Recurrent Attentional Networks for Saliency Detection.

Jason Kuen:

Zhenhua Wang:

Gang Wang:

【Saliency detection】Backtracking Sc SPM Image Classifier for Weakly Supervised Top-Down Saliency.

Hisham Cholakkal:

Jubin Johnson:

Deepu Rajan:

【Saliency detection】Exemplar-Driven Top-Down Saliency Detection via Deep Association.

Shengfeng He:

Rynson W.H. Lau:

Qingxiong Yang: http://www.cs.cityu.edu.hk/~qiyang/

【Saliency detection】Unconstrained Salient Object Detection via Proposal Subset Optimization.

Jianming Zhang:

Stan Sclaroff:

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

Xiaohui Shen:

Brian Price:

Radomír Mech:

【Saliency detection】End-To-End Saliency Mapping via Probability Distribution Prediction.

Saumya Jetley:

Naila Murray:

Eleonora Vig:

【Saliency detection】Saliency Unified: A Deep Architecture for Simultaneous Eye Fixation Prediction and Salient Object Segmentation.

Srinivas S. S. Kruthiventi:

Vennela Gudisa:

Jaley H. Dholakiya:

Venkatesh Babu:


【Scene recognition】Anticipating Visual Representations From Unlabeled Video.

Carl Vondrick:

Hamed Pirsiavash:

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

【Scene recognition】Learning to Localize Little Landmarks.

Saurabh Singh:

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

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

【Scene recognition】Solving Small-Piece Jigsaw Puzzles by Growing Consensus.

Kilho Son:

daniel Moreno:

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

David B. Cooper:

【Scene recognition】Detection and Accurate Localization of Circular Fiducials Under Highly Challenging Conditions.

Lilian Calvet:

Pierre Gurdjos:

Carsten Griwodz:

Simone Gasparini:

【Scene recognition】Scene Recognition With CNNs: Objects, Scales and Dataset Bias.

Luis Herranz:

Shuqiang Jiang:

Xiangyang Li:

【Scene recognition】Learning Action Maps of Large Environments via First-Person Vision.

Nicholas Rhinehart:

Kris M. Kitani:

【Scene recognition】Deep Residual Learning for Image Recognition.

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

Xiangyu Zhang:

Shaoqing Ren:

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

【Scene recognition】You Lead, We Exceed: Labor-Free Video Concept Learning by Jointly Exploiting Web Videos and Images.

Chuang Gan:

Ting Yao:

Kuiyuan Yang:

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

Tao Mei:

【Scene recognition】Hierarchical Recurrent Neural Encoder for Video Representation With Application to Captioning.

Pingbo Pan:

Zhongwen Xu:

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

Fei Wu:

Yueting Zhuang:

【Scene recognition】From Keyframes to Key Objects: Video Summarization by Representative Object Proposal Selection.

Jingjing Meng:

Hongxing Wang:

Junsong Yuan:

Yap-Peng Tan:

【Scene recognition】Summary Transfer: Exemplar-Based Subset Selection for Video Summarization.

Ke Zhang:

Wei-Lun Chao:

Fei Sha:

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

【Scene recognition】Sketch Net: Sketch Classification With Web Images.

Hua Zhang:

Si Liu:

Changqing Zhang:

Wenqi Ren:

Rui Wang:

Xiaochun Cao:

【Scene recognition】Fine-Grained Image Classification by Exploring Bipartite-Graph Labels.

Feng Zhou:

Yuanqing Lin:

【Scene recognition】Picking Deep Filter Responses for Fine-Grained Image Recognition.

Xiaopeng Zhang:

Hongkai Xiong:

Wengang Zhou:

Weiyao Lin:

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

【Scene recognition】Large-Scale Location Recognition and the Geometric Burstiness Problem.

Torsten Sattler:

Michal Havlena:

Konrad Schindler:

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

【Scene recognition】Deep Decision Network for Multi-Class Image Classification.

Venkatesh N. Murthy:

Vivek Singh:

Terrence Chen:

Manmatha:

Dorin Comaniciu: http://coewww.rutgers.edu/riul/FORMER/comanici/

【Scene recognition】CNN-RNN: A Unified Framework for Multi-Label Image Classification.

Jiang Wang:

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

Junhua Mao:

Zhiheng Huang:

Chang Huang:

Wei Xu:

【Scene recognition】Visually Indicated Sounds.

Andrew Owens:

Phillip Isola:

Josh Mc Dermott:

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

Edward H. Adelson:

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

【Scene recognition】Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification.

Le Hou:

Dimitris Samaras:

Tahsin M. Kurc:

Yi Gao:

James E. Davis:

Joel H. Saltz:

【Scene recognition】Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation.

Hoo-Chang Shin:

Kirk Roberts:

Le Lu:

Dina Demner-Fushman:

Jianhua Yao:

Ronald M. Summers:

【Scene recognition】Automating Carotid Intima-Media Thickness Video Interpretation With Convolutional Neural Networks.

Jae Shin:

Nima Tajbakhsh:

  1. Todd Hurst:

Christopher B. Kendall:

Jianming Liang:

【Scene recognition】Online Collaborative Learning for Open-Vocabulary Visual Classifiers.

Hanwang Zhang:

Xindi Shang:

Wenzhuo Yang:

Huan Xu:

Huanbo Luan:

Tat-Seng Chua:

【Scene recognition】Discriminative Multi-Modal Feature Fusion for RGBD Indoor Scene Recognition.

Hongyuan Zhu:

Jean-Baptiste Weibel:

Shijian Lu:

【Scene recognition】Conditional Graphical Lasso for Multi-Label Image Classification.

Qiang Li:

Maoying Qiao:

Wei Bian:

Dacheng Tao:

【Scene recognition】Region Ranking SVM for Image Classification.

Zijun Wei:

Minh Hoai:

【Scene recognition】Video-Story Composition via Plot Analysis.

Jinsoo Choi:

Tae-Hyun Oh:

In So Kweon: http://rcv.kaist.ac.kr/

【Scene recognition】Newtonian Scene Understanding: Unfolding the Dynamics of Objects in Static Images.

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

Hessam Bagherinezhad:

Mohammad Rastegari:

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

【Scene recognition】A Continuous Occlusion Model for Road Scene Understanding.

Vikas Dhiman:

Quoc-Huy Tran:

Jason J. Corso:

Manmohan Chandraker:

【Scene recognition】Robust Visual Place Recognition With Graph Kernels.

Elena Stumm:

Christopher Mei:

Simon Lacroix:

Juan Nieto:

Marco Hutter:

Roland Siegwart:

【Scene recognition】Dense Cap: Fully Convolutional Localization Networks for Dense Captioning.

Justin Johnson:

Andrej Karpathy: http://cs.stanford.edu/people/karpathy/

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

【Scene recognition】Unsupervised Learning From Narrated Instruction Videos.

Jean-Baptiste Alayrac:

Piotr Bojanowski:

Nishant Agrawal:

Josef Sivic:

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

Simon Lacoste-Julien:

【Scene recognition】Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks.

Haonan Yu:

Jiang Wang:

Zhiheng Huang:

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

Wei Xu:

【Scene recognition】Jointly Modeling Embedding and Translation to Bridge Video and Language.

Yingwei Pan:

Tao Mei:

Ting Yao:

Houqiang Li:

Yong Rui:

【Scene recognition】We Are Humor Beings: Understanding and Predicting Visual Humor.

Arjun Chandrasekaran:

Ashwin K. Vijayakumar:

Stanislaw Antol:

Mohit Bansal:

Dhruv Batra:

  1. Lawrence Zitnick:

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

【Scene recognition】Where to Look: Focus Regions for Visual Question Answering.

Kevin J. Shih:

Saurabh Singh:

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

【Scene recognition】Ask Me Anything: Free-Form Visual Question Answering Based on Knowledge From External Sources.

Qi Wu:

Peng Wang:

Chunhua Shen:

Anthony Dick:

Anton van den Hengel:

【Scene recognition】Movie QA: Understanding Stories in Movies Through Question-Answering.

Makarand Tapaswi:

Yukun Zhu:

Rainer Stiefelhagen:

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

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

Sanja Fidler:

【Scene recognition】Image Captioning With Semantic Attention.

Quanzeng You:

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

Zhaowen Wang:

Chen Fang:

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

【Scene recognition】Answer-Type Prediction for Visual Question Answering.

Kushal Kafle:

Christopher Kanan:

【Scene recognition】Visual Word2Vec

Satwik Kottur:

Ramakrishna Vedantam:

José M. F. Moura:

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

【Scene recognition】Visual7W: Grounded Question Answering in Images.

Yuke Zhu:

Oliver Groth:

Michael Bernstein:

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

【Scene recognition】Learning Deep Structure-Preserving Image-Text Embeddings.

Liwei Wang:

Yin Li:

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

【Scene recognition】MSR-VTT: A Large Video Description Dataset for Bridging Video and Language.

Jun Xu:

Tao Mei:

Ting Yao:

Yong Rui:

【Scene recognition】Net VLAD: CNN Architecture for Weakly Supervised Place Recognition.

Relja Arandjelović:

Petr Gronat:

Akihiko Torii:

Tomas Pajdla:

Josef Sivic:

【Scene recognition】Learning Deep Representation for Imbalanced Classification.

Chen Huang:

Yining Li:

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

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

【Scene recognition】Bottom-Up and Top-Down Reasoning With Hierarchical Rectified Gaussians.

Peiyun Hu:

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

【Scene recognition】Fast Zero-Shot Image Tagging.

Yang Zhang:

Boqing Gong:

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

【Scene recognition】Modality and Component Aware Feature Fusion For RGB-D Scene Classification.

Anran Wang:

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

Jiwen Lu:

Tat-Jen Cham:

【Text recognition】A Text Detection System for Natural Scenes With Convolutional Feature Learning and Cascaded Classification.

Siyu Zhu:

Richard Zanibbi:

【Text recognition】Aggregating Image and Text Quantized Correlated Components.

Thi Quynh Nhi Tran:

Hervé Le Borgne:

Michel Crucianu:

【Text recognition】Traffic-Sign Detection and Classification in the Wild.

Zhe Zhu:

Dun Liang:

Songhai Zhang:

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

Baoli Li:

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

【Text recognition】Recursive Recurrent Nets With Attention Modeling for OCR in the Wild.

Chen-Yu Lee:

Simon Osindero:

【Text recognition】Less Is More: Zero-Shot Learning From Online Textual Documents With Noise Suppression.

Ruizhi Qiao:

Lingqiao Liu:

Chunhua Shen:

Anton van den Hengel:

【Text recognition】CNN-N-Gram for Handwriting Word Recognition.

Arik Poznanski:

Lior Wolf:

【Text recognition】Synthetic Data for Text Localisation in Natural Images.

Ankush Gupta:

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

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

【Text recognition】Predicting Motivations of Actions by Leveraging Text.

Carl Vondrick:

Deniz Oktay:

Hamed Pirsiavash:

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

【Text recognition】Canny Text Detector: Fast and Robust Scene Text Localization Algorithm.

Hojin Cho:

Myungchul Sung:

Bongjin Jun:

【Text recognition】Temporal Multimodal Learning in Audiovisual Speech Recognition.

Di Hu:

Xuelong Li:

Xiaoqiang lu:

【Text recognition】Multi-Oriented Text Detection With Fully Convolutional Networks.

Zheng Zhang:

Chengquan Zhang:

Wei Shen:

Cong Yao:

Wenyu Liu:

Xiang Bai:

【Text recognition】Robust Scene Text Recognition With Automatic Rectification.

Baoguang Shi:

Xinggang Wang:

Pengyuan Lyu:

Cong Yao:

Xiang Bai:

【Image retrieval】They Are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers.

Xiaojun Chang:

Yao-Liang Yu:

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

Eric P. Xing:

【Image retrieval】Shortlist Selection With Residual-Aware Distance Estimator for K-Nearest Neighbor Search.

Jae-Pil Heo:

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

Xiaohui Shen:

Jonathan Brandt:

Sung-eui Yoon:

【Image retrieval】Supervised Quantization for Similarity Search .

Xiaojuan Wang:

Ting Zhang:

Guo-Jun Qi:

Jinhui Tang:

Jingdong Wang:

【Image retrieval】Efficient Large-Scale Approximate Nearest Neighbor Search on the GPU.

Patrick Wieschollek:

Oliver Wang:

Alexander Sorkine-Hornung:

Hendrik P. A. Lensch:

【Image retrieval】Collaborative Quantization for Cross-Modal Similarity Search.

Ting Zhang:

Jingdong Wang:

【Image retrieval】Efficient Indexing of Billion-Scale Datasets of Deep Descriptors.

Artem Babenko:

Victor Lempitsky:

【Image retrieval】Deep Supervised Hashing for Fast Image Retrieval.

Haomiao Liu:

Ruiping Wang:

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

Xilin Chen:

【Image retrieval】Efficient Large-Scale Similarity Search Using Matrix Factorization.

Ahmet Iscen:

Michael Rabbat:

Teddy Furon:

【Image retrieval】Incremental Object Discovery in Time-Varying Image Collections.

Theodora Kontogianni:

Markus Mathias:

Bastian Leibe: http://www.vision.rwth-aachen.de/

【Image retrieval】How Hard Can It Be? Estimating the Difficulty of Visual Search in an Image.

Radu Tudor Ionescu:

Bogdan Alexe:

Marius Leordeanu:

Marius Popescu:

Dim P. Papadopoulos:

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

【Image retrieval】Comparative Deep Learning of Hybrid Representations for Image Recommendations.

Chenyi Lei:

Dong Liu:

Weiping Li:

Zheng-Jun Zha:

Houqiang Li:

【Image retrieval】CP-mt ML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval.

Binod Bhattarai:

Gaurav Sharma:

Frederic Jurie:

【Image retrieval】Natural Language Object Retrieval.

Ronghang Hu:

Huazhe Xu:

Marcus Rohrbach:

Jiashi Feng:

Kate Saenko:

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

【Image retrieval】Pairwise Linear Regression Classification for Image Set Retrieval.

Qingxiang Feng:

Yicong Zhou:

Rushi Lan:

【Image retrieval】Learnt Quasi-Transitive Similarity for Retrieval From Large Collections of Faces.

Ognjen Arandjelović:

【Image retrieval】GIFT: A Real-Time and Scalable 3D Shape Search Engine.

Song Bai:

Xiang Bai:

Zhichao Zhou:

Zhaoxiang Zhang: http://irip.buaa.edu.cn/~zxzhang/index.html

Longin Jan Latecki:

【Image retrieval】Multilinear Hyperplane Hashing.

Xianglong Liu:

Xinjie Fan:

Cheng Deng:

Zhujin Li:

Hao Su:

Dacheng Tao:

【Image retrieval】Large Scale Hard Sample Mining With Monte Carlo Tree Search.

Olivier Canévet:

François Fleuret:

【Image retrieval】Multi-Label Ranking From Positive and Unlabeled Data.

Atsushi Kanehira:

Tatsuya Harada:

【3D modeling】Recon Net: Non-Iterative Reconstruction of Images From Compressively Sensed Measurements.

Kuldeep Kulkarni:

Suhas Lohit:

Pavan Turaga:

Ronan Kerviche:

Amit Ashok:

【3D modeling】Determining Occlusions From Space and Time Image Reconstructions.

Juan-Manuel Pérez-Rúa:

Tomas Crivelli:

Patrick Bouthemy:

Patrick Pérez:

【3D modeling】Spatiotemporal Bundle Adjustment for Dynamic 3D Reconstruction.

Minh Vo:

Srinivasa G. Narasimhan:

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

【3D modeling】Inextensible Non-Rigid Shape-From-Motion by Second-Order Cone Programming.

Ajad Chhatkuli:

Daniel Pizarro:

Toby Collins:

Adrien Bartoli:

【3D modeling】Globally Optimal Manhattan Frame Estimation in Real-Time.

Kyungdon Joo:

Tae-Hyun Oh:

Junsik Kim:

In So Kweon: http://rcv.kaist.ac.kr/

【3D modeling】Mirror Surface Reconstruction Under an Uncalibrated Camera.

Kai Han:

Kwan-Yee K. Wong:

Dirk Schnieders:

Miaomiao Liu:

【3D modeling】A Hole Filling Approach Based on Background Reconstruction for View Synthesis in 3D Video.

Guibo Luo:

Yuesheng Zhu:

Zhaotian Li:

Liming Zhang:

【3D modeling】Efficient Intersection of Three Quadrics and Applications in Computer Vision.

Zuzana Kukelova:

Jan Heller:

Andrew Fitzgibbon:

【3D modeling】Gradient-Domain Image Reconstruction Framework With Intensity-Range and Base-Structure Constraints.

Takashi Shibata:

Masayuki Tanaka:

Masatoshi Okutomi:

【3D modeling】Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-Resolution Model for Multi-Class Volumetric Labeling.

Maroš Bláha:

Christoph Vogel:

Audrey Richard:

Jan D. Wegner:

Thomas Pock:

Konrad Schindler:

【3D modeling】Warp Net: Weakly Supervised Matching for Single-View Reconstruction.

Angjoo Kanazawa:

David W. Jacobs:

Manmohan Chandraker:

【3D modeling】What Sparse Light Field Coding Reveals About Scene Structure.

Ole Johannsen:

Antonin Sulc:

Bastian Goldluecke:

【3D modeling】Online Reconstruction of Indoor Scenes From RGB-D Streams.

Hao Wang:

Jun Wang:

Wang Liang:

【3D modeling】Patches, Planes and Probabilities: A Non-Local Prior for Volumetric 3D Reconstruction.

Ali Osman Ulusoy:

Michael J. Black: http://ps.is.tue.mpg.de/person/black

Andreas Geiger:

【3D modeling】Sparse to Dense 3D Reconstruction From Rolling Shutter Images.

Olivier Saurer:

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

Gim Hee Lee:

【3D modeling】Automated 3D Face Reconstruction From Multiple Images Using Quality Measures.

Marcel Piotraschke:

Volker Blanz:

【3D modeling】Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs.

Liuhao Ge:

Hui Liang:

Junsong Yuan:

Daniel Thalmann:

【3D modeling】Understanding Real World Indoor Scenes With Synthetic Data.

Ankur Handa:

Viorica Pătrăucean:

Vijay Badrinarayanan:

Simon Stent:

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

【3D modeling】Adaptive 3D Face Reconstruction From Unconstrained Photo Collections.

Joseph Roth:

Yiying Tong:

Xiaoming Liu:

【3D modeling】3D Reconstruction of Transparent Objects With Position-Normal Consistency.

Yiming Qian:

Minglun Gong:

Yee Hong Yang:

【3D modeling】Single Image Object Modeling Based on BRDF and R-Surfaces Learning.

Fabrizio Natola:

Valsamis Ntouskos:

Fiora Pirri:

Marta Sanzari:

【3D modeling】UAV Sensor Fusion With Latent-Dynamic Conditional Random Fields in Coronal Plane Estimation.

Amir M. Rahimi:

Raphael Ruschel:

B.S. Manjunath:

【3D modeling】Temporally Coherent 3D Reconstruction of Complex Dynamic Scenes.

Armin Mustafa:

Hansung Kim:

Jean-Yves Guillemaut:

Adrian Hilton:

【3D modeling】Consensus of Non-Rigid Reconstructions.

Minsik Lee:

Jungchan Cho:

Songhwai Oh:

【3D modeling】Efficient 3D Room Shape Recovery From a Single Panorama.

Hao Yang:

Hui Zhang:

【3D modeling】Semantic 3D Reconstruction With Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint.

Nikolay Savinov:

Christian Häne:

Ľubor Ladický:

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

【3D modeling】Theory and Practice of Structure-From-Motion Using Affine Correspondences.

Carolina Raposo:

João P. Barreto:

【3D modeling】Just Look at the Image: Viewpoint-Specific Surface Normal Prediction for Improved Multi-View Reconstruction.

Silvano Galliani:

Konrad Schindler:

【3D modeling】From Dusk Till Dawn: Modeling in the Dark.

Filip Radenović:

Johannes L. Schönberger:

Dinghuang Ji:

Jan-Michael Frahm:

Ondřej Chum:

Jiří Matas:

【3D modeling】Accelerated Generative Models for 3D Point Cloud Data.

Benjamin Eckart:

Kihwan Kim:

Alejandro Troccoli:

Alonzo Kelly:

Jan Kautz:

【3D modeling】Motion From Structure

Jayakorn Vongkulbhisal:

Ricardo Cabral:

Fernando De la Torre:

João P. Costeira:

【3D modeling】Detecting Vanishing Points Using Global Image Context in a Non-Manhattan World.

Menghua Zhai:

Scott Workman:

Nathan Jacobs:

【3D modeling】A Field Model for Repairing 3D Shapes.

Duc Thanh Nguyen:

Binh-Son Hua:

Khoi Tran:

Quang-Hieu Pham:

Sai-Kit Yeung:

【3D modeling】A Paradigm for Building Generalized Models of Human Image Perception Through Data Fusion.

Shaojing Fan:

Tian-Tsong Ng:

Bryan L. Koenig:

Ming Jiang:

Qi Zhao:

【3D modeling】Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines.

Chi Nhan Duong:

Khoa Luu:

Kha Gia Quach:

Tien D. Bui:

【Feature matching】Learning to Assign Orientations to Feature Points.

Kwang Moo Yi:

Yannick Verdie:

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

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

【Feature matching】Accumulated Stability Voting: A Robust Descriptor From Descriptors of Multiple Scales.

Tsun-Yi Yang:

Yen-Yu Lin:

Yung-Yu Chuang:

【Feature matching】Co Ma L: Good Features to Match on Object Boundaries.

Swarna K. Ravindran:

Anurag Mittal: http://www.cse.iitm.ac.in/~amittal/

【Feature matching】Learning Compact Binary Descriptors With Unsupervised Deep Neural Networks.

Kevin Lin:

Jiwen Lu:

Chu-Song Chen:

Jie Zhou:

【Feature matching】Embedding Label Structures for Fine-Grained Feature Representation.

Xiaofan Zhang:

Feng Zhou:

Yuanqing Lin:

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

【Feature matching】Learning Deep Features for Discriminative Localization.

Bolei Zhou:

Aditya Khosla:

Agata Lapedriza:

Aude Oliva:

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

【Feature matching】Constrained Deep Transfer Feature Learning and Its Applications.

Yue Wu:

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

【Feature matching】Kernel Approximation via Empirical Orthogonal Decomposition for Unsupervised Feature Learning.

Yusuke Mukuta:

Tatsuya Harada:

【Feature matching】Learning Local Image Descriptors With Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions.

Vijay Kumar B G:

Gustavo Carneiro: http://cs.adelaide.edu.au/~carneiro/research.html

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

【Pose estimation】One-Shot Learning of Scene Locations via Feature Trajectory Transfer.

Roland Kwitt:

Sebastian Hegenbart:

Marc Niethammer:

【Pose estimation】Temporal Epipolar Regions.

Mor Dar:

Yael Moses:

【Pose estimation】Optimal Relative Pose With Unknown Correspondences.

Johan Fredriksson:

Viktor Larsson:

Carl Olsson:

Fredrik Kahl:

【Pose estimation】Homography Estimation From the Common Self-Polar Triangle of Separate Ellipses.

Haifei Huang:

Hui Zhang:

Yiu-ming Cheung:

【Pose estimation】A Consensus-Based Framework for Distributed Bundle Adjustment.

Anders Eriksson:

John Bastian:

Tat-Jun Chin:

Mats Isaksson:

【Pose estimation】A Direct Least-Squares Solution to the Pn P Problem With Unknown Focal Length.

Yinqiang Zheng:

Laurent Kneip:

【Pose estimation】Camera Calibration From Periodic Motion of a Pedestrian.

Shiyao Huang:

Xianghua Ying:

Jiangpeng Rong:

Zeyu Shang:

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

【Pose estimation】Single Image Camera Calibration With Lenticular Arrays for Augmented Reality.

Ian Schillebeeckx:

Robert Pless:

【Pose estimation】Rolling Shutter Absolute Pose Problem With Known Vertical Direction.

Cenek Albl:

Zuzana Kukelova:

Tomas Pajdla:

【Pose estimation】Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image.

Eric Brachmann:

Frank Michel:

Alexander Krull:

Michael Ying Yang:

Stefan Gumhold:

carsten Rother:

【Pose estimation】Multicamera Calibration From Visible and Mirrored Epipoles.

Andrey Bushnevskiy:

Lorenzo Sorgi:

Bodo Rosenhahn:

【Pose estimation】Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd.

Andreas Doumanoglou:

Rigas Kouskouridas:

Sotiris Malassiotis:

Tae-Kyun Kim:

【Pose estimation】Joint Multiview Segmentation and Localization of RGB-D Images Using Depth-Induced Silhouette Consistency.

Chi Zhang:

Zhiwei Li:

Rui Cai:

Hongyang Chao:

Yong Rui:

【Pose estimation】6D Dynamic Camera Relocalization From Single Reference Image.

Wei Feng:

Fei-Peng Tian:

Qian Zhang:

Jizhou Sun:

【Pose estimation】Camera Calibration From Dynamic Silhouettes Using Motion Barcodes.

Gil Ben-Artzi:

Yoni Kasten:

Shmuel Peleg: http://www.cs.huji.ac.il/~peleg/

Michael Werman: http://www.cs.huji.ac.il/~werman/

【Pose estimation】Structure-From-Motion Revisited.

Johannes L. Schönberger:

Jan-Michael Frahm:

【Pose estimation】Constructing Canonical Regions for Fast and Effective View Selection.

Wencheng Wang:

Tianhao Gao:

【Pose estimation】Prior-Less Compressible Structure From Motion.

Chen Kong:

Simon Lucey:

【Pose estimation】Rolling Shutter Camera Relative Pose: Generalized Epipolar Geometry.

Yuchao Dai:

Hongdong Li:

Laurent Kneip:

【Pose estimation】Structure From Motion With Objects.

Marco Crocco:

Cosimo Rubino:

Alessio Del Bue:

【Pose estimation】Isometric Non-Rigid Shape-From-Motion in Linear Time.

Shaifali Parashar:

Daniel Pizarro:

Adrien Bartoli:

【Pose estimation】Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees.

Jianhui Chen:

Hoang M. Le:

Peter Carr:

Yisong Yue:

James J. Little:

【Pose estimation】Structured Feature Learning for Pose Estimation.

Xiao Chu:

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

Hongsheng Li:

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

【Pose estimation】Convolutional Pose Machines.

Shih-En Wei:

Varun Ramakrishna:

Takeo Kanade:

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

【Stereo matching】Learning Dense Correspondence via 3D-Guided Cycle Consistency.

Tinghui Zhou:

Philipp Krähenbuhl:

Mathieu Aubry:

Qixing Huang:

Alexei A. Efros:

【Stereo matching】Piecewise-Planar 3D Approximation From Wide-Baseline Stereo.

Cédric Verleysen:

Christophe De Vleeschouwer:

【Stereo matching】A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo.

Boxin Shi:

Zhe Wu:

Zhipeng Mo:

Dinglong Duan:

Sai-Kit Yeung:

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

【Stereo matching】Depth From Semi-Calibrated Stereo and Defocus.

Ting-Chun Wang:

Manohar Srikanth:

Ravi Ramamoorthi:

【Stereo matching】Coordinating Multiple Disparity Proposals for Stereo Computation.

Ang Li:

Dapeng Chen:

Yuanliu Liu:

Zejian Yuan:

【Stereo matching】Dense Monocular Depth Estimation in Complex Dynamic Scenes.

René Ranftl:

Vibhav Vineet:

Qifeng Chen:

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

【Stereo matching】Stereo Matching With Color and Monochrome Cameras in Low-Light Conditions.

Hae-Gon Jeon:

Joon-Young Lee:

Sunghoon Im:

Hyowon Ha:

In So Kweon: http://rcv.kaist.ac.kr/

【Stereo matching】Joint Recovery of Dense Correspondence and Cosegmentation in Two Images.

Tatsunori Taniai:

Sudipta N. Sinha:

Yoichi Sato:

【Stereo matching】Uncalibrated Photometric Stereo by Stepwise Optimization Using Principal Components of Isotropic BRDFs.

Keisuke Midorikawa:

Toshihiko Yamasaki:

Kiyoharu Aizawa:

【Stereo matching】Unbiased Photometric Stereo for Colored Surfaces: A Variational Approach.

Yvain Quéau:

Roberto Mecca:

Jean-Denis Durou:

【Stereo matching】Real-Time Depth Refinement for Specular Objects.

Roy Or-El:

Rom Hershkovitz:

Aaron Wetzler:

Guy Rosman:

Alfred M. Bruckstein:

Ron Kimmel:

【Stereo matching】High-Quality Depth From Uncalibrated Small Motion Clip.

Hyowon Ha:

Sunghoon Im:

Jaesik Park:

Hae-Gon Jeon:

In So Kweon: http://rcv.kaist.ac.kr/

【Stereo matching】Hyper Depth: Learning Depth From Structured Light Without Matching.

Sean Ryan Fanello:

Christoph Rhemann:

Vladimir Tankovich:

Adarsh Kowdle:

Sergio Orts Escolano:

David Kim:

Shahram Izadi:

【Stereo matching】Monocular Depth Estimation Using Neural Regression Forest.

Anirban Roy:

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

【Stereo matching】Deep Stereo: Learning to Predict New Views From the World’s Imagery.

John Flynn:

Ivan Neulander:

James Philbin:

Noah Snavely:

【Stereo matching】Efficient Deep Learning for Stereo Matching.

Wenjie Luo:

Alexander G. Schwing:

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

【Optical flow】Simultaneous Optical Flow and Intensity Estimation From an Event Camera.

Patrick Bardow:

Andrew J. Davison:

Stefan Leutenegger:

【Optical flow】Robust Optical Flow Estimation of Double-Layer Images Under Transparency or Reflection.

Jiaolong Yang:

Hongdong Li:

Yuchao Dai:

Robby T. Tan:

【Optical flow】Proposal Flow.

Bumsub Ham:

Minsu Cho:

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

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

【Optical flow】Force From Motion: Decoding Physical Sensation in a First Person Video.

Hyun Soo Park:

jyh-Jing Hwang:

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

【Optical flow】Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video.

Dinesh Jayaraman:

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

【Optical flow】A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation.

Nikolaus Mayer:

Eddy Ilg:

Philip Häusser:

Philipp Fischer:

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

Alexey Dosovitskiy:

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

【Optical flow】Patch Batch: A Batch Augmented Loss for Optical Flow.

David Gadot:

Lior Wolf:

【Optical flow】Full Flow: Optical Flow Estimation By Global Optimization Over Regular Grids.

Qifeng Chen:

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

【Optical flow】Efficient Coarse-To-Fine Patch Match for Large Displacement Optical Flow.

Yinlin Hu:

Rui Song:

Yunsong Li:

【Region matching】The Global Patch Collider.

Shenlong Wang:

Sean Ryan Fanello:

Christoph Rhemann:

Shahram Izadi:

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

【Region matching】Progressive Feature Matching With Alternate Descriptor Selection and Correspondence Enrichment.

Yuan-Ting Hu:

Yen-Yu Lin:

【Region matching】Needle-Match: Reliable Patch Matching Under High Uncertainty.

Or Lotan:

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

【Region matching】Regularity-Driven Facade Matching Between Aerial and Street Views.

Mark Wolff:

Robert T. Collins:

Yanxi Liu:

【Region matching】Using Spatial Order to Boost the Elimination of Incorrect Feature Matches.

Lior Talker:

Yael Moses:

Ilan Shimshoni:

【Region matching】A Probabilistic Framework for Color-Based Point Set Registration.

Martin Danelljan:

Giulia Meneghetti:

Fahad Shahbaz Khan:

Michael Felsberg:

【Region matching】Conformal Surface Alignment With Optimal Möbius Search.

Huu Le:

Tat-Jun Chin:

David Suter:

【Region matching】Learned Binary Spectral Shape Descriptor for 3D Shape Correspondence.

Jin Xie:

Meng Wang:

Yi Fang:

【Region matching】Multiple Model Fitting as a Set Coverage Problem.

Luca Magri:

Andrea Fusiello:

【Region matching】Joint Unsupervised Deformable Spatio-Temporal Alignment of Sequences.

Lazaros Zafeiriou:

Epameinondas Antonakos:

Stefanos Zafeiriou:

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

【Region matching】Learning to Match Aerial Images With Deep Attentive Architectures.

Hani Altwaijry:

Eduard Trulls:

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

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

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

【Region matching】Kinematic Structure Correspondences via Hypergraph Matching.

Hyung Jin Chang:

Tobias Fischer:

Maxime Petit:

Martina Zambelli:

Yiannis Demiris:

【Region matching】Functional Faces: Groupwise Dense Correspondence Using Functional Maps.

Chao Zhang:

William A. P. Smith:

Arnaud Dessein:

Nick Pears:

Hang Dai:

【Region matching】Geospatial Correspondences for Multimodal Registration.

Diego Marcos:

Raffay Hamid:

Devis Tuia:

【Region matching】GOGMA: Globally-Optimal Gaussian Mixture Alignment.

Dylan Campbell:

Lars Petersson:

【Region matching】Estimating Correspondences of Deformable Objects In-The-Wild.

Yuxiang Zhou:

Epameinondas Antonakos:

Joan Alabort-i-Medina:

Anastasios Roussos:

Stefanos Zafeiriou:

【Region matching】Gravitational Approach for Point Set Registration.

Vladislav Golyanik:

Sk Aziz Ali:

Didier Stricker:

【Region matching】Context-Aware Gaussian Fields for Non-Rigid Point Set Registration.

Gang Wang:

Zhicheng Wang:

Yufei Chen:

Qiangqiang Zhou:

Weidong Zhao:

【Region matching】Globally Optimal Rigid Intensity Based Registration: A Fast Fourier Domain Approach.

Behrooz Nasihatkon:

Frida Fejne:

Fredrik Kahl:

【Region matching】Marr Revisited: 2D-3D Alignment via Surface Normal Prediction.

Aayush Bansal:

Bryan Russell:

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

【Image editing】Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer.

Oriel Frigo:

Neus Sabater:

Julie Delon:

Pierre Hellier:

【Computational photography】From Noise Modeling to Blind Image Denoising.

Fengyuan Zhu:

Guangyong Chen:

Pheng-Ann Heng:

【Computational photography】Efficient and Robust Color Consistency for Community Photo Collections.

Jaesik Park:

Yu-Wing Tai:

Sudipta N. Sinha:

In So Kweon: http://rcv.kaist.ac.kr/

【Computational photography】Soft-Segmentation Guided Object Motion Deblurring.

Jinshan Pan:

Zhe Hu:

Zhixun Su:

Hsin-Ying Lee:

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

【Computational photography】Multiview Image Completion With Space Structure Propagation.

Seung-Hwan Baek:

Inchang Choi:

Min H. Kim:

【Computational photography】Composition-Preserving Deep Photo Aesthetics Assessment.

Long Mai:

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

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

【Computational photography】Automatic Image Cropping : A Computational Complexity Study.

Jiansheng Chen:

Gaocheng Bai:

Shaoheng Liang:

Zhengqin Li:

【Computational photography】Information-Driven Adaptive Structured-Light Scanners.

Guy Rosman:

Daniela Rus:

John W. Fisher III:

【Computational photography】ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks Using Angle Sensitive Pixels.

Huaijin G. Chen:

Suren Jayasuriya:

Jiyue Yang:

Judy Stephen:

Sriram Sivaramakrishnan:

Ashok Veeraraghavan:

Alyosha Molnar:

【Computational photography】Computational Imaging for VLBI Image Reconstruction.

Katherine L. Bouman:

Michael D. Johnson:

Daniel Zoran:

Vincent L. Fish:

Sheperd S. Doeleman:

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

【Computational photography】Video2GIF: Automatic Generation of Animated GIFs From Video.

Michael Gygli:

Yale Song:

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

【Computational photography】Blind Image Deblurring Using Dark Channel Prior.

Jinshan Pan:

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

Hanspeter Pfister:

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

【Computational photography】Deeply-Recursive Convolutional Network for Image Super-Resolution.

Jiwon Kim:

Jung Kwon Lee:

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

【Computational photography】Accurate Image Super-Resolution Using Very Deep Convolutional Networks.

Jiwon Kim:

Jung Kwon Lee:

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

【Computational photography】RAW Image Reconstruction Using a Self-Contained s RGB-JPEG Image With Only KB Overhead.

Rang M. H. Nguyen:

Michael S. Brown:

【Computational photography】Group MAD Competition – A New Methodology to Compare Objective Image Quality Models.

Kede Ma:

Qingbo Wu:

Zhou Wang:

Zhengfang Duanmu:

Hongwei Yong:

Hongliang Li:

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

【Computational photography】Non-Local Image Dehazing.

Dana Berman:

Tali treibitz:

Shai Avidan:

【Computational photography】A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising.

Seonghyeon Nam:

Youngbae Hwang:

Yasuyuki Matsushita:

Seon Joo Kim:

【Computational photography】Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization.

Qi Xie:

Qian Zhao:

Deyu Meng:

Zongben Xu:

Shuhang Gu:

Wangmeng Zuo:

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

【Computational photography】A Comparative Study for Single Image Blind Deblurring.

Wei-Sheng Lai:

Jia-Bin Huang:

Zhe Hu:

Narendra Ahuja: http://vision.ai.illinois.edu/publications.htm

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

【Computational photography】Heterogeneous Light Fields.

Maximilian Diebold:

Bernd Jähne:

Alexander Gatto:

【Computational photography】Blind Image Deconvolution by Automatic Gradient Activation.

Dong Gong:

Mingkui Tan:

Yanning Zhang:

Anton van den Hengel:

Qinfeng Shi:

【Computational photography】PSy Co: Manifold Span Reduction for Super Resolution.

Eduardo Pérez-Pellitero:

Jordi Salvador:

Javier Ruiz-Hidalgo:

Bodo Rosenhahn:

【Computational photography】Parametric Object Motion From Blur.

Jochen Gast:

Anita Sellent:

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

【Computational photography】Image Deblurring Using Smartphone Inertial Sensors.

Zhe Hu:

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

Stephen Lin:

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

【Computational photography】Seven Ways to Improve Example-Based Single Image Super Resolution.

Radu Timofte:

Rasmus Rothe:

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

【Computational photography】Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network.

Wenzhe Shi:

Jose Caballero:

Ferenc Huszár:

Johannes Totz:

Andrew P. Aitken:

Rob Bishop:

Daniel Rueckert:

Zehan Wang:

【Computational photography】Recurrent Face Aging.

Wei Wang:

Zhen Cui:

Yan Yan:

Jiashi Feng:

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

Xiangbo Shu:

Nicu Sebe:

【Computational photography】Face2Face: Real-Time Face Capture and Reenactment of RGB Videos.

Justus Thies:

Michael Zollhöfer:

Marc Stamminger:

Christian Theobalt:

Matthias Nießner:

【Computational photography】Image Style Transfer Using Convolutional Neural Networks.

Leon A. Gatys:

Alexander S. Ecker:

Matthias Bethge:

【Computational photography】Simultaneous Estimation of Near IR BRDF and Fine-Scale Surface Geometry.

Gyeongmin Choe:

Srinivasa G. Narasimhan:

In So Kweon: http://rcv.kaist.ac.kr/

【Computational photography】Do It Yourself Hyperspectral Imaging With Everyday Digital Cameras.

Seoung Wug Oh:

Michael S. Brown:

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

Seon Joo Kim:

【Computational photography】Automatic Content-Aware Color and Tone Stylization.

Joon-Young Lee:

Kalyan Sunkavalli:

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

Xiaohui Shen:

In So Kweon: http://rcv.kaist.ac.kr/

【Computational photography】Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis.

Chuan Li:

Michael Wand:

【Computational photography】Context Encoders: Feature Learning by Inpainting.

Deepak Pathak:

Philipp Krähenbuhl:

Jeff Donahue:

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

Alexei A. Efros:

【Computational photography】Laplacian Patch-Based Image Synthesis.

Joo Ho Lee:

Inchang Choi:

Min H. Kim:

【Computational photography】Rain Streak Removal Using Layer Priors.

Yu Li:

Robby T. Tan:

Xiaojie Guo:

Jiangbo Lu:

Michael S. Brown:

【Computational photography】Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion.

Jialei Wang:

Peder A. Olsen:

Andrew R. Conn:

Aurélie C. Lozano:

【Computational photography】D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images.

Zhangyang Wang:

Ding Liu:

Shiyu Chang:

Qing Ling:

Yingzhen Yang:

Thomas S. Huang:

【Computational photography】From Bows to Arrows: Rolling Shutter Rectification of Urban Scenes.

Vijay Rengarajan:

Ambasamudram N. Rajagopalan:

Rangarajan Aravind:

【Computational photography】Robust Kernel Estimation With Outliers Handling for Image Deblurring.

Jinshan Pan:

Zhouchen Lin: http://www.cis.pku.edu.cn/faculty/vision/zlin/zlin.htm

Zhixun Su:

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

【Computational photography】Seeing Behind the Camera: Identifying the Authorship of a Photograph.

Christopher Thomas:

Adriana Kovashka:

【Computational photography】Amplitude Modulated Video Camera – Light Separation in Dynamic Scenes.

Amir Kolaman:

Maxim Lvov:

Rami Hagege:

Hugo Guterman:

【Computational photography】Exploiting Spectral-Spatial Correlation for Coded Hyperspectral Image Restoration.

Ying Fu:

Yinqiang Zheng:

Imari Sato:

Yoichi Sato:

【Computational photography】Variable Aperture Light Field Photography: Overcoming the Diffraction-Limited Spatio-Angular Resolution Tradeoff.

Julie Chang:

Isaac Kauvar:

Xuemei Hu:

Gordon Wetzstein:

【Computational photography】Convolutional Networks for Shape From Light Field.

Stefan Heber:

Thomas Pock:

【Computational photography】Panoramic Stereo Videos With a Single Camera.

Rajat Aggarwal:

Amrisha Vohra:

Anoop M. Namboodiri:

【Computational photography】The Next Best Underwater View.

Mark Sheinin:

Yoav Y. Schechner:

【Computational photography】Reconstructing Shapes and Appearances of Thin Film Objects Using RGB Images.

Yoshie Kobayashi:

Tetsuro Morimoto:

Imari Sato:

Yasuhiro Mukaigawa:

Takao Tomono:

Katsushi Ikeuchi:

【Computational photography】Noisy Label Recovery for Shadow Detection in Unfamiliar Domains.

Tomás F. Yago Vicente:

Minh Hoai:

Dimitris Samaras:

【Computational photography】Using Self-Contradiction to Learn Confidence Measures in Stereo Vision.

Christian Mostegel:

Markus Rumpler:

Friedrich Fraundorfer:

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

【Computational photography】An Egocentric Look at Video Photographer Identity.

Yedid Hoshen:

Shmuel Peleg: http://www.cs.huji.ac.il/~peleg/

【Computational photography】Recovering Transparent Shape From Time-Of-Flight Distortion.

Kenichiro Tanaka:

Yasuhiro Mukaigawa:

Hiroyuki Kubo:

Yasuyuki Matsushita:

Yasushi Yagi:

【Computational photography】Robust Light Field Depth Estimation for Noisy Scene With Occlusion.

Williem:

In Kyu Park:

【Computational photography】Rotational Crossed-Slit Light Field.

Nianyi Li:

Haiting Lin:

Bilin Sun:

Mingyuan Zhou:

Jingyi Yu:

【Computational photography】An Empirical Evaluation of Current Convolutional Architectures’ Ability to Manage Nuisance Location and Scale Variability.

Nikolaos Karianakis:

Jingming Dong:

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

【Computational photography】Deep Reflectance Maps.

Konstantinos Rematas:

Tobias Ritschel:

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

Efstratios Gavves:

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

【Computational photography】Semantic Filtering.

Qingxiong Yang: http://www.cs.cityu.edu.hk/~qiyang/

【Computational photography】TGIF: A New Dataset and Benchmark on Animated GIF Description.

Yuncheng Li:

Yale Song:

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

Joel Tetreault:

Larry Goldberg:

Alejandro Jaimes: http://www.alexjaimes.com/

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

【Computational photography】Deep Gaussian Conditional Random Field Network: A Model-Based Deep Network for Discriminative Denoising.

Raviteja Vemulapalli:

Oncel Tuzel:

Ming-Yu Liu:

【Computational photography】Event-Specific Image Importance.

Yufei Wang:

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

Xiaohui Shen:

Radomír Mĕch:

Gavin Miller:

Garrison W. Cottrell:

【Computational photography】Deep Canonical Time Warping.

George Trigeorgis:

Mihalis A. Nicolaou:

Stefanos Zafeiriou:

Björn W. Schuller:

【Computational photography】SVBRDF-Invariant Shape and Reflectance Estimation From Light-Field Cameras.

Ting-Chun Wang:

Manmohan Chandraker:

Alexei A. Efros:

Ravi Ramamoorthi:

【Texture analysis】Equiangular Kernel Dictionary Learning With Applications to Dynamic Texture Analysis.

Yuhui Quan:

Chenglong Bao:

Hui Ji:

【Texture analysis】Geometry-Informed Material Recognition.

Joseph De Gol:

Mani Golparvar-Fard:

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

【Texture analysis】Visualizing and Understanding Deep Texture Representations.

Tsung-Yu Lin:

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

【Texture analysis】Material Classification Using Raw Time-Of-Flight Measurements.

Shuochen Su:

Felix Heide:

Robin Swanson:

Jonathan Klein:

Clara Callenberg:

Matthias Hullin:

Wolfgang Heidrich:

【Texture analysis】RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian With Application to Material Recognition.

Qilong Wang:

Peihua Li:

Wangmeng Zuo:

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

【Texture analysis】Sparse Coding for Third-Order Super-Symmetric Tensor Descriptors With Application to Texture Recognition.

Piotr Koniusz:

Anoop Cherian:

【Illumation analysis】Two Illuminant Estimation and User Correction Preference.

Dongliang Cheng:

Abdelrahman Abdelhamed:

Brian Price:

Scott Cohen:

Michael S. Brown:

【Illumation analysis】A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation.

Xueyang Fu:

Delu Zeng:

Yue Huang:

Xiao-Ping Zhang:

Xinghao Ding:

【Medical image】A Nonlinear Regression Technique for Manifold Valued Data With Applications to Medical Image Analysis.

Monami Banerjee:

Rudrasis Chakraborty:

Edward Ofori:

Michael S. Okun:

David E. Viallancourt:

Baba C. Vemuri:

【Data clustering】Closed-Form Training of Mahalanobis Distance for Supervised Clustering.

Marc T. Law:

Yao Liang Yu:

Matthieu Cord:

Eric P. Xing:

【Data clustering】Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit.

Chong You:

Daniel Robinson:

René Vidal:

【Data clustering】Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering.

Chong You:

Chun-Guang Li:

Daniel P. Robinson:

René Vidal:

【Data clustering】Joint Unsupervised Learning of Deep Representations and Image Clusters.

Jianwei Yang:

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

Dhruv Batra:

【Data clustering】Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds.

Ming Yin:

Yi Guo:

Junbin Gao:

Zhaoshui He:

Shengli Xie:

【Data clustering】Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming.

Yongfang Cheng:

Yin Wang:

Mario Sznaier:

Octavia Camps:

【Data clustering】Semi-Supervised Vocabulary-Informed Learning.

Yanwei Fu:

Leonid Sigal:

【Data clustering】Simultaneous Clustering and Model Selection for Tensor Affinities.

Zhuwen Li:

Shuoguang Yang:

Loong-Fah Cheong:

Kim-Chuan Toh:

【Data clustering】Discriminatively Embedded K-Means for Multi-View Clustering.

Jinglin Xu:

Junwei Han:

Feiping Nie:

【Data clustering】Random Features for Sparse Signal Classification.

Jen-Hao Rick Chang:

Aswin C. Sankaranarayanan:

  1. V. K. Vijaya Kumar:

【Data clustering】FANNG: Fast Approximate Nearest Neighbour Graphs.

Ben Harwood:

Tom Drummond:

【Space reduction】Sparse Coding and Dictionary Learning With Linear Dynamical Systems.

Wenbing Huang:

Fuchun Sun:

Lele Cao:

Deli Zhao:

Huaping Liu:

Mehrtash Harandi:

【Space reduction】When VLAD Met Hilbert.

Mehrtash Harandi:

Mathieu Salzmann:

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

【Space reduction】Robust Tensor Factorization With Unknown Noise.

Xi’ai Chen:

Zhi Han:

Yao Wang:

Qian Zhao:

Deyu Meng:

Yandong Tang:

【Space reduction】Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization.

Canyi Lu:

Jiashi Feng:

Yudong Chen:

Wei Liu:

Zhouchen Lin: http://www.cis.pku.edu.cn/faculty/vision/zlin/zlin.htm

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

【Space reduction】Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations.

Xian Wei:

Hao Shen:

Martin Kleinsteuber:

【Space reduction】Trust No One: Low Rank Matrix Factorization Using Hierarchical RANSAC.

Magnus Oskarsson:

Kenneth Batstone:

Kalle Åström:

【Space reduction】Sparse Coding for Classification via Discrimination Ensemble.

Yuhui Quan:

Yong Xu:

Yuping Sun:

Yan Huang:

Hui Ji:

【Machine learning】Multi-Cue Zero-Shot Learning With Strong Supervision.

Zeynep Akata:

Mateusz Malinowski:

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

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

【Machine learning】Latent Embeddings for Zero-Shot Classification.

Yongqin Xian:

Zeynep Akata:

Gaurav Sharma:

Quynh Nguyen:

Matthias Hein:

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

【Machine learning】Learning Attributes Equals Multi-Source Domain Generalization.

Chuang Gan:

Tianbao Yang:

Boqing Gong:

【Machine learning】Joint Probabilistic Matching Using m-Best Solutions.

Seyed Hamid Rezatofighi:

Anton Milan:

Zhen Zhang:

Qinfeng Shi:

Anthony Dick:

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

【Machine learning】Compact Bilinear Pooling.

Yang Gao:

Oscar Beijbom:

Ning Zhang:

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

【Machine learning】Pairwise Matching Through Max-Weight Bipartite Belief Propagation.

Zhen Zhang:

Qinfeng Shi:

Julian Mc Auley:

Wei Wei:

Yanning Zhang:

Anton van den Hengel:

【Machine learning】Structured Feature Similarity With Explicit Feature Map.

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

【Machine learning】Structured Regression Gradient Boosting.

Ferran Diego:

Fred A. Hamprecht:

【Machine learning】Loss Functions for Top-k Error: Analysis and Insights.

Maksim Lapin:

Matthias Hein:

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

【Machine learning】Metric Learning as Convex Combinations of Local Models With Generalization Guarantees.

Valentina Zantedeschi:

Rémi Emonet:

Marc Sebban:

【Machine learning】Improved Hamming Distance Search Using Variable Length Substrings.

Eng-Jon Ong:

Miroslaw Bober:

【Machine learning】Fast Algorithms for Linear and Kernel SVM+.

Wen Li:

Dengxin Dai:

Mingkui Tan:

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

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

【Machine learning】Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project

Won Hwa Kim:

Hyunwoo J. Kim:

Nagesh Adluru:

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

【Machine learning】Cross Modal Distillation for Supervision Transfer.

Saurabh Gupta:

Judy Hoffman:

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

【Machine learning】Learning Aligned Cross-Modal Representations From Weakly Aligned Data.

Lluís Castrejón:

Yusuf Aytar:

Carl Vondrick:

Hamed Pirsiavash:

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

【Machine learning】A Probabilistic Collaborative Representation Based Approach for Pattern Classification.

Sijia Cai:

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

Wangmeng Zuo:

Xiangchu Feng:

【Machine learning】Sublabel-Accurate Relaxation of Nonconvex Energies.

Thomas Möllenhoff:

Emanuel Laude:

Michael Moeller:

Jan Lellmann:

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

【Machine learning】The Multiverse Loss for Robust Transfer Learning.

Etai Littwin:

Lior Wolf:

【Machine learning】Learning From the Mistakes of Others: Matching Errors in Cross-Dataset Learning.

Viktoriia Sharmanska:

Novi Quadrianto:

【Machine learning】An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds.

Rudrasis Chakraborty:

Dohyung Seo:

Baba C. Vemuri:

【Machine learning】Online Learning With Bayesian Classification Trees.

Samuel Rota Bulò:

Peter Kontschieder:

【Machine learning】Cross-Stitch Networks for Multi-Task Learning.

Ishan Misra:

Abhinav Shrivastava:

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

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

【Machine learning】Mixture of Bilateral-Projection Two-Dimensional Probabilistic Principal Component Analysis.

Fujiao Ju:

Yanfeng Sun:

Junbin Gao:

Simeng Liu:

Yongli Hu:

Baocai Yin:

【Machine learning】Logistic Boosting Regression for Label Distribution Learning.

Chao Xing:

Xin Geng:

Hui Xue:

【Machine learning】Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold.

Xikang Zhang:

Yin Wang:

Mengran Gou:

Mario Sznaier:

Octavia Camps:

【Machine learning】Tensor Power Iteration for Multi-Graph Matching.

Xinchu Shi:

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

Weiming Hu:

Junliang Xing:

Yanning Zhang:

【Machine learning】Multivariate Regression on the Grassmannian for Predicting Novel Domains.

Yongxin Yang:

Timothy M. Hospedales:

【Machine learning】Learning Cross-Domain Landmarks for Heterogeneous Domain Adaptation.

Yao-Hung Hubert Tsai:

Yi-Ren Yeh:

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

【Machine learning】Sliced Wasserstein Kernels for Probability Distributions.

Soheil Kolouri:

Yang Zou:

Gustavo K. Rohde:

【Machine learning】Synthesized Classifiers for Zero-Shot Learning.

Soravit Changpinyo:

Wei-Lun Chao:

Boqing Gong:

Fei Sha:

【Machine learning】Min Norm Point Algorithm for Higher Order MRF-MAP Inference.

Ishant Shanu:

Chetan Arora:

Parag Singla:

【Machine learning】Linear Shape Deformation Models With Local Support Using Graph-Based Structured Matrix Factorisation.

Florian Bernard:

Peter Gemmar:

Frank Hertel:

Jorge Goncalves:

Johan Thunberg:

【Machine learning】Relaxation-Based Preprocessing Techniques for Markov Random Field Inference.

Chen Wang:

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

【Machine learning】Principled Parallel Mean-Field Inference for Discrete Random Fields.

Pierre Baqué:

Timur Bagautdinov:

François Fleuret:

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

【Machine learning】Guaranteed Outlier Removal With Mixed Integer Linear Programs.

Tat-Jun Chin:

Yang Heng Kee:

Anders Eriksson:

Frank Neumann:

【Machine learning】Proximal Riemannian Pursuit for Large-Scale Trace-Norm Minimization.

Mingkui Tan:

Shijie Xiao:

Junbin Gao:

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

Anton van den Hengel:

Qinfeng Shi:

【Machine learning】Minimizing the Maximal Rank.

Erik Bylow:

Carl Olsson:

Fredrik Kahl:

Mikael Nilsson:

【Machine learning】Solving Temporal Puzzles.

Caglayan Dicle:

Burak Yilmaz:

Octavia Camps:

Mario Sznaier:

【Machine learning】Estimating Sparse Signals With Smooth Support via Convex Programming and Block Sparsity.

Sohil Shah:

Tom Goldstein:

Christoph Studer:

【Machine learning】Ten SR: Multi-Dimensional Tensor Sparse Representation.

Na Qi:

Yunhui Shi:

Xiaoyan Sun:

Baocai Yin:

【Machine learning】Moral Lineage Tracing.

Florian Jug:

Evgeny Levinkov:

Corinna Blasse:

Eugene W. Myers:

Bjoern Andres:

【Machine learning】On Benefits of Selection Diversity via Bilevel Exclusive Sparsity.

Haichuan Yang:

Yijun Huang:

Lam Tran:

Ji Liu:

Shuai Huang:

【Machine learning】Zero-Shot Learning via Joint Latent Similarity Embedding.

Ziming Zhang:

Venkatesh Saligrama:

【Deep learning】Neural Module Networks.

Jacob Andreas:

Marcus Rohrbach:

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

Dan Klein:

【Deep learning】TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks.

Dmitry Laptev:

Nikolay Savinov:

Joachim M. Buhmann:

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

【Deep learning】Efficient Training of Very Deep Neural Networks for Supervised Hashing.

Ziming Zhang:

Yuting Chen:

Venkatesh Saligrama:

【Deep learning】Towards Open Set Deep Networks.

Abhijit Bendale:

Terrance E. Boult:

【Deep learning】When Naïve Bayes Nearest Neighbors Meet Convolutional Neural Networks.

Ilja Kuzborskij:

Fabio Maria Carlucci:

Barbara Caputo:

【Deep learning】Refining Architectures of Deep Convolutional Neural Networks.

Sukrit Shankar:

Duncan Robertson:

Yani Ioannou:

Antonio Criminisi:

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

【Deep learning】Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks.

Seong Jae Hwang:

Nagesh Adluru:

Maxwell D. Collins:

Sathya N. Ravi:

Barbara B. Bendlin:

Sterling C. Johnson:

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

【Deep learning】Fast Conv Nets Using Group-Wise Brain Damage.

Vadim Lebedev:

Victor Lempitsky:

【Deep learning】Deep Fool: A Simple and Accurate Method to Fool Deep Neural Networks.

Seyed-Mohsen Moosavi-Dezfooli:

Alhussein Fawzi:

Pascal Frossard:

【Deep learning】Blockout: Dynamic Model Selection for Hierarchical Deep Networks.

Calvin Murdock:

Zhen Li:

Howard Zhou:

Tom Duerig:

【Deep learning】Fire Caffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters.

Forrest N. Iandola:

Matthew W. Moskewicz:

Khalid Ashraf:

Kurt Keutzer:

【Deep learning】MDL-CW: A Multimodal Deep Learning Framework With Cross Weights.

Sarah Rastegar:

Mahdieh Soleymani:

Hamid R. Rabiee:

Seyed Mohsen Shojaee:

【Deep learning】Structured Receptive Fields in CNNs.

Jörn-Henrik Jacobsen:

Jan van Gemert:

Zhongyu Lou:

Arnold W. M. Smeulders:

【Deep learning】Rethinking the Inception Architecture for Computer Vision.

Christian Szegedy:

Vincent Vanhoucke:

Sergey Ioffe:

Jon Shlens:

Zbigniew Wojna:

【Deep learning】Analyzing Classifiers: Fisher Vectors and Deep Neural Networks.

Sebastian Bach:

Alexander Binder:

Grégoire Montavon:

Klaus-Robert Müller:

Wojciech Samek:

【Deep learning】Learning Structured Inference Neural Networks With Label Relations.

Hexiang Hu:

Guang-Tong Zhou:

Zhiwei Deng:

Zicheng Liao:

Greg Mori: http://www.cs.sfu.ca/~mori/

【Deep learning】Occlusion-Free Face Alignment: Deep Regression Networks Coupled With De-Corrupt Auto Encoders.

Jie Zhang:

Meina Kan:

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

Xilin Chen:

【Deep learning】Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled.

Oscar Koller:

Hermann Ney:

Richard Bowden:

【Deep learning】Deep Metric Learning via Lifted Structured Feature Embedding.

Hyun Oh Song:

Yu Xiang:

Stefanie Jegelka:

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

【Deep learning】Fast Algorithms for Convolutional Neural Networks.

Andrew Lavin:

Scott Gray:

【Deep learning】Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks.

Varun Jampani:

Martin Kiefel:

Peter V. Gehler:

【Deep learning】Improving the Robustness of Deep Neural Networks via Stability Training.

Stephan Zheng:

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

Thomas Leung:

Ian Goodfellow:

【Deep learning】WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks.

Thibaut Durand:

Nicolas Thome:

Matthieu Cord:

【Deep learning】Disturb Label: Regularizing CNN on the Loss Layer.

Lingxi Xie:

Jingdong Wang:

Zhen Wei:

Meng Wang:

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

【Deep learning】Gradual Drop In of Layers to Train Very Deep Neural Networks.

Leslie N. Smith:

Emily M. Hand:

Timothy Doster:

【Deep learning】Deep Sim Nets.

Nadav Cohen:

Or Sharir:

Amnon Shashua:

【Deep learning】Quantized Convolutional Neural Networks for Mobile Devices.

Jiaxiang Wu:

Cong Leng:

Yuhang Wang:

Qinghao Hu:

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

【Deep learning】Structural-RNN: Deep Learning on Spatio-Temporal Graphs.

Ashesh Jain:

Amir R. Zamir:

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

Ashutosh Saxena: http://www.cs.cornell.edu/~asaxena/

【Deep learning】Learning to Select Pre-Trained Deep Representations With Bayesian Evidence Framework.

Yong-Deok Kim:

Taewoong Jang:

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

Seungjin Choi:

【Deep learning】Recombinator Networks: Learning Coarse-To-Fine Feature Aggregation.

Sina Honari:

Jason Yosinski:

Pascal Vincent:

Christopher Pal:

【Deep learning】Fast Training of Triplet-Based Deep Binary Embedding Networks.

Bohan Zhuang:

Guosheng Lin:

Chunhua Shen:

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

 

 

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