2017 CVPR

下面是2017 CVPR文章的主题标签,文章列表来源于http://www.cvpapers.com/cvpr2017.html
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; Multimodel learning;
【Scene parsing】Deep Supervision With Shape Concepts for Occlusion-Aware 3D Object Parsing

Chi Li:

Zeeshan Zia:

Quoc-Huy Tran:

Xiang Yu:

Gregory D. Hager:

Manmohan Chandraker:

【Scene parsing】FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

Seungryong Kim:

Dongbo Min:

Bumsub Ham:

Sangryul Jeon:

Stephen Lin:

Kwanghoon Sohn:

【Scene parsing】Surveillance Video Parsing With Single Frame Supervision

Si Liu: http://liusi-group.com/

Changhu Wang:

Ruihe Qian:

Han Yu:

Renda Bao:

Yao Sun:

【Scene parsing】Locality-Sensitive Deconvolution Networks With Gated Fusion for RGB-D Indoor Semantic Segmentation

Yanhua Cheng:

Rui Cai:

Zhiwei Li:

Xin Zhao:

Kaiqi Huang:

【Scene parsing】Simple Does It: Weakly Supervised Instance and Semantic Segmentation

Anna Khoreva:

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

Jan Hosang:

Matthias Hein:

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

【Scene parsing】Webly Supervised Semantic Segmentation

Bin Jin:

Maria V. Ortiz Segovia:

Sabine Süsstrunk:

【Scene parsing】Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network

Chao Peng:

Xiangyu Zhang:

Gang Yu:

Guiming Luo:

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

【Scene parsing】Weakly Supervised Semantic Segmentation Using Web-Crawled Videos

Seunghoon Hong:

Donghun Yeo:

Suha Kwak:

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

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

【Scene parsing】Learning Random-Walk Label Propagation for Weakly-Supervised Semantic Segmentation

Paul Vernaza:

Manmohan Chandraker:

【Scene parsing】Semantic Amodal Segmentation

Yan Zhu:

Yuandong Tian:

Dimitris Metaxas:

Piotr Dollár:

【Scene parsing】Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

Tobias Pohlen:

Alexander Hermans:

Markus Mathias:

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

【Scene parsing】Learning Adaptive Receptive Fields for Deep Image Parsing Network

Zhen Wei:

Yao Sun:

Jinqiao Wang:

Hanjiang Lai:

Si Liu: http://liusi-group.com/

【Scene parsing】Fully Convolutional Instance-Aware Semantic Segmentation

Yi Li: http://users.cecs.anu.edu.au/~yili/

Haozhi Qi:

Jifeng Dai:

Xiangyang Ji:

Yichen Wei:

【Scene parsing】Semantic Autoencoder for Zero-Shot Learning

Elyor Kodirov:

Tao Xiang:

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

【Scene parsing】Parsing Images of Overlapping Organisms With Deep Singling-Out Networks

Victor Yurchenko:

Victor Lempitsky:

【Scene parsing】Gated Feedback Refinement Network for Dense Image Labeling

Md Amirul Islam:

Mrigank Rochan:

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

Yang Wang:

【Scene parsing】Scene Parsing Through ADE20K Dataset

Bolei Zhou:

Hang Zhao:

Xavier Puig:

Sanja Fidler:

Adela Barriuso:

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

【Scene parsing】Refine Net: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

Guosheng Lin:

Anton Milan:

Chunhua Shen:

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

【Scene parsing】Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF

Falong Shen:

Rui Gan:

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

Gang Zeng:

【Scene parsing】Learning Object Interactions and Descriptions for Semantic Image Segmentation

Guangrun Wang:

Ping Luo:

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

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

【Scene parsing】Unsupervised Semantic Scene Labeling for Streaming Data

Maggie Wigness:

John G. Rogers III:

【Scene parsing】WILDCAT: Weakly Supervised Learning of Deep Conv Nets for Image Classification, Pointwise Localization and Segmentation

Thibaut Durand:

Taylor Mordan:

Nicolas Thome:

Matthieu Cord:

【Scene parsing】MIML-FCN+: Multi-Instance Multi-Label Learning via Fully Convolutional Networks With Privileged Information

Hao Yang:

Joey Tianyi Zhou:

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

Yew Soon Ong:

【Scene parsing】Low-Rank Embedded Ensemble Semantic Dictionary for Zero-Shot Learning

Zhengming Ding:

Ming Shao:

Yun Fu:

【Scene parsing】Joint Multi-Person Pose Estimation and Semantic Part Segmentation

Fangting Xia:

Peng Wang:

Xianjie Chen:

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

【Scene parsing】Convolutional Random Walk Networks for Semantic Image Segmentation

Gedas Bertasius:

Lorenzo Torresani:

Stella X. Yu:

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

【Scene parsing】Pyramid Scene Parsing Network

Hengshuang Zhao:

Jianping Shi:

Xiaojuan Qi:

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

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

【Scene parsing】Indoor Scene Parsing With Instance Segmentation, Semantic Labeling and Support Relationship Inference

Wei Zhuo:

Mathieu Salzmann:

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

Miaomiao Liu:

【Scene parsing】Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade

Xiaoxiao Li:

Ziwei Liu:

Ping Luo:

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

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

【Scene parsing】Object Region Mining With Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach

Yunchao Wei:

Jiashi Feng:

Xiaodan Liang:

Ming-Ming Cheng:

Yao Zhao:

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

【Scene parsing】Loss Max-Pooling for Semantic Image Segmentation

Samuel Rota Bulò:

Gerhard Neuhold:

Peter Kontschieder:

【Scene parsing】STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling

Yang He:

Wei-Chen Chiu:

Margret Keuper:

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

【Scene parsing】Detect, Replace, Refine: Deep Structured Prediction for Pixel Wise Labeling

Spyros Gidaris:

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

【Object segmentation】Designing Effective Inter-Pixel Information Flow for Natural Image Matting

YaÄŸiz Aksoy:

Tunç Ozan Aydin:

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

【Object segmentation】Instance-Level Salient Object Segmentation

Guanbin Li:

Yuan Xie:

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

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

【Object segmentation】End-To-End Instance Segmentation With Recurrent Attention

Mengye Ren:

Richard S. Zemel:

【Object segmentation】Deep Image Matting

PDF: https://arxiv.org/abs/1703.03872

Ning Xu:

Brian Price:

Scott Cohen:

Thomas Huang:

【Object segmentation】Multi-Scale FCN With Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting in the Wild

Dafang He:

Xiao Yang:

Chen Liang:

Zihan Zhou:

Alexander G. Ororbi II:

Daniel Kifer:

  1. Lee Giles:

【Object segmentation】Boundary-Aware Instance Segmentation

Zeeshan Hayder:

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

Mathieu Salzmann:

【Object segmentation】Pixelwise Instance Segmentation With a Dynamically Instantiated Network

Anurag Arnab:

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

【Object segmentation】Coarse-To-Fine Segmentation With Shape-Tailored Continuum Scale Spaces

Naeemullah Khan:

Byung-Woo Hong:

Anthony Yezzi:

Ganesh Sundaramoorthi:

【Object segmentation】Fusion Seg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos

Suyog Dutt Jain:

Bo Xiong:

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

【Object segmentation】Fast Mask: Segment Multi-Scale Object Candidates in One Shot

Hexiang Hu:

Shiyi Lan:

Yuning Jiang:

Zhimin Cao:

Fei Sha:

【Object segmentation】Improving RANSAC-Based Segmentation Through CNN Encapsulation

Dustin Morley:

Hassan Foroosh:

【Object segmentation】Deep Watershed Transform for Instance Segmentation

Min Bai:

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

【Object segmentation】Learning Video Object Segmentation From Static Images

Federico Perazzi:

Anna Khoreva:

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

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

Alexander Sorkine-Hornung:

【Object segmentation】Efficient Optimization for Hierarchically-structured Interacting Segments

Hossam Isack:

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

Ipek Oguz:

Milan Sonka:

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

【Object segmentation】Exploiting Saliency for Object Segmentation From Image Level Labels

Seong Joon Oh:

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

Anna Khoreva:

Zeynep Akata:

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

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

【Object segmentation】One-Shot Video Object Segmentation

PDF: https://github.com/kmaninis/OSVOS-caffe

Sergi Caelles:

Kevis-Kokitsi Maninis:

Jordi Pont-Tuset:

Laura Leal-Taixé:

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

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

【Object segmentation】SPFTN: A Self-Paced Fine-Tuning Network for Segmenting Objects in Weakly Labelled Videos

Dingwen Zhang:

Le Yang:

Deyu Meng:

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

Junwei Han:

【Object segmentation】Video Segmentation via Multiple Granularity Analysis

Rui Yang:

Bingbing Ni:

Chao Ma:

Yi Xu:

Xiaokang Yang:

【Object segmentation】Primary Object Segmentation in Videos Based on Region Augmentation and Reduction

Yeong Jun Koh:

Chang-Su Kim:

【Object segmentation】Online Video Object Segmentation via Convolutional Trident Network

Won-Dong Jang:

Chang-Su Kim:

【Image segmentation】Superpixels and Polygons Using Simple Non-Iterative Clustering

Radhakrishna Achanta:

Sabine Süsstrunk:

【Image segmentation】Contour-Constrained Superpixels for Image and Video Processing

Se-Ho Lee:

Won-Dong Jang:

Chang-Su Kim:

【Image segmentation】4D Light Field Superpixel and Segmentation

Hao Zhu:

Qi Zhang:

Qing Wang:

【Image segmentation】Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation

Anirban Roy:

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

【Video segmentation】Budget-Aware Deep Semantic Video Segmentation

Behrooz Mahasseni:

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

Alan Fern:

【Boundary detection】Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs

Martin Simonovsky:

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

【Boundary detection】Deep Crisp Boundaries

Yupei Wang:

Xin Zhao:

Kaiqi Huang:

【Boundary detection】CASENet: Deep Category-Aware Semantic Edge Detection

Zhiding Yu:

Chen Feng:

Ming-Yu Liu:

Srikumar Ramalingam:

【Boundary detection】Building a Regular Decision Boundary With Deep Networks

Edouard Oyallon:

【Boundary detection】MCMLSD: A Dynamic Programming Approach to Line Segment Detection

Emilio J. Almazà n:

Ron Tal:

Yiming Qian:

James H. Elder:

【Boundary detection】Richer Convolutional Features for Edge Detection

Yun Liu:

Ming-Ming Cheng:

Xiaowei Hu:

Kai Wang:

Xiang Bai:

【Boundary detection】Instance Cut: From Edges to Instances With Multi Cut

Alexander Kirillov:

Evgeny Levinkov:

Bjoern Andres:

Bogdan Savchynskyy:

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

【Contour analysis】Learning Shape Abstractions by Assembling Volumetric Primitives

Shubham Tulsiani:

Hao Su:

Leonidas J. Guibas:

Alexei A. Efros:

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

【Contour analysis】Can Walking and Measuring Along Chord Bunches Better Describe Leaf Shapes?

Bin Wang:

Yongsheng Gao:

Changming Sun:

Michael Blumenstein:

John La Salle:

【Contour analysis】Shape Odds: Variational Bayesian Learning of Generative Shape Models

Shireen Elhabian:

Ross Whitaker:

【Contour analysis】Dense Reg: Fully Convolutional Dense Shape Regression In-The-Wild

Rıza Alp Güler:

George Trigeorgis:

Epameinondas Antonakos:

Patrick Snape:

Stefanos Zafeiriou:

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

【Contour analysis】Learning Category-Specific 3D Shape Models From Weakly Labeled 2D Images

Dingwen Zhang:

Junwei Han:

Yang Yang:

Dong Huang:

【Contour analysis】Dynamic Attention-Controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-Set Sample Weighting

Zhen-Hua Feng:

Josef Kittler:

William Christmas:

Patrik Huber:

Xiao-Jun Wu:

【Contour analysis】Straight to Shapes: Real-Time Detection of Encoded Shapes

Saumya Jetley:

Michael Sapienza:

Stuart Golodetz:

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

【Contour analysis】A General Framework for Curve and Surface Comparison and Registration With Oriented Varifolds

Irène Kaltenmark:

Benjamin Charlier:

Nicolas Charon:

【Contour analysis】Detailed, Accurate, Human Shape Estimation From Clothed 3D Scan Sequences

Chao Zhang:

Sergi Pujades:

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

Gerard Pons-Moll:

【Contour analysis】Human Shape From Silhouettes Using Generative HKS Descriptors and Cross-Modal Neural Networks

Endri Dibra:

Himanshu Jain:

Cengiz Öztireli:

Remo Ziegler:

Markus Gross:

【Contour analysis】Parametric T-Spline Face Morphable Model for Detailed Fitting in Shape Subspace

Weilong Peng:

Zhiyong Feng:

Chao Xu:

Yong Su:

【Contour analysis】Learning Non-Lambertian Object Intrinsics Across Shape Net Categories

Jian Shi:

Yue Dong:

Hao Su:

Stella X. Yu:

【Contour analysis】Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis

Angela Dai:

Charles Ruizhongtai Qi:

Matthias Nießner:

【Contour analysis】Sync Spec CNN: Synchronized Spectral CNN for 3D Shape Segmentation

Li Yi:

Hao Su:

Xingwen Guo:

Leonidas J. Guibas:

【Contour analysis】Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition

Yufei Wang:

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

Xiaohui Shen:

Scott Cohen:

Garrison W. Cottrell:

【Object tracking】Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core

Wadim Kehl:

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

Slobodan Ilic:

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

【Object tracking】Superpixel-Based Tracking-By-Segmentation Using Markov Chains

Donghun Yeo:

Jeany Son:

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

Joon Hee Han:

【Object tracking】Branch Out: Regularization for Online Ensemble Tracking With Convolutional Neural Networks

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

Jack Sim:

Hartwig Adam:

【Object tracking】Learning Motion Patterns in Videos

Pavel Tokmakov:

Karteek Alahari:

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

【Object tracking】Deep Level Sets for Salient Object Detection

Ping Hu:

Bing Shuai:

Jun Liu:

Gang Wang:

【Object tracking】Art Track: Articulated Multi-Person Tracking in the Wild

Eldar Insafutdinov:

Mykhaylo Andriluka:

Leonid Pishchulin:

Siyu Tang:

Evgeny Levinkov:

Bjoern Andres:

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

【Object tracking】Action-Decision Networks for Visual Tracking With Deep Reinforcement Learning

Sangdoo Yun:

Jongwon Choi:

Youngjoon Yoo:

Kimin Yun:

Jin Young Choi:

【Object tracking】Context-Aware Correlation Filter Tracking

Matthias Mueller:

Neil Smith:

Bernard Ghanem:

【Object tracking】Deep Network Flow for Multi-Object Tracking

Manmohan Chandraker:

Paul Vernaza:

Wongun Choi:

Samuel Schulter:

【Object tracking】Multi-Object Tracking With Quadruplet Convolutional Neural Networks

Mooyeol Baek:

Jeany Son:

Minsu Cho:

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

【Object tracking】Large Margin Object Tracking With Circulant Feature Maps

Mengmeng Wang:

Yong Liu:

Zeyi Huang:

【Object tracking】Multi-Task Correlation Particle Filter for Robust Object Tracking

Tianzhu Zhang:

Changsheng Xu:

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

【Object tracking】Attentional Correlation Filter Network for Adaptive Visual Tracking

Jongwon Choi:

Hyung Jin Chang:

Sangdoo Yun:

Tobias Fischer:

Yiannis Demiris:

Jin Young Choi:

【Object tracking】The World of Fast Moving Objects

Denys Rozumnyi:

Jan Kotera:

Filip Å roubek:

Lukáš Novotný:

Jiří Matas:

【Object tracking】End-To-End Representation Learning for Correlation Filter Based Tracking

Jack Valmadre:

Luca Bertinetto:

João Henriques:

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

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

【Object tracking】Robust Visual Tracking Using Oblique Random Forests

Le Zhang:

Jagannadan Varadarajan:

Ponnuthurai Nagaratnam Suganthan:

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

Pierre Moulin:

【Object tracking】ECO: Efficient Convolution Operators for Tracking

Martin Danelljan:

Goutam Bhat:

Fahad Shahbaz Khan:

Michael Felsberg:

【Action recognition】Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection

Guillermo Garcia-Hernando:

Tae-Kyun Kim:

【Action recognition】Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition With Convolutional Neural Networks

Pichao Wang:

Wanqing Li:

Zhimin Gao:

Yuyao Zhang:

Chang Tang:

Philip Ogunbona:

【Action recognition】Spatio-Temporal Naive-Bayes Nearest-Neighbor

Junwu Weng:

Chaoqun Weng:

Junsong Yuan:

【Action recognition】Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks

Ajjen Joshi:

Soumya Ghosh:

Margrit Betke:

Stan Sclaroff:

Hanspeter Pfister:

【Action recognition】Temporal Convolutional Networks for Action Segmentation and Detection

Colin Lea:

Michael D. Flynn:

René Vidal:

Austin Reiter:

Gregory D. Hager:

【Action recognition】Weakly Supervised Actor-Action Segmentation via Robust Multi-Task Ranking

Yan Yan:

Chenliang Xu:

Dawen Cai:

Jason J. Corso:

【Action recognition】Zero-Shot Action Recognition With Error-Correcting Output Codes

Jie Qin:

Li Liu:

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

Fumin Shen:

Bingbing Ni:

Jiaxin Chen:

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

【Action recognition】Enhancing Video Summarization via Vision-Language Embedding

Bryan A. Plummer:

Matthew Brown:

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

【Action recognition】Synthesizing Dynamic Patterns by Spatial-Temporal Generative Conv Net

Jianwen Xie:

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

Ying Nian Wu:

【Action recognition】Deep Learning on Lie Groups for Skeleton-Based Action Recognition

Zhiwu Huang:

Chengde Wan:

Thomas Probst:

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

【Action recognition】Weakly Supervised Action Learning With RNN Based Fine-To-Coarse Modeling

Alexander Richard:

Hilde Kuehne:

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

【Action recognition】Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields

PDF: https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation

Zhe Cao:

Tomas Simon:

Shih-En Wei:

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

【Action recognition】CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos

Zheng Shou:

Jonathan Chan:

Alireza Zareian:

Kazuyuki Miyazawa:

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

【Action recognition】Generalized Rank Pooling for Activity Recognition

Anoop Cherian:

Basura Fernando:

Mehrtash Harandi:

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

【Action recognition】Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization

Runpeng Cui:

Hu Liu:

Changshui Zhang:

【Action recognition】Modeling Sub-Event Dynamics in First-Person Action Recognition

Hasan F. M. Zaki:

Faisal Shafait:

Ajmal Mian:

【Action recognition】Predictive-Corrective Networks for Action Detection

PDF: https://arxiv.org/pdf/1704.03615.pdf

Achal Dave:

Olga Russakovsky:

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

【Action recognition】Unified Embedding and Metric Learning for Zero-Exemplar Event Detection

Noureldien Hussein:

Efstratios Gavves:

Arnold W.M. Smeulders:

【Action recognition】Spatiotemporal Pyramid Network for Video Action Recognition

Yunbo Wang:

Mingsheng Long:

Jianmin Wang:

Philip S. Yu:

【Action recognition】ER3: A Unified Framework for Event Retrieval, Recognition and Recounting

Zhanning Gao:

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

Dongqing Zhang:

Nebojsa Jojic:

Le Wang:

Jianru Xue:

Nanning Zheng:

【Action recognition】Temporal Action Co-Segmentation in 3D Motion Capture Data and Videos

Konstantinos Papoutsakis:

Costas Panagiotakis:

Antonis A. Argyros:

【Action recognition】Procedural Generation of Videos to Train Deep Action Recognition Networks

César Roberto de Souza:

Adrien Gaidon:

Yohann Cabon:

Antonio Manuel López:

【Action recognition】Action VLAD: Learning Spatio-Temporal Aggregation for Action Classification

Rohit Girdhar:

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

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

Josef Sivic:

Bryan Russell:

【Action recognition】SCC: Semantic Context Cascade for Efficient Action Detection

Fabian Caba Heilbron:

Wayner Barrios:

Victor Escorcia:

Bernard Ghanem:

【Action recognition】Spatio-Temporal Vector of Locally Max Pooled Features for Action Recognition in Videos

Ionut Cosmin Duta:

Bogdan Ionescu:

Kiyoharu Aizawa:

Nicu Sebe:

【Action recognition】Temporal Action Localization by Structured Maximal Sums

Zehuan Yuan:

Jonathan C. Stroud:

Tong Lu:

Jia Deng:

【Action recognition】Detecting Visual Relationships With Deep Relational Networks

Bo Dai:

Yuqi Zhang:

Dahua Lin: http://dahua.me/

【Action recognition】Social Scene Understanding: End-To-End Multi-Person Action Localization and Collective Activity Recognition

Timur Bagautdinov:

Alexandre Alahi:

François Fleuret:

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

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

【Action recognition】Seeing Invisible Poses: Estimating 3D Body Pose From Egocentric Video

Hao Jiang:

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

【Action recognition】Deep Future Gaze: Gaze Anticipation on Egocentric Videos Using Adversarial Networks

Mengmi Zhang:

Keng Teck Ma:

Joo Hwee Lim:

Qi Zhao:

Jiashi Feng:

【Action recognition】Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks

Hongsong Wang:

Liang Wang:

【Action recognition】Forecasting Human Dynamics From Static Images

Yu-Wei Chao:

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

Brian Price:

Scott Cohen:

Jia Deng:

【Action recognition】Deep Sequential Context Networks for Action Prediction

Yu Kong:

Zhiqiang Tao:

Yun Fu:

【Action recognition】Global Context-Aware Attention LSTM Networks for 3D Action Recognition

Jun Liu:

Gang Wang:

Ping Hu:

Ling-Yu Duan:

Alex C. Kot:

【Action recognition】Towards Accurate Multi-Person Pose Estimation in the Wild

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

Tyler Zhu:

Nori Kanazawa:

Alexander Toshev:

Jonathan Tompson:

Chris Bregler:

Kevin Murphy:

【Action recognition】LSTM Self-Supervision for Detailed Behavior Analysis

Biagio Brattoli:

Uta Büchler:

Anna-Sophia Wahl:

Martin E. Schwab:

Björn Ommer:

【Action recognition】Multi-Task Clustering of Human Actions by Sharing Information

Shizhe Hu:

Xiaoqiang Yan:

Yangdong Ye:

【Action recognition】CERN: Confidence-Energy Recurrent Network for Group Activity Recognition

Tianmin Shu:

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

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

【Action recognition】A New Representation of Skeleton Sequences for 3D Action Recognition

Qiuhong Ke:

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

Senjian An:

Ferdous Sohel:

Farid Boussaid:

【Action recognition】Pose Track: Joint Multi-Person Pose Estimation and Tracking

Umar Iqbal:

Anton Milan:

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

【Action recognition】On Human Motion Prediction Using Recurrent Neural Networks

Julieta Martinez:

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

Javier Romero:

【Action recognition】Learning and Refining of Privileged Information-Based RNNs for Action Recognition From Depth Sequences

Zhiyuan Shi:

Tae-Kyun Kim:

【Action recognition】Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

João Carreira:

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

【Action recognition】Asynchronous Temporal Fields for Action Recognition

Gunnar A. Sigurdsson:

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

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

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

【Action recognition】Ada Scan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos

Amlan Kar:

Nishant Rai:

Karan Sikka:

Gaurav Sharma:

【Action recognition】Deep Structured Learning for Facial Action Unit Intensity Estimation

Robert Walecki:

Ognjen (Oggi) Rudovic:

Vladimir Pavlovic:

Bjöern Schuller:

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

【Action recognition】SST: Single-Stream Temporal Action Proposals

Shyamal Buch:

Victor Escorcia:

Chuanqi Shen:

Bernard Ghanem:

Juan Carlos Niebles:

【Action recognition】Untrimmed Nets for Weakly Supervised Action Recognition and Detection

Limin Wang:

Yuanjun Xiong:

Dahua Lin: http://dahua.me/

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

【Action recognition】Binary Coding for Partial Action Analysis With Limited Observation Ratios

Jie Qin:

Li Liu:

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

Bingbing Ni:

Chen Chen:

Fumin Shen:

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

【Action recognition】Action Unit Detection With Region Adaptation, Multi-Labeling Learning and Optimal Temporal Fusing

Wei Li:

Farnaz Abtahi:

Zhigang Zhu:

【Action recognition】Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals

Katsuyuki Nakamura:

Serena Yeung:

Alexandre Alahi:

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

【Action recognition】Recurrent Modeling of Interaction Context for Collective Activity Recognition

Minsi Wang:

Bingbing Ni:

Xiaokang Yang:

【Action recognition】Spatiotemporal Multiplier Networks for Video Action Recognition

Christoph Feichtenhofer:

Axel Pinz:

Richard P. Wildes:

【Crowd analysis】A Domain Based Approach to Social Relation Recognition

Qianru Sun:

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

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

【Crowd analysis】Switching Convolutional Neural Network for Crowd Counting

Deepak Babu Sam:

Shiv Surya:

  1. Venkatesh Babu:

【Crowd analysis】Generating Descriptions With Grounded and Co-Referenced People

Anna Rohrbach:

Marcus Rohrbach:

Siyu Tang:

Seong Joon Oh:

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

【Crowd analysis】Understanding Traffic Density From Large-Scale Web Camera Data

Shanghang Zhang:

Guanhang Wu:

João P. Costeira:

José M. F. Moura:

【Crowd analysis】Collaborative Summarization of Topic-Related Videos

Rameswar Panda:

Amit K. Roy-Chowdhury:

【Crowd analysis】Counting Everyday Objects in Everyday Scenes

Prithvijit Chattopadhyay:

Ramakrishna Vedantam:

Ramprasaath R. Selvaraju:

Dhruv Batra:

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

【Crowd analysis】Quality Aware Network for Set to Set Recognition

Yu Liu:

Junjie Yan:

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

【Crowd analysis】Relationship Proposal Networks

Ji Zhang:

Mohamed Elhoseiny:

Scott Cohen:

Walter Chang:

Ahmed Elgammal:

【Crowd analysis】Lean Crowdsourcing: Combining Humans and Machines in an Online System

Steve Branson:

Grant Van Horn:

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

【Video Analysis】Predicting Behaviors of Basketball Players From First Person Videos

Shan Su:

Jung Pyo Hong:

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

Hyun Soo Park:

【Video Analysis】Bidirectional Multirate Reconstruction for Temporal Modeling in Videos

Linchao Zhu:

Zhongwen Xu:

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

【Video Analysis】Deep 360 Pilot: Learning a Deep Agent for Piloting Through 360° Sports Videos

Hou-Ning Hu:

Yen-Chen Lin:

Ming-Yu Liu:

Hsien-Tzu Cheng:

Yung-Ju Chang:

Min Sun:

【Video Analysis】Query-Focused Video Summarization: Dataset, Evaluation, and a Memory Network Based Approach

Aidean Sharghi:

Jacob S. Laurel:

Boqing Gong:

【Video Analysis】Flexible Spatio-Temporal Networks for Video Prediction

Chaochao Lu:

Michael Hirsch:

Bernhard Schölkopf:

【Video Analysis】DESIRE: Distant Future Prediction in Dynamic Scenes With Interacting Agents

Namhoon Lee:

Wongun Choi:

Paul Vernaza:

Christopher B. Choy:

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

Manmohan Chandraker:

【Video Analysis】Fast Video Classification via Adaptive Cascading of Deep Models

Haichen Shen:

Seungyeop Han:

Matthai Philipose:

Arvind Krishnamurthy:

【Video Analysis】Video Frame Interpolation via Adaptive Convolution

Simon Niklaus:

Long Mai:

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

【Video Analysis】Unsupervised Video Summarization With Adversarial LSTM Networks

Behrooz Mahasseni:

Michael Lam:

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

【Video Analysis】Video Propagation Networks

Varun Jampani:

Raghudeep Gadde:

Peter V. Gehler:

【Video Analysis】Hierarchical Boundary-Aware Neural Encoder for Video Captioning

Lorenzo Baraldi:

Costantino Grana:

Rita Cucchiara:

【Video Analysis】HOPE: Hierarchical Object Prototype Encoding for Efficient Object Instance Search in Videos

Tan Yu:

Yuwei Wu:

Junsong Yuan:

【Video Analysis】End-To-End Learning of Driving Models From Large-Scale Video Datasets

Huazhe Xu:

Yang Gao:

Fisher Yu:

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

【Video Analysis】Deep Feature Flow for Video Recognition

Xizhou Zhu:

Yuwen Xiong:

Jifeng Dai:

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

Yichen Wei:

【Video Analysis】Identifying First-Person Camera Wearers in Third-Person Videos

Chenyou Fan:

Jangwon Lee:

Mingze Xu:

Krishna Kumar Singh:

Yong Jae Lee:

David J. Crandall:

Michael S. Ryoo:

【Video Analysis】Weakly Supervised Dense Video Captioning

Zhiqiang Shen:

Jianguo Li:

Zhou Su:

Minjun Li:

Yurong Chen:

Yu-Gang Jiang:

Xiangyang Xue:

【Video Analysis】Self-Supervised Video Representation Learning With Odd-One-Out Networks

Basura Fernando:

Hakan Bilen:

Efstratios Gavves:

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

【Video Analysis】Factorized Variational Autoencoders for Modeling Audience Reactions to Movies

Zhiwei Deng:

Rajitha Navarathna:

Peter Carr:

Stephan Mandt:

Yisong Yue:

Iain Matthews:

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

【Video Analysis】Supervising Neural Attention Models for Video Captioning by Human Gaze Data

Youngjae Yu:

Jongwook Choi:

Yeonhwa Kim:

Kyung Yoo:

Sang-Hun Lee:

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

【Video Analysis】Task-Driven Dynamic Fusion: Reducing Ambiguity in Video Description

Xishan Zhang:

Ke Gao:

Yongdong Zhang:

Dongming Zhang:

Jintao Li:

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

【Video Analysis】Unsupervised Learning of Long-Term Motion Dynamics for Videos

Zelun Luo:

Boya Peng:

De-An Huang:

Alexandre Alahi:

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

【Video Analysis】A Dataset and Exploration of Models for Understanding Video Data Through Fill-In-The-Blank Question-Answering

Tegan Maharaj:

Nicolas Ballas:

Anna Rohrbach:

Aaron Courville:

Christopher Pal:

【Video Analysis】Learning to Learn From Noisy Web Videos

Serena Yeung:

Vignesh Ramanathan:

Olga Russakovsky:

Liyue Shen:

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

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

【Human detection】Spindle Net: Person Re-Identification With Human Body Region Guided Feature Decomposition and Fusion

Haiyu Zhao:

Maoqing Tian:

Shuyang Sun:

Jing Shao:

Junjie Yan:

Shuai Yi:

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

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

【Human detection】Fully-Adaptive Feature Sharing in Multi-Task Networks With Applications in Person Attribute Classification

Yongxi Lu:

Abhishek Kumar:

Shuangfei Zhai:

Yu Cheng:

Tara Javidi:

Rogerio Feris: http://rogerioferis.com/

【Human detection】Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-Identification

Weihua Chen:

Xiaotang Chen:

Jianguo Zhang:

Kaiqi Huang:

【Human detection】Unsupervised Adaptive Re-Identification in Open World Dynamic Camera Networks

Rameswar Panda:

Amran Bhuiyan:

Vittorio Murino:

Amit K. Roy-Chowdhury:

【Human detection】One-Shot Metric Learning for Person Re-Identification

Slawomir BÄ…k:

Peter Carr:

【Human detection】Deep Representation Learning for Human Motion Prediction and Classification

Judith Bütepage:

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

Danica Kragic:

Hedvig Kjellström:

【Human detection】Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model

Qixiang Ye:

Tianliang Zhang:

Wei Ke:

Qiang Qiu:

Jie Chen:

Guillermo Sapiro:

Baochang Zhang:

【Human detection】Person Re-Identification in the Wild

Liang Zheng:

Hengheng Zhang:

Shaoyan Sun:

Manmohan Chandraker:

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

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

【Human detection】Scalable Person Re-Identification on Supervised Smoothed Manifold

Song Bai:

Xiang Bai:

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

【Human detection】Joint Detection and Identification Feature Learning for Person Search

Tong Xiao:

Shuang Li:

Bochao Wang:

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

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

【Human detection】Consistent-Aware Deep Learning for Person Re-Identification in a Camera Network

Ji Lin:

Liangliang Ren:

Jiwen Lu:

Jianjiang Feng:

Jie Zhou:

【Human detection】Re-Ranking Person Re-Identification With k-Reciprocal Encoding

Zhun Zhong:

Liang Zheng:

Donglin Cao:

Shaozi Li:

【Human detection】Multiple People Tracking by Lifted Multicut and Person Re-Identification

Siyu Tang:

Mykhaylo Andriluka:

Bjoern Andres:

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

【Human detection】Learning Cross-Modal Deep Representations for Robust Pedestrian Detection

Dan Xu:

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

Elisa Ricci:

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

Nicu Sebe:

【Human detection】City Persons: A Diverse Dataset for Pedestrian Detection

Shanshan Zhang:

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

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

【Human detection】Forecasting Interactive Dynamics of Pedestrians With Fictitious Play

Wei-Chiu Ma:

De-An Huang:

Namhoon Lee:

Kris M. Kitani:

【Human detection】Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters

Shiyu Huang:

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

【Human detection】Unite the People: Closing the Loop Between 3D and 2D Human Representations

Christoph Lassner:

Javier Romero:

Martin Kiefel:

Federica Bogo:

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

Peter V. Gehler:

【Human detection】Deep Multitask Architecture for Integrated 2D and 3D Human Sensing

Alin-Ionut Popa:

Mihai Zanfir:

Cristian Sminchisescu:

【Human detection】Point to Set Similarity Based Deep Feature Learning for Person Re-Identification

Sanping Zhou:

Jinjun Wang:

Jiayun Wang:

Yihong Gong:

Nanning Zheng:

【Human detection】Fast Person Re-Identification via Cross-Camera Semantic Binary Transformation

Jiaxin Chen:

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

Jie Qin:

Li Liu:

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

【Human detection】Sequential Person Recognition in Photo Albums With a Recurrent Network

Yao Li:

Guosheng Lin:

Bohan Zhuang:

Lingqiao Liu:

Chunhua Shen:

Anton van den Hengel:

【Human detection】See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-Based Person Re-Identification

Zhen Zhou:

Yan Huang:

Wei Wang:

Liang Wang:

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

【Human detection】Pose-Aware Person Recognition

Vijay Kumar:

Anoop Namboodiri:

Manohar Paluri:

V. Jawahar:

【Human parsing】Crossing Nets: Combining GANs and VAEs With a Shared Latent Space for Hand Pose Estimation

Chengde Wan:

Thomas Probst:

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

Angela Yao:

【Human parsing】LCR-Net: Localization-Classification-Regression for Human Pose

Grégory Rogez:

Philippe Weinzaepfel:

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

【Human parsing】Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations

Georgios Pavlakos:

Xiaowei Zhou:

Konstantinos G. Derpanis:

Kostas Daniilidis:

【Human parsing】Coarse-To-Fine Volumetric Prediction for Single-Image 3D Human Pose

Georgios Pavlakos:

Xiaowei Zhou:

Konstantinos G. Derpanis:

Kostas Daniilidis:

【Human parsing】3D Human Pose Estimation From a Single Image via Distance Matrix Regression

Francesc Moreno-Noguer:

【Human parsing】Big Hand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis

Shanxin Yuan:

Qi Ye:

Björn Stenger:

Siddhant Jain:

Tae-Kyun Kim:

【Human parsing】Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly

Hao Jiang:

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

【Human parsing】Video2Shop: Exact Matching Clothes in Videos to Online Shopping Images

Zhi-Qi Cheng:

Xiao Wu:

Yang Liu:

Xian-Sheng Hua:

【Human parsing】Hand Keypoint Detection in Single Images Using Multiview Bootstrapping

Tomas Simon:

Hanbyul Joo:

Iain Matthews:

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

【Human parsing】Dynamic FAUST: Registering Human Bodies in Motion

Federica Bogo:

Javier Romero:

Gerard Pons-Moll:

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

【Human parsing】Multi-Context Attention for Human Pose Estimation

Xiao Chu:

Wei Yang:

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

Cheng Ma:

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

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

【Human parsing】3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation From Single Depth Images

Liuhao Ge:

Hui Liang:

Junsong Yuan:

Daniel Thalmann:

【Human parsing】Lifting From the Deep: Convolutional 3D Pose Estimation From a Single Image

Denis Tome:

Chris Russell:

Lourdes Agapito:

【Human parsing】3D Human Pose Estimation = 2D Pose Estimation + Matching

Ching-Hang Chen:

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

【Human parsing】Look Into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing

Ke Gong:

Xiaodan Liang:

Dongyu Zhang:

Xiaohui Shen:

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

【Human parsing】Deep Mixture of Linear Inverse Regressions Applied to Head-Pose Estimation

Stéphane Lathuilière:

Rémi Juge:

Pablo Mesejo:

Rafael Muñoz-Salinas:

Radu Horaud:

【Human parsing】A Low Power, Fully Event-Based Gesture Recognition System

Arnon Amir:

Brian Taba:

David Berg:

Timothy Melano:

Jeffrey Mc Kinstry:

Carmelo Di Nolfo:

Tapan Nayak:

Alexander Andreopoulos:

Guillaume Garreau:

Marcela Mendoza:

Jeff Kusnitz:

Michael Debole:

Steve Esser:

Tobi Delbruck:

Myron Flickner:

Dharmendra Modha:

【Face recognition】Using Ranking-CNN for Age Estimation

Shixing Chen:

Caojin Zhang:

Ming Dong:

Jialiang Le:

Mike Rao:

【Face recognition】Disentangled Representation Learning GAN for Pose-Invariant Face Recognition

Luan Tran:

Xi Yin:

Xiaoming Liu:

【Face recognition】Finding Tiny Faces

Peiyun Hu:

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

【Face recognition】Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network

Jinwei Gu:

Xiaodong Yang:

Shalini De Mello:

Jan Kautz:

【Face recognition】Joint Registration and Representation Learning for Unconstrained Face Identification

Munawar Hayat:

Salman H. Khan:

Naoufel Werghi:

Roland Goecke:

【Face recognition】Synthesizing Normalized Faces From Facial Identity Features

Forrester Cole:

David Belanger:

Dilip Krishnan:

Aaron Sarna:

Inbar Mosseri:

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

【Face recognition】Level Playing Field for Million Scale Face Recognition

Aaron Nech:

Ira Kemelmacher-Shlizerman:

【Face recognition】Neural Aggregation Network for Video Face Recognition

PDF: https://arxiv.org/pdf/1603.05474.pdf

Jiaolong Yang (Microsoft Research):

Peiran Ren (Microsoft Research):

Dongqing Zhang (Microsoft Research):

Dong Chen (Microsoft Research):

Fang Wen (Microsoft Research):

Hongdong Li (ANU):

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

【Face recognition】Discriminative Covariance Oriented Representation Learning for Face Recognition With Image Sets

Wen Wang:

Ruiping Wang:

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

Xilin Chen:

【Face recognition】Sphere Face: Deep Hypersphere Embedding for Face Recognition

Weiyang Liu:

Yandong Wen:

Zhiding Yu:

Ming Li:

Bhiksha Raj:

Le Song:

【Face recognition】Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-Spectral Hallucination and Low-Rank Embedding

José Lezama:

Qiang Qiu:

Guillermo Sapiro:

【Face detection】Detecting Masked Faces in the Wild With LLE-CNNs

Shiming Ge:

Jia Li:

Qiting Ye:

Zhao Luo:

【Face detection】Scale-Aware Face Detection

Zekun Hao:

Yu Liu:

Hongwei Qin:

Junjie Yan:

Xiu Li:

Xiaolin Hu:

【Face parsing】Learning Residual Images for Face Attribute Manipulation

Wei Shen:

Rujie Liu:

【Face parsing】Interspecies Knowledge Transfer for Facial Keypoint Detection

Maheen Rashid:

Xiuye Gu:

Yong Jae Lee:

【Face parsing】Emotion Recognition in Context

Ronak Kosti:

Jose M. Alvarez:

Adria Recasens:

Agata Lapedriza:

【Face parsing】Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild

Shan Li:

Weihong Deng:

Jun Ping Du:

【Face parsing】A Deep Regression Architecture With Two-Stage Re-Initialization for High Performance Facial Landmark Detection

Jiangjing Lv:

Xiaohu Shao:

Junliang Xing:

Cheng Cheng:

Xi Zhou:

【Face parsing】Improving Facial Attribute Prediction Using Semantic Segmentation

Mahdi M. Kalayeh:

Boqing Gong:

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

【Face parsing】Age Progression/Regression by Conditional Adversarial Autoencoder

Zhifei Zhang:

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

Hairong Qi:

【Face parsing】POSEidon: Face-From-Depth for Driver Pose Estimation

Guido Borghi:

Marco Venturelli:

Roberto Vezzani:

Rita Cucchiara:

【Face parsing】Simultaneous Facial Landmark Detection, Pose and Deformation Estimation Under Facial Occlusion

Yue Wu:

Chao Gou:

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

【Object recognition】Deep Affordance-Grounded Sensorimotor Object Recognition

Spyridon Thermos:

Georgios Th. Papadopoulos:

Petros Daras:

Gerasimos Potamianos:

【Object recognition】A Compact DNN: Approaching Goog Le Net-Level Accuracy of Classification and Domain Adaptation

Chunpeng Wu:

Wei Wen:

Tariq Afzal:

Yongmei Zhang:

Yiran Chen:

Hai (Helen) Li:

【Object recognition】Fine-Grained Recognition of Thousands of Object Categories With Single-Example Training

Leonid Karlinsky:

Joseph Shtok:

Yochay Tzur:

Asaf Tzadok:

【Object recognition】Semantically Consistent Regularization for Zero-Shot Recognition

Pedro Morgado:

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

【Object recognition】Unsupervised Part Learning for Visual Recognition

Ronan Sicre:

Yannis Avrithis:

Ewa Kijak:

Frédéric Jurie:

【Object recognition】Network Dissection: Quantifying Interpretability of Deep Visual Representations

David Bau:

Bolei Zhou:

Aditya Khosla:

Aude Oliva:

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

【Object recognition】Joint Discriminative Bayesian Dictionary and Classifier Learning

Naveed Akhtar:

Ajmal Mian:

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

【Object recognition】Deep Learning Human Mind for Automated Visual Classification

Concetto Spampinato:

Simone Palazzo:

Isaak Kavasidis:

Daniela Giordano:

Nasim Souly:

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

【Object recognition】Correlational Gaussian Processes for Cross-Domain Visual Recognition

Chengjiang Long:

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

【Object recognition】Zero-Shot Classification With Discriminative Semantic Representation Learning

Meng Ye:

Yuhong Guo: http://www.cis.temple.edu/~yuhong/

【Object recognition】Zero-Shot Recognition Using Dual Visual-Semantic Mapping Paths

Yanan Li:

Donghui Wang:

Huanhang Hu:

Yuetan Lin:

Yueting Zhuang:

【Object recognition】Few-Shot Object Recognition From Machine-Labeled Web Images

Zhongwen Xu:

Linchao Zhu:

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

【Object recognition】i Ca RL: Incremental Classifier and Representation Learning

Sylvestre-Alvise Rebuffi:

Alexander Kolesnikov:

Georg Sperl:

Christoph H. Lampert: http://pub.ist.ac.at/~chl/

【Object recognition】From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis

Yang Long:

Li Liu:

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

Fumin Shen:

Guiguang Ding:

Jungong Han:

【Object recognition】Learning Deep Match Kernels for Image-Set Classification

Haoliang Sun:

Xiantong Zhen:

Yuanjie Zheng:

Gongping Yang:

Yilong Yin:

Shuo Li:

【Object recognition】Fine-Grained Recognition as HSnet Search for Informative Image Parts

Michael Lam:

Behrooz Mahasseni:

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

【Object recognition】G2De Net: Global Gaussian Distribution Embedding Network and Its Application to Visual Recognition

Qilong Wang:

Peihua Li:

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

【Object recognition】IRINA: Iris Recognition

Hugo Proença:

João C. Neves:

【Object recognition】Joint Intensity and Spatial Metric Learning for Robust Gait Recognition

Yasushi Makihara:

Atsuyuki Suzuki:

Daigo Muramatsu:

Xiang Li:

Yasushi Yagi:

【Object recognition】Low-Rank Bilinear Pooling for Fine-Grained Classification

Shu Kong:

Charless Fowlkes: http://www.ics.uci.edu/~fowlkes/

【Object recognition】Seeing Into Darkness: Scotopic Visual Recognition

Bo Chen:

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

【Object recognition】Deep Co-Occurrence Feature Learning for Visual Object Recognition

Ya-Fang Shih:

Yang-Ming Yeh:

Yen-Yu Lin:

Ming-Fang Weng:

Yi-Chang Lu:

Yung-Yu Chuang:

【Object detection】Discovering Causal Signals in Images

David Lopez-Paz:

Robert Nishihara:

Soumith Chintala:

Bernhard Schölkopf:

Léon Bottou:

【Object detection】SRN: Side-output Residual Network for Object Symmetry Detection in the Wild

Wei Ke:

Jie Chen:

Jianbin Jiao:

Guoying Zhao:

Qixiang Ye:

【Object detection】Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes From 2D Ones in RGB-Depth Images

Zhuo Deng:

Longin Jan Latecki:

【Object detection】Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes

  1. Alireza Golestaneh:

Lina J. Karam:

【Object detection】Accurate Single Stage Detector Using Recurrent Rolling Convolution

Jimmy Ren:

Xiaohao Chen:

Jianbo Liu:

Wenxiu Sun:

Jiahao Pang:

Qiong Yan:

Yu-Wing Tai:

Li Xu:

【Object detection】Object Detection in Videos With Tubelet Proposal Networks

Kai Kang:

Hongsheng Li:

Tong Xiao:

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

Junjie Yan:

Xihui Liu:

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

【Object detection】Feature Pyramid Networks for Object Detection

Tsung-Yi Lin:

Piotr Dollár:

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

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

Bharath Hariharan:

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

【Object detection】Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features

Arthur Daniel Costea:

Robert Varga:

Sergiu Nedevschi:

【Object detection】Discriminative Bimodal Networks for Visual Localization and Detection With Natural Language Queries

Yuting Zhang:

Luyao Yuan:

Yijie Guo:

Zhiyuan He:

I-An Huang:

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

【Object detection】Discover and Learn New Objects From Documentaries

Kai Chen:

Hang Song:

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

Dahua Lin: http://dahua.me/

【Object detection】Training Object Class Detectors With Click Supervision

Dim P. Papadopoulos:

Jasper R. R. Uijlings:

Frank Keller:

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

【Object detection】Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing

Jin Sun:

David W. Jacobs:

【Object detection】Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization

Kuo-Hao Zeng:

Shih-Han Chou:

Fu-Hsiang Chan:

Juan Carlos Niebles:

Min Sun:

【Object detection】Deep MANTA: A Coarse-To-Fine Many-Task Network for Joint 2D and 3D Vehicle Analysis From Monocular Image

Florian Chabot:

Mohamed Chaouch:

Jaonary Rabarisoa:

Céline Teulière:

Thierry Chateau:

【Object detection】Perceptual Generative Adversarial Networks for Small Object Detection

Jianan Li:

Xiaodan Liang:

Yunchao Wei:

Tingfa Xu:

Jiashi Feng:

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

【Object detection】A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

Xiaolong Wang:

Abhinav Shrivastava:

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

【Object detection】Multiple Instance Detection Network With Online Instance Classifier Refinement

Peng Tang:

Xinggang Wang:

Xiang Bai:

Wenyu Liu:

【Object detection】Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors

Jonathan Huang:

Vivek Rathod:

Chen Sun:

Menglong Zhu:

Anoop Korattikara:

Alireza Fathi:

Ian Fischer:

Zbigniew Wojna:

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

Sergio Guadarrama:

Kevin Murphy:

【Object detection】Visual-Inertial-Semantic Scene Representation for 3D Object Detection

Jingming Dong:

Xiaohan Fei:

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

【Object detection】Polyhedral Conic Classifiers for Visual Object Detection and Classification

Hakan Cevikalp:

Bill Triggs:

【Object detection】Incremental Kernel Null Space Discriminant Analysis for Novelty Detection

Juncheng Liu:

Zhouhui Lian:

Yi Wang:

Jianguo Xiao:

【Object detection】Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection From Videos

Yang Du:

Chunfeng Yuan:

Bing Li:

Weiming Hu:

Stephen Maybank:

【Object detection】Deep Self-Taught Learning for Weakly Supervised Object Localization

Zequn Jie:

Yunchao Wei:

Xiaojie Jin:

Jiashi Feng:

Wei Liu:

【Object detection】Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute Detection

Xiaodan Liang:

Lisa Lee:

Eric P. Xing:

【Object detection】Annotating Object Instances With a Polygon-RNN

Lluís Castrejón:

Kaustav Kundu:

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

Sanja Fidler:

【Object detection】Minimum Delay Moving Object Detection

Dong Lao:

Ganesh Sundaramoorthi:

【Object detection】Weakly Supervised Affordance Detection

Johann Sawatzky:

Abhilash Srikantha:

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

【Object detection】RON: Reverse Connection With Objectness Prior Networks for Object Detection

Tao Kong:

Fuchun Sun:

Anbang Yao:

Huaping Liu:

Ming Lu:

Yurong Chen:

【Object detection】What Can Help Pedestrian Detection?

Jiayuan Mao:

Tete Xiao:

Yuning Jiang:

Zhimin Cao:

【Object detection】YOLO9000: Better, Faster, Stronger

Joseph Redmon:

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

【Object detection】Multi-View 3D Object Detection Network for Autonomous Driving

Xiaozhi Chen:

Huimin Ma:

Ji Wan:

Bo Li:

Tian Xia:

【Object detection】Mimicking Very Efficient Network for Object Detection

Quanquan Li:

Shengying Jin:

Junjie Yan:

【Object detection】Learning Detection With Diverse Proposals

Samaneh Azadi:

Jiashi Feng:

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

【Object detection】Learning Deep Context-Aware Features Over Body and Latent Parts for Person Re-Identification

Dangwei Li:

Xiaotang Chen:

Zhang Zhang:

Kaiqi Huang:

【Object detection】You Tube-Bounding Boxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video

Esteban Real:

Jonathon Shlens:

Stefano Mazzocchi:

Xin Pan:

Vincent Vanhoucke:

【Saliency detection】Top-Down Visual Saliency Guided by Captions

Vasili Ramanishka:

Abir Das:

Jianming Zhang:

Kate Saenko:

【Saliency detection】Predicting Salient Face in Multiple-Face Videos

Yufan Liu:

Songyang Zhang:

Mai Xu:

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

【Saliency detection】Attentional Push: A Deep Convolutional Network for Augmenting Image Salience With Shared Attention Modeling in Social Scenes

Siavash Gorji:

James J. Clark:

【Saliency detection】Learning to Detect Salient Objects With Image-Level Supervision

Lijun Wang:

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

Yifan Wang:

Mengyang Feng:

Dong Wang:

Baocai Yin:

Xiang Ruan:

【Saliency detection】What Is and What Is Not a Salient Object? Learning Salient Object Detector by Ensembling Linear Exemplar Regressors

Changqun Xia:

Jia Li:

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

Anlin Zheng:

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

【Saliency detection】Deeply Supervised Salient Object Detection With Short Connections

Qibin Hou:

Ming-Ming Cheng:

Xiaowei Hu:

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

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

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

【Saliency detection】Saliency Revisited: Analysis of Mouse Movements Versus Fixations

Hamed R. Tavakoli:

Fawad Ahmed:

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

Jorma Laaksonen:

【Saliency detection】Non-Local Deep Features for Salient Object Detection

Zhiming Luo:

Akshaya Mishra:

Andrew Achkar:

Justin Eichel:

Shaozi Li:

Pierre-Marc Jodoin:

【Scene recognition】The More You Know: Using Knowledge Graphs for Image Classification

Kenneth Marino:

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

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

【Scene recognition】Context-Aware Captions From Context-Agnostic Supervision

Ramakrishna Vedantam:

Samy Bengio:

Kevin Murphy:

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

Gal Chechik:

【Scene recognition】Temporal Attention-Gated Model for Robust Sequence Classification

Wenjie Pei:

Tadas Baltrušaitis:

David M.J. Tax:

Louis-Philippe Morency:

【Scene recognition】Video Captioning With Transferred Semantic Attributes

Yingwei Pan:

Ting Yao:

Houqiang Li:

Tao Mei:

【Scene recognition】Automatic Understanding of Image and Video Advertisements

Zaeem Hussain:

Mingda Zhang:

Xiaozhong Zhang:

Keren Ye:

Christopher Thomas:

Zuha Agha:

Nathan Ong:

Adriana Kovashka:

【Scene recognition】Semantic Compositional Networks for Visual Captioning

Zhe Gan:

Chuang Gan:

Xiaodong He:

Yunchen Pu:

Kenneth Tran:

Jianfeng Gao:

Lawrence Carin:

Li Deng:

【Scene recognition】Captioning Images With Diverse Objects

Subhashini Venugopalan:

Lisa Anne Hendricks:

Marcus Rohrbach:

Raymond Mooney:

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

Kate Saenko:

【Scene recognition】Self-Critical Sequence Training for Image Captioning

Steven J. Rennie:

Etienne Marcheret:

Youssef Mroueh:

Jerret Ross:

Vaibhava Goel:

【Scene recognition】TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering

Yunseok Jang:

Yale Song:

Youngjae Yu:

Youngjin Kim:

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

【Scene recognition】Improving Pairwise Ranking for Multi-Label Image Classification

Yuncheng Li:

Yale Song:

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

【Scene recognition】Active Convolution: Learning the Shape of Convolution for Image Classification

Yunho Jeon:

Junmo Kim:

【Scene recognition】Linking Image and Text With 2-Way Nets

Aviv Eisenschtat:

Lior Wolf:

【Scene recognition】Image Splicing Detection via Camera Response Function Analysis

Can Chen:

Scott Mc Closkey:

Jingyi Yu:

【Scene recognition】CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

Justin Johnson:

Bharath Hariharan:

Laurens van der Maaten:

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

  1. Lawrence Zitnick:

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

【Scene recognition】Self-Supervised Learning of Visual Features Through Embedding Images Into Text Topic Spaces

Lluis Gomez:

Yash Patel:

Marçal Rusiñol:

Dimosthenis Karatzas:

  1. V. Jawahar:

【Scene recognition】Learning Spatial Regularization With Image-Level Supervisions for Multi-Label Image Classification

Feng Zhu:

Hongsheng Li:

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

Nenghai Yu:

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

【Scene recognition】Adaptive Class Preserving Representation for Image Classification

Jian-Xun Mi:

Qiankun Fu:

Weisheng Li:

【Scene recognition】Semantic Regularisation for Recurrent Image Annotation

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

Tao Xiang:

Timothy M. Hospedales:

Wankou Yang:

Changyin Sun:

【Scene recognition】Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition

Jianlong Fu:

Heliang Zheng:

Tao Mei:

【Scene recognition】Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects

Ting Yao:

Yingwei Pan:

Yehao Li:

Tao Mei:

【Scene recognition】Generalized Deep Image to Image Regression

Venkataraman Santhanam:

Vlad I. Morariu:

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

【Scene recognition】Diverse Image Annotation

Baoyuan Wu:

Fan Jia:

Wei Liu:

Bernard Ghanem:

【Scene recognition】Episodic CAMN: Contextual Attention-Based Memory Networks With Iterative Feedback for Scene Labeling

Abrar H. Abdulnabi:

Bing Shuai:

Stefan Winkler:

Gang Wang:

【Scene recognition】SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning

Long Chen:

Hanwang Zhang:

Jun Xiao:

Liqiang Nie:

Jian Shao:

Wei Liu:

Tat-Seng Chua:

【Scene recognition】Commonly Uncommon: Semantic Sparsity in Situation Recognition

Mark Yatskar:

Vicente Ordonez:

Luke Zettlemoyer:

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

【Scene recognition】Gaze Embeddings for Zero-Shot Image Classification

Nour Karessli:

Zeynep Akata:

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

Andreas Bulling:

【Scene recognition】Adversarially Tuned Scene Generation

VSR Veeravasarapu:

Constantin Rothkopf:

Ramesh Visvanathan:

【Scene recognition】Residual Attention Network for Image Classification

Fei Wang:

Mengqing Jiang:

Chen Qian:

Shuo Yang:

Cheng Li:

Honggang Zhang:

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

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

【Scene recognition】Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification

Lifang He:

Chun-Ta Lu:

Hao Ding:

Shen Wang:

Linlin Shen:

Philip S. Yu:

Ann B. Ragin:

【Scene recognition】A Graph Regularized Deep Neural Network for Unsupervised Image Representation Learning

Shijie Yang:

Liang Li:

Shuhui Wang:

Weigang Zhang:

Qingming Huang:

【Scene recognition】Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence Models for Fill-In-The-Blank Image Captioning

Qing Sun:

Stefan Lee:

Dhruv Batra:

【Scene recognition】Fine-Grained Image Classification via Combining Vision and Language

Xiangteng He:

Yuxin Peng:

【Scene recognition】Temporal Residual Networks for Dynamic Scene Recognition

Christoph Feichtenhofer:

Axel Pinz:

Richard P. Wildes:

【Text recognition】EAST: An Efficient and Accurate Scene Text Detector

Xinyu Zhou:

Cong Yao:

He Wen:

Yuzhi Wang:

Shuchang Zhou:

Weiran He:

Jiajun Liang:

【Text recognition】Unambiguous Text Localization and Retrieval for Cluttered Scenes

Xuejian Rong:

Chucai Yi:

Yingli Tian:

【Text recognition】Deep Matching Prior Network: Toward Tighter Multi-Oriented Text Detection

Yuliang Liu:

Lianwen Jin:

【Text recognition】Detecting Oriented Text in Natural Images by Linking Segments

Baoguang Shi:

Xiang Bai:

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

【Text recognition】Learning to Extract Semantic Structure From Documents Using Multimodal Fully Convolutional Neural Networks

Xiao Yang:

Ersin Yumer:

Paul Asente:

Mike Kraley:

Daniel Kifer:

Lee Giles:

【Image retrieval】Deep Visual-Semantic Quantization for Efficient Image Retrieval

Yue Cao:

Mingsheng Long:

Jianmin Wang:

Shichen Liu:

【Image retrieval】Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval

Li Liu:

Fumin Shen:

Yuming Shen:

Xianglong Liu:

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

【Image retrieval】Spatial-Semantic Image Search by Visual Feature Synthesis

Long Mai:

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

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

Chen Fang:

Jonathan Brandt:

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

【Image retrieval】Generating Holistic 3D Scene Abstractions for Text-Based Image Retrieval

Ang Li:

Jin Sun:

Joe Yue-Hei Ng:

Ruichi Yu:

Vlad I. Morariu:

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

【Image retrieval】Generalized Semantic Preserving Hashing for N-Label Cross-Modal Retrieval

Devraj Mandal:

Kunal N. Chaudhury:

Soma Biswas:

【Image retrieval】Deep Cross-Modal Hashing

Qing-Yuan Jiang:

Wu-Jun Li:

【Image retrieval】Bayesian Supervised Hashing

Zihao Hu:

Junxuan Chen:

Hongtao Lu:

Tongzhen Zhang:

【Image retrieval】Learning Barycentric Representations of 3D Shapes for Sketch-Based 3D Shape Retrieval

Jin Xie:

Guoxian Dai:

Fan Zhu:

Yi Fang:

【Image retrieval】Simultaneous Feature Aggregating and Hashing for Large-Scale Image Search

Thanh-Toan Do:

Dang-Khoa Le Tan:

Trung T. Pham:

Ngai-Man Cheung:

【Image retrieval】Learning to Rank Retargeted Images

Yang Chen:

Yong-Jin Liu:

Yu-Kun Lai:

【Image retrieval】Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories

Ziad Al-Halah:

Rainer Stiefelhagen:

【Image retrieval】Discretely Coding Semantic Rank Orders for Supervised Image Hashing

Li Liu:

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

Fumin Shen:

Mengyang Yu:

【Image retrieval】Beyond Instance-Level Image Retrieval: Leveraging Captions to Learn a Global Visual Representation for Semantic Retrieval

Diane Larlus:

Albert Gordo:

【Image retrieval】Deep Hashing Network for Unsupervised Domain Adaptation

Hemanth Venkateswara:

Jose Eusebio:

Shayok Chakraborty:

Sethuraman Panchanathan:

【Image retrieval】Memory-Augmented Attribute Manipulation Networks for Interactive Fashion Search

Bo Zhao:

Jiashi Feng:

Xiao Wu:

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

【Image retrieval】Asymmetric Feature Maps With Application to Sketch Based Retrieval

Giorgos Tolias:

Ondřej Chum:

【Image retrieval】AMC: Attention guided Multi-modal Correlation Learning for Image Search

Kan Chen:

Trung Bui:

Chen Fang:

Zhaowen Wang:

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

【Image retrieval】Learning Multifunctional Binary Codes for Both Category and Attribute Oriented Retrieval Tasks

Haomiao Liu:

Ruiping Wang:

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

Xilin Chen:

【Image retrieval】Cross-Modality Binary Code Learning via Fusion Similarity Hashing

Hong Liu:

Rongrong Ji:

Yongjian Wu:

Feiyue Huang:

Baochang Zhang:

【Image retrieval】Collaborative Deep Reinforcement Learning for Joint Object Search

Xiangyu Kong:

Bo Xin:

Yizhou Wang:

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

【Image retrieval】Kernel Square-Loss Exemplar Machines for Image Retrieval

Rafael S. Rezende:

Joaquin Zepeda:

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

Francis Bach:

Patrick Pérez:

【3D modeling】Point Net: Deep Learning on Point Sets for 3D Classification and Segmentation

Charles R. Qi:

Hao Su:

Kaichun Mo:

Leonidas J. Guibas:

【3D modeling】Elastic Shape-From-Template With Spatially Sparse Deforming Forces

Abed Malti:

Cédric Herzet:

【3D modeling】Distinguishing the Indistinguishable: Exploring Structural Ambiguities via Geodesic Context

Qingan Yan:

Long Yang:

Ling Zhang:

Chunxia Xiao:

【3D modeling】Multi-View Supervision for Single-View Reconstruction via Differentiable Ray Consistency

PDF: https://shubhtuls.github.io/drc/

Shubham Tulsiani:

Tinghui Zhou:

Alexei A. Efros:

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

【3D modeling】Transformation-Grounded Image Generation Network for Novel 3D View Synthesis

Eunbyung Park:

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

Ersin Yumer:

Duygu Ceylan:

Alexander C. Berg:

【3D modeling】Surf Net: Generating 3D Shape Surfaces Using Deep Residual Networks

Ayan Sinha:

Asim Unmesh:

Qixing Huang:

Karthik Ramani:

【3D modeling】Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation

Hao Zhao:

Ming Lu:

Anbang Yao:

Yiwen Guo:

Yurong Chen:

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

【3D modeling】Making 360° Video Watchable in 2D: Learning Videography for Click Free Viewing

Yu-Chuan Su:

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

【3D modeling】Exploiting 2D Floorplan for Building-Scale Panorama RGBD Alignment

Erik Wijmans:

Yasutaka Furukawa:

【3D modeling】Regressing Robust and Discriminative 3D Morphable Models With a Very Deep Neural Network

Anh Tuấn Trần:

Tal Hassner:

Iacopo Masi:

Gérard Medioni:

【3D modeling】End-To-End 3D Face Reconstruction With Deep Neural Networks

Pengfei Dou:

Shishir K. Shah:

Ioannis A. Kakadiaris:

【3D modeling】DUST: Dual Union of Spatio-Temporal Subspaces for Monocular Multiple Object 3D Reconstruction

Antonio Agudo:

Francesc Moreno-Noguer:

【3D modeling】Light Field Reconstruction Using Deep Convolutional Network on EPI

Gaochang Wu:

Mandan Zhao:

Liangyong Wang:

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

Tianyou Chai:

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

【3D modeling】Reconstructing Transient Images From Single-Photon Sensors

Matthew O’Toole:

Felix Heide:

David B. Lindell:

Kai Zang:

Steven Diamond:

Gordon Wetzstein:

【3D modeling】Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures

Julian Straub:

Trevor Campbell:

Jonathan P. How:

John W. Fisher III:

【3D modeling】IM2CAD

Hamid Izadinia:

Qi Shan:

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

【3D modeling】Scan Net: Richly-Annotated 3D Reconstructions of Indoor Scenes

Angela Dai:

Angel X. Chang:

Manolis Savva:

Maciej Halber:

Thomas Funkhouser:

Matthias Nießner:

【3D modeling】Group-Wise Point-Set Registration Based on Rényi’s Second Order Entropy

Luis G. Sanchez Giraldo:

Erion Hasanbelliu:

Murali Rao:

Jose C. Principe:

【3D modeling】A Point Set Generation Network for 3D Object Reconstruction From a Single Image

Haoqiang Fan:

Hao Su:

Leonidas J. Guibas:

【3D modeling】3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder

Gil Elbaz:

Tamar Avraham:

Anath Fischer:

【3D modeling】Scalable Surface Reconstruction From Point Clouds With Extreme Scale and Density Diversity

Christian Mostegel:

Rudolf Prettenthaler:

Friedrich Fraundorfer:

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

【3D modeling】Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks

Amir Arsalan Soltani:

Haibin Huang:

Jiajun Wu:

Tejas D. Kulkarni:

Joshua B. Tenenbaum:

【3D modeling】Pose Agent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning

Alexander Krull:

Eric Brachmann:

Sebastian Nowozin:

Frank Michel:

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

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

【3D modeling】An Efficient Background Term for 3D Reconstruction and Tracking With Smooth Surface Models

Mariano Jaimez:

Thomas J. Cashman:

Andrew Fitzgibbon:

Javier Gonzalez-Jimenez:

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

【3D modeling】Vid Loc: A Deep Spatio-Temporal Model for 6-Do F Video-Clip Relocalization

Ronald Clark:

Sen Wang:

Andrew Markham:

Niki Trigoni:

Hongkai Wen:

【3D modeling】Template-Based Monocular 3D Recovery of Elastic Shapes Using Lagrangian Multipliers

Nazim Haouchine:

Stephane Cotin:

【3D modeling】Discriminative Optimization: Theory and Applications to Point Cloud Registration

Jayakorn Vongkulbhisal:

Fernando De la Torre:

João P. Costeira:

【3D modeling】Predicting Ground-Level Scene Layout From Aerial Imagery

Menghua Zhai:

Zachary Bessinger:

Scott Workman:

Nathan Jacobs:

【3D modeling】The Surfacing of Multiview 3D Drawings via Lofting and Occlusion Reasoning

Anil Usumezbas:

Ricardo Fabbri:

Benjamin B. Kimia:

【3D modeling】Learning to Align Semantic Segmentation and 2.5D Maps for Geolocalization

Anil Armagan:

Martin Hirzer:

Peter M. Roth:

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

【3D modeling】A Generative Model for Depth-Based Robust 3D Facial Pose Tracking

Lu Sheng:

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

Tat-Jen Cham:

Vladimir Pavlovic:

King Ngi Ngan:

【3D modeling】Fast 3D Reconstruction of Faces With Glasses

Fabio Maninchedda:

Martin R. Oswald:

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

【3D modeling】Learning From Synthetic Humans

Gül Varol:

Javier Romero:

Xavier Martin:

Naureen Mahmood:

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

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

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

【3D modeling】Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks

Yinda Zhang:

Shuran Song:

Ersin Yumer:

Manolis Savva:

Joon-Young Lee:

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

Thomas Funkhouser:

【3D modeling】A Reinforcement Learning Approach to the View Planning Problem

Mustafa Devrim Kaba:

Mustafa Gokhan Uzunbas:

Ser Nam Lim:

【3D modeling】3D Face Morphable Models “In-The-Wild”

James Booth:

Epameinondas Antonakos:

Stylianos Ploumpis:

George Trigeorgis:

Yannis Panagakis:

Stefanos Zafeiriou:

【3D modeling】Killing Fusion: Non-Rigid 3D Reconstruction Without Correspondences

Miroslava Slavcheva:

Maximilian Baust:

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

Slobodan Ilic:

【3D modeling】3D Menagerie: Modeling the 3D Shape and Pose of Animals

Silvia Zuffi:

Angjoo Kanazawa:

David W. Jacobs:

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

【3D modeling】Recurrent 3D Pose Sequence Machines

Mude Lin:

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

Xiaodan Liang:

Keze Wang:

Hui Cheng:

【3D modeling】Learning Detailed Face Reconstruction From a Single Image

Elad Richardson:

Matan Sela:

Roy Or-El:

Ron Kimmel:

【3D modeling】Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes

Armin Mustafa:

Adrian Hilton:

【3D modeling】On the Two-View Geometry of Unsynchronized Cameras

Cenek Albl:

Zuzana Kukelova:

Andrew Fitzgibbon:

Jan Heller:

Matej Smid:

Tomas Pajdla:

【3D modeling】Using Locally Corresponding CAD Models for Dense 3D Reconstructions From a Single Image

Chen Kong:

Chen-Hsuan Lin:

Simon Lucey:

【3D modeling】De Mo N: Depth and Motion Network for Learning Monocular Stereo

PDF: https://arxiv.org/pdf/1612.02401.pdf

Benjamin Ummenhofer:

Huizhong Zhou:

Jonas Uhrig:

Nikolaus Mayer:

Eddy Ilg:

Alexey Dosovitskiy:

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

【3D modeling】3D Bounding Box Estimation Using Deep Learning and Geometry

Arsalan Mousavian:

Dragomir Anguelov:

John Flynn:

Jana Košecká:

【3D modeling】Oct Net: Learning Deep 3D Representations at High Resolutions

Gernot Riegler:

Ali Osman Ulusoy:

Andreas Geiger:

【3D modeling】3D Shape Segmentation With Projective Convolutional Networks

Evangelos Kalogerakis:

Melinos Averkiou:

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

Siddhartha Chaudhuri:

【3D modeling】Stereo-Based 3D Reconstruction of Dynamic Fluid Surfaces by Global Optimization

Yiming Qian:

Minglun Gong:

Yee-Hong Yang:

【3D modeling】Fine-To-Coarse Global Registration of RGB-D Scans

Maciej Halber:

Thomas Funkhouser:

【3D modeling】Exploiting Symmetry and/or Manhattan Properties for 3D Object Structure Estimation From Single and Multiple Images

Yuan Gao:

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

【3D modeling】Deep View Morphing

Dinghuang Ji:

Junghyun Kwon:

Max Mc Farland:

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

【Feature matching】3DMatch: Learning Local Geometric Descriptors From RGB-D Reconstructions

Andy Zeng:

Shuran Song:

Matthias Nießner:

Matthew Fisher:

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

Thomas Funkhouser:

【Feature matching】BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition

Jacob Chan:

Jimmy Addison Lee:

Qian Kemao:

【Feature matching】Geodesic Distance Descriptors

Gil Shamai:

Ron Kimmel:

【Feature matching】HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors

Vassileios Balntas:

Karel Lenc:

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

Krystian Mikolajczyk:

【Feature matching】Quad-Networks: Unsupervised Learning to Rank for Interest Point Detection

Nikolay Savinov:

Akihito Seki:

Ľubor Ladický:

Torsten Sattler:

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

【Feature matching】Learning Deep Binary Descriptor With Multi-Quantization

Yueqi Duan:

Jiwen Lu:

Ziwei Wang:

Jianjiang Feng:

Jie Zhou:

【Feature matching】BRISKS: Binary Features for Spherical Images on a Geodesic Grid

Hao Guan:

William A. P. Smith:

【Feature matching】Learning Discriminative and Transformation Covariant Local Feature Detectors

Xu Zhang:

Felix X. Yu:

Svebor Karaman:

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

【Feature matching】Mu Ca Le-Net: Multi Categorical-Level Networks to Generate More Discriminating Features

Youssef Tamaazousti:

Hervé Le Borgne:

Céline Hudelot:

【Feature matching】Learning Features by Watching Objects Move

Deepak Pathak:

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

Piotr Dollár:

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

Bharath Hariharan:

【Feature matching】L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space

Yurun Tian:

Bin Fan:

Fuchao Wu:

【Feature matching】Fried Binary Embedding for High-Dimensional Visual Features

Weixiang Hong:

Junsong Yuan:

Sreyasee Das Bhattacharjee:

【Feature matching】Comparative Evaluation of Hand-Crafted and Learned Local Features

Johannes L. Schönberger:

Hans Hardmeier:

Torsten Sattler:

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

【Pose estimation】Global Hypothesis Generation for 6D Object Pose Estimation

PDF: http://wwwpub.zih.tu-dresden.de/~cvweb/publications/papers/2017/globalhyp.pdf

Frank Michel:

Alexander Kirillov:

Eric Brachmann:

Alexander Krull:

Stefan Gumhold:

Bogdan Savchynskyy:

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

【Pose estimation】A Practical Method for Fully Automatic Intrinsic Camera Calibration Using Directionally Encoded Light

Mahdi Abbaspour Tehrani:

Thabo Beeler:

Anselm Grundhöfer:

【Pose estimation】On-The-Fly Adaptation of Regression Forests for Online Camera Relocalisation

Tommaso Cavallari:

Stuart Golodetz:

Nicholas A. Lord:

Julien Valentin:

Luigi Di Stefano:

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

【Pose estimation】Radiometric Calibration for Internet Photo Collections

PDF: http://alumni.media.mit.edu/~shiboxin/files/Mo_CVPR17.pdf

Zhipeng Mo:

Boxin Shi:

Sai-Kit Yeung:

Yasuyuki Matsushita:

【Pose estimation】A Linear Extrinsic Calibration of Kaleidoscopic Imaging System From Single 3D Point

Kosuke Takahashi:

Akihiro Miyata:

Shohei Nobuhara:

Takashi Matsuyama:

【Pose estimation】From Local to Global: Edge Profiles to Camera Motion in Blurred Images

Subeesh Vasu:

  1. N. Rajagopalan:

【Pose estimation】NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance

Geoffrey Pascoe:

Will Maddern:

Michael Tanner:

Pedro Piniés:

Paul Newman:

【Pose estimation】Cross-View Image Matching for Geo-Localization in Urban Environments

Yicong Tian:

Chen Chen:

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

【Pose estimation】HSf M: Hybrid Structure-from-Motion

Hainan Cui:

Xiang Gao:

Shuhan Shen:

Zhanyi Hu: http://vision.ia.ac.cn/zh/index_cn.html

【Pose estimation】A New Rank Constraint on Multi-View Fundamental Matrices, and Its Application to Camera Location Recovery

Soumyadip Sengupta:

Tal Amir:

Meirav Galun:

Tom Goldstein:

David W. Jacobs:

Amit Singer:

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

【Pose estimation】Flight Dynamics-Based Recovery of a UAV Trajectory Using Ground Cameras

Artem Rozantsev:

Sudipta N. Sinha:

Debadeepta Dey:

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

【Pose estimation】DSAC – Differentiable RANSAC for Camera Localization

Eric Brachmann:

Alexander Krull:

Sebastian Nowozin:

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

Frank Michel:

Stefan Gumhold:

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

【Pose estimation】Position Tracking for Virtual Reality Using Commodity Wi Fi

Manikanta Kotaru:

Sachin Katti:

【Pose estimation】Deep Nav: Learning to Navigate Large Cities

Samarth Brahmbhatt:

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

【Pose estimation】Learned Contextual Feature Reweighting for Image Geo-Localization

Hyo Jin Kim:

Enrique Dunn:

Jan-Michael Frahm:

【Pose estimation】Simultaneous Geometric and Radiometric Calibration of a Projector-Camera Pair

Marjan Shahpaski:

Luis Ricardo Sapaico:

Gaspard Chevassus:

Sabine Süsstrunk:

【Pose estimation】Sports Field Localization via Deep Structured Models

Namdar Homayounfar:

Sanja Fidler:

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

【Pose estimation】Self-Calibration-Based Approach to Critical Motion Sequences of Rolling-Shutter Structure From Motion

Eisuke Ito:

Takayuki Okatani:

【Pose estimation】The Misty Three Point Algorithm for Relative Pose

Tobias Palmér:

Kalle Åström:

Jan-Michael Frahm:

【Pose estimation】An Efficient Algebraic Solution to the Perspective-Three-Point Problem

Tong Ke:

Stergios I. Roumeliotis:

【Pose estimation】Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos

Jie Song:

Limin Wang:

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

Otmar Hilliges:

【Pose estimation】A Dataset for Benchmarking Image-Based Localization

Xun Sun:

Yuanfan Xie:

Pei Luo:

Liang Wang:

【Pose estimation】Event-Based Visual Inertial Odometry

Alex Zihao Zhu:

Nikolay Atanasov:

Kostas Daniilidis:

【Pose estimation】Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?

Torsten Sattler:

Akihiko Torii:

Josef Sivic:

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

Hajime Taira:

Masatoshi Okutomi:

Tomas Pajdla:

【Pose estimation】Spatio-Temporal Alignment of Non-Overlapping Sequences From Independently Panning Cameras

Seyed Morteza Safdarnejad:

Xiaoming Liu:

【Pose estimation】Geometric Loss Functions for Camera Pose Regression With Deep Learning

Alex Kendall:

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

【Pose estimation】CNN-SLAM: Real-Time Dense Monocular SLAM With Learned Depth Prediction

Keisuke Tateno:

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

Iro Laina:

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

【Pose estimation】Unsupervised Vanishing Point Detection and Camera Calibration From a Single Manhattan Image With Radial Distortion

Michel Antunes:

João P. Barreto:

Djamila Aouada:

Björn Ottersten:

【Pose estimation】Toroidal Constraints for Two-Point Localization Under High Outlier Ratios

Federico Camposeco:

Torsten Sattler:

Andrea Cohen:

Andreas Geiger:

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

【Pose estimation】Cognitive Mapping and Planning for Visual Navigation

Saurabh Gupta:

James Davidson:

Sergey Levine:

Rahul Sukthankar:

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

【Stereo matching】CATS: A Color and Thermal Stereo Benchmark

Wayne Treible:

Philip Saponaro:

Scott Sorensen:

Abhishek Kolagunda:

Michael O’Neal:

Brian Phelan:

Kelly Sherbondy:

Chandra Kambhamettu:

【Stereo matching】Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation

Dan Xu:

Elisa Ricci:

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

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

Nicu Sebe:

【Stereo matching】A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting

Yvain Quéau:

Tao Wu:

François Lauze:

Jean-Denis Durou:

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

【Stereo matching】Polarimetric Multi-View Stereo

Zhaopeng Cui:

Jinwei Gu:

Boxin Shi:

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

Jan Kautz:

【Stereo matching】Learning Dynamic Guidance for Depth Image Enhancement

PDF: http://www4.comp.polyu.edu.hk/~cslzhang/paper/DGDE_CVPR17.pdf

Shuhang Gu:

Wangmeng Zuo:

Shi Guo:

Yunjin Chen:

Chongyu Chen:

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

【Stereo matching】End-To-End Training of Hybrid CNN-CRF Models for Stereo

Patrick Knöbelreiter:

Christian Reinbacher:

Alexander Shekhovtsov: http://cmp.felk.cvut.cz/~shekhovt/

Thomas Pock:

【Stereo matching】Dense Captioning With Joint Inference and Visual Context

Linjie Yang:

Kevin Tang:

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

Li-Jia Li:

【Stereo matching】Semi-Supervised Deep Learning for Monocular Depth Map Prediction

Yevhen Kuznietsov:

Jörg Stückler:

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

【Stereo matching】Noise Robust Depth From Focus Using a Ring Difference Filter

Jaeheung Surh:

Hae-Gon Jeon:

Yunwon Park:

Sunghoon Im:

Hyowon Ha:

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

【Stereo matching】Accurate Depth and Normal Maps From Occlusion-Aware Focal Stack Symmetry

Michael Strecke:

Anna Alperovich:

Bastian Goldluecke:

【Stereo matching】A Multi-View Stereo Benchmark With High-Resolution Images and Multi-Camera Videos

Thomas Schöps:

Johannes L. Schönberger:

Silvano Galliani:

Torsten Sattler:

Konrad Schindler:

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

Andreas Geiger:

【Stereo matching】Poly Net: A Pursuit of Structural Diversity in Very Deep Networks

Xingcheng Zhang:

Zhizhong Li:

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

Dahua Lin: http://dahua.me/

【Stereo matching】Improving Training of Deep Neural Networks via Singular Value Bounding

Kui Jia:

Dacheng Tao:

Shenghua Gao:

Xiangmin Xu:

【Stereo matching】Semi-Calibrated Near Field Photometric Stereo

Fotios Logothetis:

Roberto Mecca:

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

【Stereo matching】Semantic Multi-View Stereo: Jointly Estimating Objects and Voxels

Ali Osman Ulusoy:

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

Andreas Geiger:

【Stereo matching】Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps

Matteo Poggi:

Stefano Mattoccia:

【Stereo matching】Depth From Defocus in the Wild

Huixuan Tang:

Scott Cohen:

Brian Price:

Stephen Schiller:

Kiriakos N. Kutulakos:

【Stereo matching】Matting and Depth Recovery of Thin Structures Using a Focal Stack

Chao Liu:

Srinivasa G. Narasimhan:

Artur W. Dubrawski:

【Stereo matching】Ultra Stereo: Efficient Learning-Based Matching for Active Stereo Systems

Sean Ryan Fanello:

Julien Valentin:

Christoph Rhemann:

Adarsh Kowdle:

Vladimir Tankovich:

Philip Davidson:

Shahram Izadi:

【Stereo matching】Unsupervised Monocular Depth Estimation With Left-Right Consistency

Clément Godard:

Oisin Mac Aodha:

Gabriel J. Brostow:

【Stereo matching】Unsupervised Learning of Depth and Ego-Motion From Video

Tinghui Zhou:

Matthew Brown:

Noah Snavely:

David G. Lowe:

【Stereo matching】Improved Stereo Matching With Constant Highway Networks and Reflective Confidence Learning

Amit Shaked:

Lior Wolf:

【Optical flow】Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data

Joel Janai:

Fatma Güney:

Jonas Wulff:

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

Andreas Geiger:

【Optical flow】Flow Net 2.0: Evolution of Optical Flow Estimation With Deep Networks

Eddy Ilg:

Nikolaus Mayer:

Tonmoy Saikia:

Margret Keuper:

Alexey Dosovitskiy:

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

【Optical flow】CNN-Based Patch Matching for Optical Flow With Thresholded Hinge Embedding Loss

Christian Bailer:

Kiran Varanasi:

Didier Stricker:

【Optical flow】Optical Flow Estimation Using a Spatial Pyramid Network

Anurag Ranjan:

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

【Optical flow】S2F: Slow-To-Fast Interpolator Flow

Yanchao Yang:

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

【Optical flow】Robust Interpolation of Correspondences for Large Displacement Optical Flow

Yinlin Hu:

Yunsong Li:

Rui Song:

【Optical flow】Accurate Optical Flow via Direct Cost Volume Processing

Jia Xu:

René Ranftl:

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

【Optical flow】Interpo Net, a Brain Inspired Neural Network for Optical Flow Dense Interpolation

Shay Zweig:

Lior Wolf:

【Optical flow】Fast Multi-Frame Stereo Scene Flow With Motion Segmentation

Tatsunori Taniai:

Sudipta N. Sinha:

Yoichi Sato:

【Optical flow】Optical Flow in Mostly Rigid Scenes

Jonas Wulff:

Laura Sevilla-Lara:

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

【Optical flow】Optical Flow Requires Multiple Strategies

Tal Schuster:

Lior Wolf:

David Gadot:

【Region matching】Convolutional Neural Network Architecture for Geometric Matching

Ignacio Rocco:

Relja Arandjelović:

Josef Sivic:

【Region matching】Binary Constraint Preserving Graph Matching

Bo Jiang:

Jin Tang:

Chris Ding: http://ranger.uta.edu/~chqding/

Bin Luo:

【Region matching】Template Matching With Deformable Diversity Similarity

Itamar Talmi:

Roey Mechrez:

Lihi Zelnik-Manor: http://lihi.eew.technion.ac.il/

【Region matching】A Combinatorial Solution to Non-Rigid 3D Shape-To-Image Matching

Florian Bernard:

Frank R. Schmidt:

Johan Thunberg:

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

【Region matching】A Minimal Solution for Two-View Focal-Length Estimation Using Two Affine Correspondences

Daniel Barath:

Tekla Toth:

Levente Hajder:

【Region matching】Direct Photometric Alignment by Mesh Deformation

Kaimo Lin:

Nianjuan Jiang:

Shuaicheng Liu:

Loong-Fah Cheong:

Minh Do:

Jiangbo Lu:

【Region matching】GMS: Grid-based Motion Statistics for Fast, Ultra-Robust Feature Correspondence

Jia Wang Bian:

Wen-Yan Lin:

Yasuyuki Matsushita:

Sai-Kit Yeung:

Tan-Dat Nguyen:

Ming-Ming Cheng:

【Region matching】Anchor Net: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching

David Novotny:

Diane Larlus:

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

【Region matching】Surface Motion Capture Transfer With Gaussian Process Regression

Adnane Boukhayma:

Jean-Sébastien Franco:

Edmond Boyer:

【Region matching】CLKN: Cascaded Lucas-Kanade Networks for Image Alignment

Che-Han Chang:

Chun-Nan Chou:

Edward Y. Chang:

【Region matching】A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching

Paul Swoboda:

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

Hassan Abu Alhaija:

Dagmar Kainmüller:

Bogdan Savchynskyy:

【Region matching】Object-Aware Dense Semantic Correspondence

Fan Yang:

Xin Li:

Hong Cheng:

Jianping Li:

Leiting Chen:

【Region matching】Alternating Direction Graph Matching

  1. Khuê Lê-Huu:

Nikos Paragios:

【Region matching】Joint Geometrical and Statistical Alignment for Visual Domain Adaptation

Jing Zhang:

Wanqing Li:

Philip Ogunbona:

【Region matching】Deep Semantic Feature Matching

Nikolai Ufer:

Björn Ommer:

【Region matching】SGM-Nets: Semi-Global Matching With Neural Networks

Akihito Seki:

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

【Image editing】Semantic Scene Completion From a Single Depth Image

Shuran Song:

Fisher Yu:

Andy Zeng:

Angel X. Chang:

Manolis Savva:

Thomas Funkhouser:

【Image editing】Deshadow Net: A Multi-Context Embedding Deep Network for Shadow Removal

Liangqiong Qu:

Jiandong Tian:

Shengfeng He:

Yandong Tang:

Rynson W. H. Lau:

【Image editing】Benchmarking Denoising Algorithms With Real Photographs

Tobias Plötz:

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

【Image editing】Style Bank: An Explicit Representation for Neural Image Style Transfer

Dongdong Chen:

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

Jing Liao:

Nenghai Yu:

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

【Image editing】Image Super-Resolution via Deep Recursive Residual Network

Ying Tai:

Jian Yang:

Xiaoming Liu:

【Image editing】Deep Image Harmonization

Yi-Hsuan Tsai:

Xiaohui Shen:

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

Kalyan Sunkavalli:

Xin Lu:

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

【Image editing】Learning Deep CNN Denoiser Prior for Image Restoration

PDF: https://github.com/cszn/ircnn

Kai Zhang:

Wangmeng Zuo:

Shuhang Gu:

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

【Image editing】Real-Time Video Super-Resolution With Spatio-Temporal Networks and Motion Compensation

Jose Caballero:

Christian Ledig:

Andrew Aitken:

Alejandro Acosta:

Johannes Totz:

Zehan Wang:

Wenzhe Shi:

【Image editing】From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur

Dong Gong:

Jie Yang:

Lingqiao Liu:

Yanning Zhang:

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

Chunhua Shen:

Anton van den Hengel:

Qinfeng Shi:

【Image editing】Noise-Blind Image Deblurring

Meiguang Jin:

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

Paolo Favaro:

【Image editing】Simultaneous Visual Data Completion and Denoising Based on Tensor Rank and Total Variation Minimization and Its Primal-Dual Splitting Algorithm

Tatsuya Yokota:

Hidekata Hontani:

【Image editing】Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization

Renwei Dian:

Leyuan Fang:

Shutao Li:

【Image editing】High-Resolution Image Inpainting Using Multi-Scale Neural Patch Synthesis

Chao Yang:

Xin Lu:

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

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

Oliver Wang:

Hao Li:

【Image editing】One-To-Many Network for Visually Pleasing Compression Artifacts Reduction

Jun Guo:

Hongyang Chao:

【Image editing】Full Resolution Image Compression With Recurrent Neural Networks

George Toderici:

Damien Vincent:

Nick Johnston:

Sung Jin Hwang:

David Minnen:

Joel Shor:

Michele Covell:

【Image editing】Neural Face Editing With Intrinsic Image Disentangling

Zhixin Shu:

Ersin Yumer:

Sunil Hadap:

Kalyan Sunkavalli:

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

Dimitris Samaras:

【Image editing】Joint Gap Detection and Inpainting of Line Drawings

Kazuma Sasaki:

Satoshi Iizuka:

Edgar Simo-Serra:

Hiroshi Ishikawa:

【Image editing】Non-Local Color Image Denoising With Convolutional Neural Networks

Stamatios Lefkimmiatis:

【Image editing】Generative Face Completion

Yijun Li:

Sifei Liu:

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

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

【Image editing】Hyper-Laplacian Regularized Unidirectional Low-Rank Tensor Recovery for Multispectral Image Denoising

Yi Chang:

Luxin Yan:

Sheng Zhong:

【Image editing】Scribbler: Controlling Deep Image Synthesis With Sketch and Color

Patsorn Sangkloy:

Jingwan Lu:

Chen Fang:

Fisher Yu:

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

【Image editing】Semantic Image Inpainting With Deep Generative Models

Raymond A. Yeh:

Chen Chen:

Teck Yian Lim:

Alexander G. Schwing:

Mark Hasegawa-Johnson:

Minh N. Do:

【Image editing】Image Deblurring via Extreme Channels Prior

Yanyang Yan:

Wenqi Ren:

Yuanfang Guo:

Rui Wang:

Xiaochun Cao:

【Image editing】Simultaneous Stereo Video Deblurring and Scene Flow Estimation

Liyuan Pan:

Yuchao Dai:

Miaomiao Liu:

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

【Image editing】Deep Photo Style Transfer

Fujun Luan:

Sylvain Paris: http://people.csail.mit.edu/sparis/

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

Kavita Bala:

【Image editing】Fast Haze Removal for Nighttime Image Using Maximum Reflectance Prior

Jing Zhang:

Yang Cao:

Shuai Fang:

Yu Kang:

Chang Wen Chen:

【Computational photography】Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

PDF: https://github.com/leehomyc/Photo-Realistic-Super-Resoluton

Christian Ledig:

Lucas Theis:

Ferenc Huszár:

Jose Caballero:

Andrew Cunningham:

Alejandro Acosta:

Andrew Aitken:

Alykhan Tejani:

Johannes Totz:

Zehan Wang:

Wenzhe Shi:

【Computational photography】Dynamic Time-Of-Flight

Michael Schober:

Amit Adam:

Omer Yair:

Shai Mazor:

Sebastian Nowozin:

【Computational photography】Deep Video Deblurring for Hand-Held Cameras

Shuochen Su:

Mauricio Delbracio:

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

Guillermo Sapiro:

Wolfgang Heidrich:

Oliver Wang:

【Computational photography】Deep Multi-Scale Convolutional Neural Network for Dynamic Scene Deblurring

Seungjun Nah:

Tae Hyun Kim:

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

【Computational photography】Deeply Aggregated Alternating Minimization for Image Restoration

Youngjung Kim:

Hyungjoo Jung:

Dongbo Min:

Kwanghoon Sohn:

【Computational photography】Wetness and Color From a Single Multispectral Image

Mihoko Shimano:

Hiroki Okawa:

Yuta Asano:

Ryoma Bise:

Ko Nishino:

Imari Sato:

【Computational photography】FC4: Fully Convolutional Color Constancy With Confidence-Weighted Pooling

Yuanming Hu:

Baoyuan Wang:

Stephen Lin:

【Computational photography】Face Normals “In-The-Wild” Using Fully Convolutional Networks

George Trigeorgis:

Patrick Snape:

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

Stefanos Zafeiriou:

【Computational photography】A Non-Local Low-Rank Framework for Ultrasound Speckle Reduction

Lei Zhu:

Chi-Wing Fu:

Michael S. Brown:

Pheng-Ann Heng:

【Computational photography】Video Acceleration Magnification

Yichao Zhang:

Silvia L. Pintea:

Jan C. van Gemert:

【Computational photography】What Is the Space of Attenuation Coefficients in Underwater Computer Vision?

Derya Akkaynak:

Tali Treibitz:

Tom Shlesinger:

Yossi Loya:

Raz Tamir:

David Iluz:

【Computational photography】Robust Energy Minimization for BRDF-Invariant Shape From Light Fields

Zhengqin Li:

Zexiang Xu:

Ravi Ramamoorthi:

Manmohan Chandraker:

【Computational photography】Model-Based Iterative Restoration for Binary Document Image Compression With Dictionary Learning

Yandong Guo:

Cheng Lu:

Jan P. Allebach:

Charles A. Bouman:

【Computational photography】A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment

PDF: https://arxiv.org/pdf/1704.00248.pdf

Shuang Ma:

Jing Liu:

Chang Wen Chen:

【Computational photography】Colorization as a Proxy Task for Visual Understanding

Gustav Larsson:

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

Gregory Shakhnarovich:

【Computational photography】Shading Annotations in the Wild

Balazs Kovacs:

Sean Bell:

Noah Snavely:

Kavita Bala:

【Computational photography】Efficient Diffusion on Region Manifolds: Recovering Small Objects With Compact CNN Representations

Ahmet Iscen:

Giorgos Tolias:

Yannis Avrithis:

Teddy Furon:

Ondřej Chum:

【Computational photography】Style Net: Generating Attractive Visual Captions With Styles

Chuang Gan:

Zhe Gan:

Xiaodong He:

Jianfeng Gao:

Li Deng:

【Computational photography】From Red Wine to Red Tomato: Composition With Context

Ishan Misra:

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

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

【Computational photography】Acquiring Axially-Symmetric Transparent Objects Using Single-View Transmission Imaging

Jaewon Kim:

Ilya Reshetouski:

Abhijeet Ghosh:

【Computational photography】Turning an Urban Scene Video Into a Cinemagraph

Hang Yan:

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

Yasutaka Furukawa:

【Computational photography】Attention-Aware Face Hallucination via Deep Reinforcement Learning

Qingxing Cao:

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

Yukai Shi:

Xiaodan Liang:

Guanbin Li:

【Computational photography】Anti-Glare: Tightly Constrained Optimization for Eyeglass Reflection Removal

Tushar Sandhan:

Jin Young Choi:

【Computational photography】Deep Joint Rain Detection and Removal From a Single Image

Wenhan Yang:

Robby T. Tan:

Jiashi Feng:

Jiaying Liu:

Zongming Guo:

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

【Computational photography】Radiometric Calibration From Faces in Images

Chen Li:

Stephen Lin:

Kun Zhou:

Katsushi Ikeuchi:

【Computational photography】Removing Rain From Single Images via a Deep Detail Network

Xueyang Fu:

Jiabin Huang:

Delu Zeng:

Yue Huang:

Xinghao Ding:

John Paisley:

【Computational photography】Single Image Reflection Suppression

Nikolaos Arvanitopoulos:

Radhakrishna Achanta:

Sabine Süsstrunk:

【Computational photography】Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

Thomas Nestmeyer:

Peter V. Gehler:

【Computational photography】Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework

Jongyoo Kim:

Sanghoon Lee:

【Computational photography】Illuminant-Camera Communication to Observe Moving Objects Under Strong External Light by Spread Spectrum Modulation

Ryusuke Sagawa:

Yutaka Satoh:

【Computational photography】Photorealistic Facial Texture Inference Using Deep Neural Networks

Shunsuke Saito:

Lingyu Wei:

Liwen Hu:

Koki Nagano:

Hao Li:

【Computational photography】The Geometry of First-Returning Photons for Non-Line-Of-Sight Imaging

Chia-Yin Tsai:

Kiriakos N. Kutulakos:

Srinivasa G. Narasimhan:

Aswin C. Sankaranarayanan:

【Computational photography】Unrolling the Shutter: CNN to Correct Motion Distortions

Vijay Rengarajan:

Yogesh Balaji:

  1. N. Rajagopalan:

【Computational photography】Light Field Blind Motion Deblurring

Pratul P. Srinivasan:

Ren Ng:

Ravi Ramamoorthi:

【Computational photography】Computational Imaging on the Electric Grid

Mark Sheinin:

Yoav Y. Schechner:

Kiriakos N. Kutulakos:

【Computational photography】Deep Outdoor Illumination Estimation

Yannick Hold-Geoffroy:

Kalyan Sunkavalli:

Sunil Hadap:

Emiliano Gambaretto:

Jean-François Lalonde:

【Computational photography】General Models for Rational Cameras and the Case of Two-Slit Projections

Matthew Trager:

Bernd Sturmfels:

John Canny:

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

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

【Computational photography】Non-Contact Full Field Vibration Measurement Based on Phase-Shifting

Hiroyuki Kayaba:

Yuji Kokumai:

【Computational photography】Designing Illuminant Spectral Power Distributions for Surface Classification

Henryk Blasinski:

Joyce Farrell:

Brian Wandell:

【Computational photography】One-Shot Hyperspectral Imaging Using Faced Reflectors

Tsuyoshi Takatani:

Takahito Aoto:

Yasuhiro Mukaigawa:

【Computational photography】A Unified Approach of Multi-Scale Deep and Hand-Crafted Features for Defocus Estimation

PDF: https://github.com/zzangjinsun/DHDE_CVPR17

Jinsun Park:

Yu-Wing Tai:

Donghyeon Cho:

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

【Computational photography】Specular Highlight Removal in Facial Images

Chen Li:

Stephen Lin:

Kun Zhou:

Katsushi Ikeuchi:

【Computational photography】A Novel Tensor-Based Video Rain Streaks Removal Approach via Utilizing Discriminatively Intrinsic Priors

Tai-Xiang Jiang:

Ting-Zhu Huang:

Xi-Le Zhao:

Liang-Jian Deng:

Yao Wang:

【Computational photography】Video Desnowing and Deraining Based on Matrix Decomposition

Weihong Ren:

Jiandong Tian:

Zhi Han:

Antoni Chan:

Yandong Tang:

【Computational photography】Learning Diverse Image Colorization

Aditya Deshpande:

Jiajun Lu:

Mao-Chuang Yeh:

Min Jin Chong:

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

【Computational photography】Awesome Typography: Statistics-Based Text Effects Transfer

Shuai Yang:

Jiaying Liu:

Zhouhui Lian:

Zongming Guo:

【Computational photography】AGA: Attribute-Guided Augmentation

Mandar Dixit:

Roland Kwitt:

Marc Niethammer:

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

【Computational photography】Towards a Quality Metric for Dense Light Fields

Vamsi Kiran Adhikarla:

Marek Vinkler:

Denis Sumin:

Rafał K. Mantiuk:

Karol Myszkowski:

Hans-Peter Seidel:

Piotr Didyk:

【Computational photography】A Wide-Field-Of-View Monocentric Light Field Camera

Donald G. Dansereau:

Glenn Schuster:

Joseph Ford:

Gordon Wetzstein:

【Computational photography】Co-Occurrence Filter

Roy J. Jevnisek:

Shai Avidan:

【Computational photography】Fractal Dimension Invariant Filtering and Its CNN-Based Implementation

Hongteng Xu:

Junchi Yan:

Nils Persson:

Weiyao Lin:

Hongyuan Zha:

【Computational photography】Reflection Removal Using Low-Rank Matrix Completion

Byeong-Ju Han:

Jae-Young Sim:

【Computational photography】Object Co-Skeletonization With Co-Segmentation

Koteswar Rao Jerripothula:

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

Jiangbo Lu:

Junsong Yuan:

【Computational photography】Fast-At: Fast Automatic Thumbnail Generation Using Deep Neural Networks

Seyed A. Esmaeili:

Bharat Singh:

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

【Computational photography】Discriminative Correlation Filter With Channel and Spatial Reliability

Alan Lukežič:

Tomáš Vojíř:

Luka ÄŒehovin Zajc:

Jiří Matas:

Matej Kristan:

【Computational photography】Hardware-Efficient Guided Image Filtering for Multi-Label Problem

Longquan Dai:

Mengke Yuan:

Zechao Li:

Xiaopeng Zhang:

Jinhui Tang:

【Computational photography】Filter Flow Made Practical: Massively Parallel and Lock-Free

Sathya N. Ravi:

Yunyang Xiong:

Lopamudra Mukherjee:

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

【Computational photography】Hallucinating Very Low-Resolution Unaligned and Noisy Face Images by Transformative Discriminative Autoencoders

Xin Yu:

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

【Computational photography】Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images Using Weakly-Supervised Joint Convolutional Sparse Coding

Yawen Huang:

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

Alejandro F. Frangi:

【Computational photography】Multiple-Scattering Microphysics Tomography

Aviad Levis:

Yoav Y. Schechner:

Anthony B. Davis:

【Computational photography】Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution

Wei-Sheng Lai:

Jia-Bin Huang:

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

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

【Computational photography】Why You Should Forget Luminance Conversion and Do Something Better

Rang M. H. Nguyen:

Michael S. Brown:

【Computational photography】Adaptive and Move Making Auxiliary Cuts for Binary Pairwise Energies

Lena Gorelick:

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

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

【Computational photography】Deep Feature Interpolation for Image Content Changes

Paul Upchurch:

Jacob Gardner:

Geoff Pleiss:

Robert Pless:

Noah Snavely:

Kavita Bala:

Kilian Weinberger:

【Computational photography】On the Effectiveness of Visible Watermarks

Tali Dekel:

Michael Rubinstein: http://people.csail.mit.edu/mrub/

Ce Liu: http://people.csail.mit.edu/celiu/

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

【Computational photography】Snapshot Hyperspectral Light Field Imaging

Zhiwei Xiong:

Lizhi Wang:

Huiqun Li:

Dong Liu:

Feng Wu:

【Computational photography】Fast Fourier Color Constancy

Jonathan T. Barron:

Yun-Ta Tsai:

【Computational photography】Learning Fully Convolutional Networks for Iterative Non-Blind Deconvolution

Jiawei Zhang:

Jinshan Pan:

Wei-Sheng Lai:

Rynson W. H. Lau:

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

【Computational photography】Neural Scene De-Rendering

Jiajun Wu:

Joshua B. Tenenbaum:

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

【Computational photography】Real-Time Neural Style Transfer for Videos

Haozhi Huang:

Hao Wang:

Wenhan Luo:

Lin Ma:

Wenhao Jiang:

Xiaolong Zhu:

Zhifeng Li:

Wei Liu:

【Computational photography】Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer

Xin Wang:

Geoffrey Oxholm:

Da Zhang:

Yuan-Fang Wang:

【Computational photography】ROAM: A Rich Object Appearance Model With Application to Rotoscoping

Ondrej Miksik:

Juan-Manuel Pérez-Rúa:

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

Patrick Pérez:

【Texture analysis】Diversified Texture Synthesis With Feed-Forward Networks

Yijun Li:

Chen Fang:

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

Zhaowen Wang:

Xin Lu:

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

【Texture analysis】Material Classification Using Frequency- and Depth-Dependent Time-Of-Flight Distortion

Kenichiro Tanaka:

Yasuhiro Mukaigawa:

Takuya Funatomi:

Hiroyuki Kubo:

Yasuyuki Matsushita:

Yasushi Yagi:

【Texture analysis】Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis

Dmitry Ulyanov:

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

Victor Lempitsky:

【Texture analysis】Connecting Look and Feel: Associating the Visual and Tactile Properties of Physical Materials

Wenzhen Yuan:

Shaoxiong Wang:

Siyuan Dong:

Edward Adelson:

【Texture analysis】FASON: First and Second Order Information Fusion Network for Texture Recognition

Xiyang Dai:

Joe Yue-Hei Ng:

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

【Texture analysis】Differential Angular Imaging for Material Recognition

Jia Xue:

Hang Zhang:

Kristin Dana:

Ko Nishino:

【Medical image】Chest X-ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Xiaosong Wang:

Yifan Peng:

Le Lu:

Zhiyong Lu:

Mohammadhadi Bagheri:

Ronald M. Summers:

【Medical image】MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network

Zizhao Zhang:

Yuanpu Xie:

Fuyong Xing:

Mason Mc Gough:

Lin Yang:

【Medical image】Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation

Kuan-Lun Tseng:

Yen-Liang Lin:

Winston Hsu:

Chung-Yang Huang:

【Medical image】Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally

Zongwei Zhou:

Jae Shin:

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

Suryakanth Gurudu:

Michael Gotway:

Jianming Liang:

【Data clustering】Exclusivity-Consistency Regularized Multi-View Subspace Clustering

Xiaojie Guo:

Xiaobo Wang:

Zhen Lei:

Changqing Zhang:

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

【Data clustering】Subspace Clustering via Variance Regularized Ridge Regression

Zhao Kang:

Chong Peng:

Qiang Cheng:

【Data clustering】Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering

Junbin Gao:

Qiong Wang:

Hong Li:

【Space reduction】On Compressing Deep Models by Low Rank and Sparse Decomposition

Xiyu Yu:

Tongliang Liu:

Xinchao Wang:

Dacheng Tao:

【Space reduction】The Incremental Multiresolution Matrix Factorization Algorithm

Vamsi K. Ithapu:

Risi Kondor:

Sterling C. Johnson:

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

【Space reduction】Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning

Rudrasis Chakraborty:

Søren Hauberg:

Baba C. Vemuri:

【Space reduction】Non-Uniform Subset Selection for Active Learning in Structured Data

Sujoy Paul:

Jawadul H. Bappy:

Amit K. Roy-Chowdhury:

【Space reduction】Low-Rank-Sparse Subspace Representation for Robust Regression

Yongqiang Zhang:

Daming Shi:

Junbin Gao:

Dansong Cheng:

【Machine learning】An Exact Penalty Method for Locally Convergent Maximum Consensus

PDF: https://drive.google.com/open?id=0B6o6fIa7jhmCSEwwMVRsWmM5S1E

Huu Le:

Tat-Jun Chin:

David Suter:

【Machine learning】Viraliency: Pooling Local Virality

Xavier Alameda-Pineda:

Andrea Pilzer:

Dan Xu:

Nicu Sebe:

Elisa Ricci:

【Machine learning】Learning by Association — A Versatile Semi-Supervised Training Method for Neural Networks

Philip Haeusser:

Alexander Mordvintsev:

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

【Machine learning】Additive Component Analysis

Calvin Murdock:

Fernando De la Torre:

【Machine learning】The Impact of Typicality for Informative Representative Selection

Jawadul H. Bappy:

Sujoy Paul:

Ertem Tuncel:

Amit K. Roy-Chowdhury:

【Machine learning】Infinite Variational Autoencoder for Semi-Supervised Learning

  1. Ehsan Abbasnejad:

Anthony Dick:

Anton van den Hengel:

【Machine learning】Variational Bayesian Multiple Instance Learning With Gaussian Processes

Manuel Haußmann:

Fred A. Hamprecht:

Melih Kandemir:

【Machine learning】AMVH: Asymmetric Multi-Valued Hashing

Cheng Da:

Shibiao Xu:

Kun Ding:

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

Shiming Xiang:

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

【Machine learning】Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation

Hongliang Yan:

Yukang Ding:

Peihua Li:

Qilong Wang:

Yong Xu:

Wangmeng Zuo:

【Machine learning】Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications

Evgeny Levinkov:

Jonas Uhrig:

Siyu Tang:

Mohamed Omran:

Eldar Insafutdinov:

Alexander Kirillov:

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

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

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

Bjoern Andres:

【Machine learning】Generative Hierarchical Learning of Sparse FRAME Models

Jianwen Xie:

Yifei Xu:

Erik Nijkamp:

Ying Nian Wu:

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

【Machine learning】Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning

Xing Xu:

Fumin Shen:

Yang Yang:

Dongxiang Zhang:

Heng Tao Shen:

Jingkuan Song:

【Machine learning】Deep Metric Learning via Facility Location

Hyun Oh Song:

Stefanie Jegelka:

Vivek Rathod:

Kevin Murphy:

【Machine learning】Efficient Solvers for Minimal Problems by Syzygy-Based Reduction

Viktor Larsson:

Kalle Åström:

Magnus Oskarsson:

【Machine learning】A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems

Paul Swoboda:

Jan Kuske:

Bogdan Savchynskyy:

【Machine learning】Efficient Linear Programming for Dense CRFs

Thalaiyasingam Ajanthan:

Alban Desmaison:

Rudy Bunel:

Mathieu Salzmann:

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

  1. Pawan Kumar:

【Machine learning】Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold

Young Joon Yoo:

Sangdoo Yun:

Hyung Jin Chang:

Yiannis Demiris:

Jin Young Choi:

【Machine learning】Generating the Future With Adversarial Transformers

Carl Vondrick:

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

【Machine learning】Learning a Deep Embedding Model for Zero-Shot Learning

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

Tao Xiang:

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

【Machine learning】Growing a Brain: Fine-Tuning by Increasing Model Capacity

Yu-Xiong Wang:

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

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

【Machine learning】Zero-Shot Learning – the Good, the Bad and the Ugly

Yongqin Xian:

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

Zeynep Akata:

【Machine learning】Scene Graph Generation by Iterative Message Passing

Danfei Xu:

Yuke Zhu:

Christopher B. Choy:

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

【Machine learning】Comprehension-Guided Referring Expressions

Ruotian Luo:

Gregory Shakhnarovich:

【Machine learning】Binge Watching: Scaling Affordance Learning From Sitcoms

Xiaolong Wang:

Rohit Girdhar:

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

【Machine learning】Outlier-Robust Tensor PCA

Pan Zhou:

Jiashi Feng:

【Machine learning】Learning an Invariant Hilbert Space for Domain Adaptation

Samitha Herath:

Mehrtash Harandi:

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

【Machine learning】Soft-Margin Mixture of Regressions

Dong Huang:

Longfei Han:

Fernando De la Torre:

【Machine learning】DOPE: Distributed Optimization for Pairwise Energies

Jose Dolz:

Ismail Ben Ayed:

Christian Desrosiers:

【Machine learning】Probabilistic Temporal Subspace Clustering

Vladimir Pavlovic:

Behnam Gholami:

【Machine learning】Provable Self-Representation Based Outlier Detection in a Union of Subspaces

Chong You:

Daniel P. Robinson:

René Vidal:

【Machine learning】Latent Multi-View Subspace Clustering

Changqing Zhang:

Qinghua Hu:

Huazhu Fu:

Pengfei Zhu:

Xiaochun Cao:

【Machine learning】Compact Matrix Factorization With Dependent Subspaces

Viktor Larsson:

Carl Olsson:

【Machine learning】FFTLasso: Large-Scale LASSO in the Fourier Domain

Adel Bibi:

Hani Itani:

Bernard Ghanem:

【Machine learning】A Message Passing Algorithm for the Minimum Cost Multicut Problem

Paul Swoboda:

Bjoern Andres:

【Machine learning】Online Graph Completion: Multivariate Signal Recovery in Computer Vision

Won Hwa Kim:

Mona Jalal:

Seongjae Hwang:

Sterling C. Johnson:

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

【Machine learning】Consensus Maximization With Linear Matrix Inequality Constraints

Pablo Speciale:

Danda Pani Paudel:

Martin R. Oswald:

Till Kroeger:

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

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

【Machine learning】Zero Shot Learning via Multi-Scale Manifold Regularization

Shay Deutsch:

Soheil Kolouri:

Kyungnam Kim:

Yuri Owechko:

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

【Machine learning】A Matrix Splitting Method for Composite Function Minimization

Ganzhao Yuan:

Wei-Shi Zheng:

Bernard Ghanem:

【Machine learning】Hidden Layers in Perceptual Learning

Gad Cohen:

Daphna Weinshall: http://www.cs.huji.ac.il/~daphna/

【Machine learning】A Clever Elimination Strategy for Efficient Minimal Solvers

Zuzana Kukelova:

Joe Kileel:

Bernd Sturmfels:

Tomas Pajdla:

【Machine learning】Convex Global 3D Registration With Lagrangian Duality

Jesus Briales:

Javier Gonzalez-Jimenez:

【Machine learning】Robust Joint and Individual Variance Explained

Christos Sagonas:

Yannis Panagakis:

Alina Leidinger:

Stefanos Zafeiriou:

【Machine learning】Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging

Hyunwoo J. Kim:

Nagesh Adluru:

Heemanshu Suri:

Baba C. Vemuri:

Sterling C. Johnson:

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

【Machine learning】Revisiting the Variable Projection Method for Separable Nonlinear Least Squares Problems

Je Hyeong Hong:

Christopher Zach:

Andrew Fitzgibbon:

【Machine learning】Efficient Multiple Instance Metric Learning Using Weakly Supervised Data

Marc T. Law:

Yaoliang Yu:

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

Richard S. Zemel:

Eric P. Xing:

【Machine learning】Learning the Multilinear Structure of Visual Data

Mengjiao Wang:

Yannis Panagakis:

Patrick Snape:

Stefanos Zafeiriou:

【Machine learning】Product Split Trees

Artem Babenko:

Victor Lempitsky:

【Machine learning】Learning Non-Maximum Suppression

Jan Hosang:

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

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

【Machine learning】Learning From Noisy Large-Scale Datasets With Minimal Supervision

Andreas Veit:

Neil Alldrin:

Gal Chechik:

Ivan Krasin:

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

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

【Machine learning】Analyzing Computer Vision Data – The Good, the Bad and the Ugly

Oliver Zendel:

Katrin Honauer:

Markus Murschitz:

Martin Humenberger:

Gustavo Fernández Domínguez:

【Machine learning】Product Manifold Filter: Non-Rigid Shape Correspondence via Kernel Density Estimation in the Product Space

Matthias Vestner:

Roee Litman:

Emanuele RodolÃ:

Alex Bronstein:

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

【Machine learning】Binarized Mode Seeking for Scalable Visual Pattern Discovery

Wei Zhang:

Xiaochun Cao:

Rui Wang:

Yuanfang Guo:

Zhineng Chen:

【Machine learning】WSISA: Making Survival Prediction From Whole Slide Histopathological Images

Xinliang Zhu:

Jiawen Yao:

Feiyun Zhu:

Junzhou Huang:

【Machine learning】Revisiting Metric Learning for SPD Matrix Based Visual Representation

Luping Zhou:

Lei Wang:

Jianjia Zhang:

Yinghuan Shi:

Yang Gao:

【Machine learning】Expert Gate: Lifelong Learning With a Network of Experts

Rahaf Aljundi:

Punarjay Chakravarty:

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

【Machine learning】A Gift From Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning

Junho Yim:

Donggyu Joo:

Jihoon Bae:

Junmo Kim:

【Machine learning】Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors

Piotr Koniusz:

Yusuf Tas:

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

【Machine learning】Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems

Daiki Ikami:

Toshihiko Yamasaki:

Kiyoharu Aizawa:

【Machine learning】Newton-Type Methods for Inference in Higher-Order Markov Random Fields

Hariprasad Kannan:

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

Nikos Paragios:

【Machine learning】Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation

Zheng Xu:

Mário A. T. Figueiredo:

Xiaoming Yuan:

Christoph Studer:

Tom Goldstein:

【Deep learning】Borrowing Treasures From the Wealthy: Deep Transfer Learning Through Selective Joint Fine-Tuning

Weifeng Ge:

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

【Deep learning】Universal Adversarial Perturbations

Seyed-Mohsen Moosavi-Dezfooli:

Alhussein Fawzi:

Omar Fawzi:

Pascal Frossard:

【Deep learning】Unsupervised Pixel-Level Domain Adaptation With Generative Adversarial Networks

Konstantinos Bousmalis:

Nathan Silberman:

David Dohan:

Dumitru Erhan:

Dilip Krishnan:

【Deep learning】Dilated Residual Networks

Fisher Yu:

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

Thomas Funkhouser:

【Deep learning】Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction

Richard Zhang:

Phillip Isola:

Alexei A. Efros:

【Deep learning】Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting

Mariano Tepper:

Guillermo Sapiro:

【Deep learning】Truncated Max-Of-Convex Models

Pankaj Pansari:

  1. Pawan Kumar:

【Deep learning】Teaching Compositionality to CNNs

Austin Stone:

Huayan Wang:

Michael Stark:

Yi Liu:

  1. Scott Phoenix:

Dileep George:

【Deep learning】LCNN: Lookup-Based Convolutional Neural Network

Hessam Bagherinezhad:

Mohammad Rastegari:

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

【Deep learning】Improving Interpretability of Deep Neural Networks With Semantic Information

Yinpeng Dong:

Hang Su:

Jun Zhu:

Bo Zhang:

【Deep learning】Deep Reinforcement Learning-Based Image Captioning With Embedding Reward

Zhou Ren:

Xiaoyu Wang:

Ning Zhang:

Xutao Lv:

Li-Jia Li:

【Deep learning】Deep Temporal Linear Encoding Networks

Ali Diba:

Vivek Sharma:

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

【Deep learning】Conditional Similarity Networks

Andreas Veit:

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

Theofanis Karaletsos:

【Deep learning】Spatially Adaptive Computation Time for Residual Networks

Michael Figurnov:

Maxwell D. Collins:

Yukun Zhu:

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

Jonathan Huang:

Dmitry Vetrov:

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

【Deep learning】Xception: Deep Learning With Depthwise Separable Convolutions

François Chollet:

【Deep learning】Feedback Networks

Amir R. Zamir:

Te-Lin Wu:

Lin Sun:

William B. Shen:

Bertram E. Shi:

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

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

【Deep learning】Online Summarization via Submodular and Convex Optimization

Ehsan Elhamifar:

  1. Clara De Paolis Kaluza:

【Deep learning】Stacked Generative Adversarial Networks

Xun Huang:

Yixuan Li:

Omid Poursaeed:

John Hopcroft:

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

【Deep learning】More Is Less: A More Complicated Network With Less Inference Complexity

Xuanyi Dong:

Junshi Huang:

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

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

【Deep learning】Deep Unsupervised Similarity Learning Using Partially Ordered Sets

Miguel A. Bautista:

Artsiom Sanakoyeu:

Björn Ommer:

【Deep learning】Interpretable Structure-Evolving LSTM

Xiaodan Liang:

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

Xiaohui Shen:

Jiashi Feng:

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

Eric P. Xing:

【Deep learning】Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach

Giorgio Patrini:

Alessandro Rozza:

Aditya Krishna Menon:

Richard Nock:

Lizhen Qu:

【Deep learning】Learning From Simulated and Unsupervised Images Through Adversarial Training

Ashish Shrivastava:

Tomas Pfister:

Oncel Tuzel:

Joshua Susskind:

Wenda Wang:

Russell Webb:

【Deep learning】Inverse Compositional Spatial Transformer Networks

Chen-Hsuan Lin:

Simon Lucey:

【Deep learning】Densely Connected Convolutional Networks

Gao Huang:

Zhuang Liu:

Laurens van der Maaten:

Kilian Q. Weinberger:

【Deep learning】Deep TEN: Texture Encoding Network

Hang Zhang:

Jia Xue:

Kristin Dana:

【Deep learning】Kernel Pooling for Convolutional Neural Networks

Yin Cui:

Feng Zhou:

Jiang Wang:

Xiao Liu:

Yuanqing Lin:

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

【Deep learning】Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

Anh Nguyen:

Jeff Clune:

Yoshua Bengio: http://lisa.iro.umontreal.ca/index_en.html

Alexey Dosovitskiy:

Jason Yosinski:

【Deep learning】Controlling Perceptual Factors in Neural Style Transfer

Leon A. Gatys:

Alexander S. Ecker:

Matthias Bethge:

Aaron Hertzmann:

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

【Deep learning】Fixed-Point Factorized Networks

Peisong Wang:

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

【Deep learning】S3Pool: Pooling With Stochastic Spatial Sampling

Shuangfei Zhai:

Hui Wu:

Abhishek Kumar:

Yu Cheng:

Yongxi Lu:

Zhongfei Zhang:

Rogerio Feris: http://rogerioferis.com/

【Deep learning】Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation

Binghui Chen:

Weihong Deng:

Junping Du:

【Deep learning】Network Sketching: Exploiting Binary Structure in Deep CNNs

Yiwen Guo:

Anbang Yao:

Hao Zhao:

Yurong Chen:

【Deep learning】Multigrid Neural Architectures

Tsung-Wei Ke:

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

Stella X. Yu:

【Deep learning】Deep Quantization: Encoding Convolutional Activations With Deep Generative Model

Zhaofan Qiu:

Ting Yao:

Tao Mei:

【Deep learning】Local Binary Convolutional Neural Networks

Felix Juefei-Xu:

Vishnu Naresh Boddeti:

Marios Savvides:

【Deep learning】On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution

Yuqian Zhang:

Yenson Lau:

Han-wen Kuo:

Sky Cheung:

Abhay Pasupathy:

John Wright:

【Deep learning】Global Optimality in Neural Network Training

Benjamin D. Haeffele:

René Vidal:

【Deep learning】Modeling Relationships in Referential Expressions With Compositional Modular Networks

Ronghang Hu:

Marcus Rohrbach:

Jacob Andreas:

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

Kate Saenko:

【Deep learning】De Li GAN : Generative Adversarial Networks for Diverse and Limited Data

Swaminathan Gurumurthy:

Ravi Kiran Sarvadevabhatla:

  1. Venkatesh Babu:

【Deep learning】Oriented Response Networks

Yanzhao Zhou:

Qixiang Ye:

Qiang Qiu:

Jianbin Jiao:

【Deep learning】Missing Modalities Imputation via Cascaded Residual Autoencoder

Luan Tran:

Xiaoming Liu:

Jiayu Zhou:

Rong Jin:

【Deep learning】All You Need Is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks With Orthonormality and Modulation

Di Xie:

Jiang Xiong:

Shiliang Pu:

【Deep learning】Hard Mixtures of Experts for Large Scale Weakly Supervised Vision

Sam Gross:

Marc’Aurelio Ranzato:

Arthur Szlam:

【Deep learning】Adversarial Discriminative Domain Adaptation

Eric Tzeng:

Judy Hoffman:

Kate Saenko:

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

【Deep learning】Weakly Supervised Cascaded Convolutional Networks

Ali Diba:

Vivek Sharma:

Ali Pazandeh:

Hamed Pirsiavash:

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

【Deep learning】Deep Learning With Low Precision by Half-Wave Gaussian Quantization

Zhaowei Cai:

Xiaodong He:

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

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

【Deep learning】Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs

Federico Monti:

Davide Boscaini:

Jonathan Masci:

Emanuele RodolÃ:

Jan Svoboda:

Michael M. Bronstein:

【Deep learning】Ubernet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory

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

【Deep learning】Deep Roots: Improving CNN Efficiency With Hierarchical Filter Groups

Yani Ioannou:

Duncan Robertson:

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

Antonio Criminisi:

【Deep learning】Aggregated Residual Transformations for Deep Neural Networks

Saining Xie:

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

Piotr Dollár:

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

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

【Deep learning】Deep Perm Net: Visual Permutation Learning

Rodrigo Santa Cruz:

Basura Fernando:

Anoop Cherian:

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

【Deep learning】Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning

Tien-Ju Yang:

Yu-Hsin Chen:

Vivienne Sze:

【Deep learning】Multi-Attention Network for One Shot Learning

Peng Wang:

Lingqiao Liu:

Chunhua Shen:

Zi Huang:

Anton van den Hengel:

Heng Tao Shen:

【Deep learning】Deep Pyramidal Residual Networks

Dongyoon Han:

Jiwhan Kim:

Junmo Kim:

【Deep learning】Generative Attribute Controller With Conditional Filtered Generative Adversarial Networks

Takuhiro Kaneko:

Kaoru Hiramatsu:

Kunio Kashino:

【Deep learning】Weighted-Entropy-Based Quantization for Deep Neural Networks

Eunhyeok Park:

Junwhan Ahn:

Sungjoo Yoo:

【Deep learning】Vi P-CNN: Visual Phrase Guided Convolutional Neural Network

Yikang Li:

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

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

Xiao’ou Tang:

【Multimodel learning】Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos

De-An Huang:

Joseph J. Lim:

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

Juan Carlos Niebles:

【Multimodel learning】Dual Attention Networks for Multimodal Reasoning and Matching

Hyeonseob Nam:

Jung-Woo Ha:

Jeonghee Kim:

【Multimodel learning】Visual Dialog

Abhishek Das:

Satwik Kottur:

Khushi Gupta:

Avi Singh:

Deshraj Yadav:

José M. F. Moura:

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

Dhruv Batra:

【Multimodel learning】Order-Preserving Wasserstein Distance for Sequence Matching

Bing Su:

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

【Multimodel learning】Attend in Groups: A Weakly-Supervised Deep Learning Framework for Learning From Web Data

Bohan Zhuang:

Lingqiao Liu:

Yao Li:

Chunhua Shen:

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

【Multimodel learning】Hierarchical Multimodal Metric Learning for Multimodal Classification

Heng Zhang:

Vishal M. Patel:

Rama Chellappa:

【Multimodel learning】Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

Amaia Salvador:

Nicholas Hynes:

Yusuf Aytar:

Javier Marin:

Ferda Ofli:

Ingmar Weber:

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

【Multimodel learning】Visual Translation Embedding Network for Visual Relation Detection

Hanwang Zhang:

Zawlin Kyaw:

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

Tat-Seng Chua:

【Multimodel learning】Graph-Structured Representations for Visual Question Answering

Damien Teney:

Lingqiao Liu:

Anton van den Hengel:

【Multimodel learning】Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning

Jiasen Lu:

Caiming Xiong:

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

Richard Socher:

【Multimodel learning】End-To-End Concept Word Detection for Video Captioning, Retrieval, and Question Answering

Youngjae Yu:

Hyungjin Ko:

Jongwook Choi:

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

【Multimodel learning】A Hierarchical Approach for Generating Descriptive Image Paragraphs

Jonathan Krause:

Justin Johnson:

Ranjay Krishna:

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

【Multimodel learning】Re-Sign: Re-Aligned End-To-End Sequence Modelling With Deep Recurrent CNN-HMMs

Oscar Koller:

Sepehr Zargaran:

Hermann Ney:

【Multimodel learning】Lip Reading Sentences in the Wild

Joon Son Chung:

Andrew Senior:

Oriol Vinyals:

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

【Multimodel learning】A Joint Speaker-Listener-Reinforcer Model for Referring Expressions

Licheng Yu:

Hao Tan:

Mohit Bansal:

Tamara L. Berg:

【Multimodel learning】Mining Object Parts From CNNs via Active Question-Answering

Quanshi Zhang:

Ruiming Cao:

Ying Nian Wu:

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

【Multimodel learning】The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions

Peng Wang:

Qi Wu:

Chunhua Shen:

Anton van den Hengel:

【Multimodel learning】Online Asymmetric Similarity Learning for Cross-Modal Retrieval

Yiling Wu:

Shuhui Wang:

Qingming Huang:

【Multimodel learning】Multi-Level Attention Networks for Visual Question Answering

Dongfei Yu:

Jianlong Fu:

Tao Mei:

Yong Rui:

【Multimodel learning】Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pierre Baqué:

François Fleuret:

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

【Multimodel learning】Guess What?! Visual Object Discovery Through Multi-Modal Dialogue

Harm de Vries:

Florian Strub:

Sarath Chandar:

Olivier Pietquin:

Hugo Larochelle:

Aaron Courville:

【Multimodel learning】Deep Multimodal Representation Learning From Temporal Data

Xitong Yang:

Palghat Ramesh:

Radha Chitta:

Sriganesh Madhvanath:

Edgar A. Bernal:

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

【Multimodel learning】Person Search With Natural Language Description

Shuang Li:

Tong Xiao:

Hongsheng Li:

Bolei Zhou:

Dayu Yue:

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

【Multimodel learning】Weakly-Supervised Visual Grounding of Phrases With Linguistic Structures

Fanyi Xiao:

Leonid Sigal:

Yong Jae Lee:

【Multimodel learning】Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension

Aniruddha Kembhavi:

Minjoon Seo:

Dustin Schwenk:

Jonghyun Choi:

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

Hannaneh Hajishirzi:

【Multimodel learning】Creativity: Generating Diverse Questions Using Variational Autoencoders

Unnat Jain:

Ziyu Zhang:

Alexander G. Schwing:

【Multimodel learning】Image-To-Image Translation With Conditional Adversarial Networks

Phillip Isola:

Jun-Yan Zhu:

Tinghui Zhou:

Alexei A. Efros:

【Multimodel learning】Knowledge Acquisition for Visual Question Answering via Iterative Querying

Yuke Zhu:

Joseph J. Lim:

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

【Multimodel learning】Link the Head to the “Beak”: Zero Shot Learning From Noisy Text Description at Part Precision

Mohamed Elhoseiny:

Yizhe Zhu:

Han Zhang:

Ahmed Elgammal:

【Multimodel learning】Making the v in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering

Yash Goyal:

Tejas Khot:

Douglas Summers-Stay:

Dhruv Batra:

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

【Multimodel learning】What’s in a Question: Using Visual Questions as a Form of Supervision

Siddha Ganju:

Olga Russakovsky:

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

【Multimodel learning】Attend to You: Personalized Image Captioning With Context Sequence Memory Networks

Cesc Chunseong Park:

Byeongchang Kim:

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

【Multimodel learning】The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives

Mohit Iyyer:

Varun Manjunatha:

Anupam Guha:

Yogarshi Vyas:

Jordan Boyd-Graber:

Hal Daumé III:

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

【Multimodel learning】Harmonic Networks: Deep Translation and Rotation Equivariance

Daniel E. Worrall:

Stephan J. Garbin:

Daniyar Turmukhambetov:

Gabriel J. Brostow:

【Multimodel learning】Instance-Aware Image and Sentence Matching With Selective Multimodal LSTM

Yan Huang:

Wei Wang:

Liang Wang:

【Multimodel learning】An Empirical Evaluation of Visual Question Answering for Novel Objects

Santhosh K. Ramakrishnan:

Ambar Pal:

Gaurav Sharma:

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

【Multimodel learning】Tracking by Natural Language Specification

Zhenyang Li:

Ran Tao:

Efstratios Gavves:

Cees G. M. Snoek:

Arnold W.M. Smeulders:

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