2015 ICCV

下面是2015ICCV文章的主题标签,文章列表来源于http://www.pamitc.org/cvpr15/program.php

PDF的网址:http://www.cv-foundation.org/openaccess/ICCV2015.py
Topic: Scene parsing; Object segmentation; Image segmentation; Video segmentation; Boundary detection; Contour analysis; Object tracking; Action recognition; Crowd analysis; Human detection; Human parsing; Face recognition; Face parsing; Object recognition; Object detection; Saliency detection; Scene recognition; Text recognition; Image retrieval; 3D modeling; Feature matching; Pose estimation; Stereo matching;Optical flow;Region matching; Image editing; Computational photography; Texture analysis; Data clustering; Space reduction; Machine learning; Deep learning;

【Scene parsing】Weakly supervised graph based semantic segmentation by learning communities of image-parts

Niloufar Pourian:

Karthikeyan Shanmuga Vadivel:

B.S. Manjunath:

【Scene parsing】Semantic Image Labeling via Deep Parsing Network

Ziwei Liu:

Xiaoxiao Li:

Ping Luo:

Chen-Change Loy:

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

【Scene parsing】Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing

Hamid Izadinia:

Fereshteh Sadeghi:

Santosh Kumar Divvala:

Hannaneh Hajishirzi:

Yejin Choi:

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

【Scene parsing】Learning Deconvolution Network for Semantic Segmentation

Hyeonwoo Noh:

Seunghoon Hong:

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

【Scene parsing】Parsimonious Labeling

Puneet Dokania:

Pawan Kumar:

【Scene parsing】Unsupervised Semantic Parsing of Video Collections

Ozan Sener:

Amir Zamir:

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

Ashutosh Saxena: http://www.cs.cornell.edu/~asaxena/

【Scene parsing】Constrained Convolutional Neural Networks for Weakly Supervised Semantic Segmentation

Deepak Pathak:

Philipp Krahenbuhl:

Evan Shelhamer:

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

【Scene parsing】Higher-order CRF Structural Segmentation of 3D Reconstructed Surfaces

Jingbo Liu:

Jinglu Wang:

Tian Fang:

Chiew-Lan Tai:

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

【Scene parsing】Visual Madlibs: Fill in the blank Description Generation and Question Answering

Licheng Yu:

Eunbyung Park:

Alex Berg:

Tamara Berg:

【Scene parsing】Box Sup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation

Jifeng Dai:

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

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

【Scene parsing】Fast Novel Visual Concept Learning from Sentence Descriptions of Images

Junhua Mao:

Xu Wei:

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

Jiang Wang:

Zhiheng Huang:

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

【Scene parsing】Object detection via a multi-region and semantic segmentation-aware CNN model

Spyros Gidaris:

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

【Scene parsing】Single Image Pop-Up from Discriminatively Learned Parts

Menglong Zhu:

Xiaowei Zhou:

Kostas Danilidiis:

【Scene parsing】Cutting Edge: Soft Correspondences in Multimodal Scene Parsing

Sarah Taghavi Namin:

Mohammad Najafi:

Mathieu Salzmann:

Lars Petersson:

【Scene parsing】Semantic Segmentation With Object Clique Potential

QI Xiaojuan:

Shu Liu:

Jianping Shi:

Renjie Liao:

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

【Scene parsing】Enhancing World Maps by Parsing Aerial Images

Gellert Mattyus:

Shenlong Wang:

Sanja Fidler:

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

【Scene parsing】ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph

Baoyuan Wu:

Siwei Lyu:

Bernard Ghanem:

【Scene parsing】Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture

David Eigen:

Rob Fergus: http://cs.nyu.edu/~fergus/

【Scene parsing】Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations

David Varas:

Ferran Marques:

【Scene parsing】Common Subspace for Model and Similarity: Phrase Learning for Sentence Generation from Images

Yoshitaka Ushiku:

Yusuke Mukuta:

Masataka Yamaguchi:

Tatsuya Harada:

【Scene parsing】Semantic Segmentation of RGBD Images with Mutex Constraints

Zhuo Deng:

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

Longin Jan Latecki:

【Scene parsing】Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation

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

Liang-Chieh Chen:

Kevin Murphy:

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

【Scene parsing】Semantic Video Entity Linking based on Visual Content and Metadata

Yuncheng Li:

Xitong Yang:

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

【Object segmentation】Trans Cut: Transparent Object Segmentation from a Light-Field Image

Yichao Xu:

Hajime Nagahara:

Atsushi Shimada:

Rin-ichiro Taniguchi:

【Object segmentation】Secrets of Grab Cut and Kernel K-means

Meng Tang:

Ismail Ben Ayed:

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

【Object segmentation】Video Matting via Sparse and Low-rank Representation

Dongqing Zou:

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

Guangying Cao:

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

【Object segmentation】Joint Object and Part Segmentation using Deep Learned Potentials

Peng Wang:

Xiaohui Shen:

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

Scott Cohen:

Brian Price:

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

【Object segmentation】Image Matting with KL-Divergence Based Sparse Sampling

Levent Karacan:

Aykut Erdem:

Erkut Erdem:

【Object segmentation】Frequency-based Environment Matting by Compressive Sensing

Yiming Qian:

Minglun Gong:

Yee Hong Yang:

【Object segmentation】Probabilistic Appearance Models for Segmentation and Classification

Julia Krüger:

Jan Ehrhardt:

Heinz Handels:

【Object segmentation】Monocular Object Instance Segmentation and Depth Ordering with CNNs

Ziyu Zhang:

Alexander Schwing:

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

Sanja Fidler:

【Object segmentation】Self-Occlusions and Disocclusions in Causal Video Object Segmentation

Yanchao Yang:

Ganesh Sundaramoorthi:

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

【Image segmentation】Piecewise Flat Embedding for Image Segmentation

Chaowei Fang:

Zicheng Liao:

Yizhou Yu: http://i.cs.hku.hk/~yzyu/

【Image segmentation】Volumetric Bias in Energy-based Segmentation: Secrets and Solutions

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

Hossam Isack:

Carl Olsson:

Ismail Ben Ayed:

【Image segmentation】Robust Image Segmentation Using Contour-guided Color Palettes

Xiang Fu:

Chien-Yi Wang:

Chen Chen:

Changhu Wang:

C.-C. Jay Kuo:

【Image segmentation】Joint Optimization of Segmentation and Color Clustering

Ekaterina Lobacheva:

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

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

【Image segmentation】An Adaptive Data Representation for Robust Point-Set Registration and Merging

Dylan Campbell:

Lars Petersson:

【Image segmentation】Learning to Boost Filamentary Structure Segmentation

Lin Gu:

LI Cheng:

【Image segmentation】A Randomized Ensemble Approach to Industrial CT Segmentation

Hyojin Kim:

Jayaraman Jayaraman Thiagarajan:

Peer-Timo Bremer:

【Image segmentation】Semi-Supervised Normalized Cuts for Image Segmentation

Selene Chew:

Nathan Cahill:

【Image segmentation】Introducing Geometry into Active Learning for Image Segmentation

Ksenia Konyushkova:

Raphael Sznitman:

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

【Image segmentation】Efficient Classifier Training for Electron Microscopy Segmentation

Toufiq Parag:

【Video segmentation】Fast and Effective L_0 Gradient Minimization by Region Fusion

Rang Man Ho Nguyen:

Michael Brown: http://www.comp.nus.edu.sg/~brown/

【Video segmentation】Context-aware diffusion for label propagation on graphs

Kwang In Kim:

James Tompkin:

Hanspeter Pfister:

Christian Theobalt:

【Video segmentation】Efficient Video Segmentation using Parametric Graph Partitioning

Chen-Ping Yu:

Hieu Le:

Greg Zelinsky:

Dimitris Samaras:

【Video segmentation】Tracking-by-Segmentation with Online Gradient Boosting Decision Tree

Jeany Son:

Ilchae Jung:

Kayoung Park:

Bohyung Han:

【Video segmentation】Simultaneous Deep Transfer Across Domains and Tasks

Eric Tzeng:

Judy Hoffman:

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

Kate Saenko:

【Video segmentation】Fully Connected Object Proposals for Video Segmentation

Federico Perazzi:

Oliver Wang:

Alexander Sorkine-Hornung:

Markus Gross:

【Video segmentation】Video Segmentation with Just a Few Strokes

Naveen Shankar Nagaraja:

Frank Schmidt:

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

【Video segmentation】Multi-Cue Structure Preserving MRF for Unconstrained Video Segmentation

Saehoon Yi:

Vladimir Pavlovic:

【Video segmentation】Motion Trajectory Segmentation via Minimum Cost Multicuts

Margret Keuper:

Bjoern Andres:

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

【Boundary detection】Holistically-Nested Edge Detection

Saining Xie:

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

【Boundary detection】Learning informative edge maps for indoor scene layout prediction

Arun Mallya:

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

【Boundary detection】High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision

Gedas Bertasius:

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

Lorenzo Torresani:

【Contour analysis】Learning Shape, Motion and Elastic Models in Force Space

Antonio Agudo:

Francesc Moreno-Noguer:

【Contour analysis】The One Triangle Three Parallelograms Sampling Strategy and Its Application in Shape Regression

Mikael Nilsson:

【Contour analysis】Body Print: Pose Invariant 3D Shape Matching of Human Bodies

Jiangping Wang:

Kai Ma:

Terrence Chen:

Vivek Singh:

Thomas Huang:

【Contour analysis】The Layered Deformation Model for Shape Matching

Yuanqi Su:

Yuehu Liu:

Bonan CUAN:

【Contour analysis】As-Rigid-As-Possible Volumetric Shape-from-Template

Shaifali PARASHAR:

Daniel Pizarro:

Adrien Bartoli:

Toby Collins:

【Contour analysis】Compositional Hierarchical Representation of Shape Manifolds for Classification of Non-manifold Shapes

Mete Ozay:

Umit Aktas:

Jeremy Wyatt:

Ales Leonardis:

【Contour analysis】Contour Detection and Characterization for Asynchronous Event Sensors

Francisco Barranco:

Ching Teo:

Cornelia Fermuller:

Yiannis Aloimonos: http://www.cfar.umd.edu/~yiannis/

【Contour analysis】Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment

Huijun Di:

Qingxuan Shi:

Feng Lv:

Ming Qin:

Yao Lu:

【Contour analysis】Stereo Snakes: Contour Based Consistent Object Extraction For Stereo Images

Ran Ju:

Tongwei Ren:

Gangshan Wu:

【Contour analysis】3D Surface Profilometry using Phase Shifting of De Bruijn Pattern

Matea Donlic:

Tomislav Petkovic:

Tomislav Pribanic:

【Object tracking】Learning to Track: Online Multi-Object Tracking by Decision Making

Yu Xiang:

Alexandre Alahi:

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

【Object tracking】Multiple Hypothesis Tracking Revisited: Blending in Modern Appearance Model

Chanho Kim:

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

Arridhana Ciptadi:

James Rehg: http://www.cc.gatech.edu/~rehg/

【Object tracking】Discriminative Low-Rank Tracking

Yao Sui:

Yafei Tang:

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

【Object tracking】SOWP: Spatially Ordered and Weighted Patch Descriptor for Visual Tracking

Han-Ul Kim:

Dae-Youn Lee:

Jae-Young Sim:

Chang-Su Kim:

【Object tracking】Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor

Wongun Choi:

【Object tracking】Multi-kernel Correlation Filter for Visual Tracking

Ming Tang:

JIayi Feng:

【Object tracking】Exploring Causal Relationships in Visual Object Tracking

Karel Lebeda:

Simon Hadfield:

Richard Bowden:

【Object tracking】A Versatile Learning-based 3D Temporal Tracker: Scalable, Robust, Online

David Joseph Tan:

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

Slobodan Ilic:

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

【Object tracking】Unsupervised Object Discovery and Tracking in Video Collections

Suha Kwak:

Minsu Cho:

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

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

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

【Object tracking】Hierarchical Convolutional Features for Visual Tracking

Chao Ma:

Jia-Bin Huang:

Xiaokang Yang:

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

【Object tracking】Robust Non-rigid Motion Tracking and Surface Reconstruction Using L0 Regularization

Kaiwen Guo:

Feng Xu:

Yangang Wang:

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

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

【Object tracking】Online Object Tracking with Proposal Selection

Yang Hua:

Karteek Alahari:

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

【Object tracking】Understanding and Diagnosing Visual Tracking Systems

Naiyan Wang:

Jianping Shi:

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

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

【Object tracking】Visual Tracking with Fully Convolutional Networks

Lijun Wang:

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

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

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

【Object tracking】Multiple Feature Fusion via Weighted Entropy for Visual Tracking

Lin Ma:

Jiwen Lu:

Jianjiang Feng:

Jie Zhou:

【Object tracking】Local Subspace Collaborative Tracking

Lin Ma:

Xiaoqin Zhang:

Weiming Hu:

Junliang Xing:

Jiwen Lu:

Jie Zhou:

【Object tracking】Learning Spatially Regularized Correlation Filters for Visual Tracking

Martin Danelljan:

Gustav Häger:

Fahad Khan:

Michael Felsberg:

【Object tracking】TRIC-track: Tracking by Regression with Incrementally Learned Cascades

Xiaomeng Wang:

Michel Valstar:

Brais Martinez:

Muhammad Khan:

Tony Pridmore:

【Object tracking】Recurrent Network Models for Kinematic Tracking

Katerina Fragkiadaki:

Panna Felsen:

Sergey Levine:

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

【Object tracking】Follow Me: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation

Philip Lenz:

Andreas Geiger:

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

【Object tracking】Learning to Divide and Conquer for Online Multi-Target Tracking

Francesco Solera:

Simone Calderara:

Rita Cucchiara:

【Object tracking】Minimizing Human Effort in Interactive Tracking by Incremental Learning of Model Parameters

Arridhana Ciptadi:

James Rehg: http://www.cc.gatech.edu/~rehg/

【Object tracking】Model-Based Tracking at 300Hz using Raw Time-of-Flight Observations

Jan Stühmer:

Sebastian Nowozin:

Andrew Fitzgibbon:

Richard Szeliski: http://research.microsoft.com/en-us/um/people/szeliski/

Travis Perry:

Sunil Acharya:

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

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

【Object tracking】Robust 3D Object Detection and Tracking from Minimal Image Information

Alberto Crivellaro:

Mahdi Rad:

Yannick Verdie:

Kwang Yi:

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

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

【Object tracking】Globally Linear Approximation to Nonlinear Learning for Visual Tracking

Bo Ma:Andrew Bagnell

Hongwei Hu:

Yuping Zhang:

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

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

【Object tracking】Dense Continuous-Time Tracking and Mapping with Rolling Shutter RGB-D Cameras

Christian Kerl:

Jörg Stückler:

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

【Action recognition】Issuing Notifications for Missing Actions: Don’t Forget to Turn the Lights Off!

Bilge Soran:

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

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

【Action recognition】Category-blind Human Action Recognition: A Practical Recognition System

Wenbo Li:

Longyin Wen:

Mooi Choo Chuah:

Siwei Lyu:

【Action recognition】Learning Temporal Embeddings for Complex Video Analysis

Vignesh Ramanathan:

Kevin Tang:

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

Fei-Fei Li:

【Action recognition】Unsupervised Synchrony Discovery in Human Interaction

Wen-Sheng Chu:

Jiabei Zeng:

Fernando De la Torre:

Jeffrefy Cohn:

Daniel Messinger:

【Action recognition】HICO: A Benchmark for Recognizing Human-Object Interactions in Images

Yu-Wei Chao:

Zhan Wang:

Yugeng He:

Jiaxuan Wang:

Jia Deng:

【Action recognition】Robust Heart Rate Measurement from Video Using Select Random Patches

Antony Lam:

Yoshinori Kuno:

【Action recognition】Flowing Conv Nets for Human Pose Estimation in Videos

Tomas Pfister:

James Charles:

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

【Action recognition】Learning to track for spatio-temporal action localization

Philippe Weinzaepfel:

Zaid Harchaoui:

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

【Action recognition】Differential Recurrent Neural Networks for Action Recognition

Vivek Veeriah:

Naifan Zhuang:

Guo-Jun Qi:

【Action recognition】Activity Auto-Completion: Predicting Human Activities from Partial Videos

Zhen Xu:

Laiyun Qing:

Jun Miao:

【Action recognition】Contextual Action Recognition with RCNN

Georgia Gkioxari:

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

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

【Action recognition】Actions and Attributes from Wholes and Parts

Georgia Gkioxari:

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

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

【Action recognition】Temporal Perception and Prediction in Ego-Centric Video

Yipin Zhou:

Tamara Berg:

【Action recognition】From Emotions to Action Units with Hidden and Semi-Hidden-Task Learning

Adria Ruiz:

Joost van de Weijer:

Xavier Binefa:

【Action recognition】Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions

Sven Bambach:

Stefan Lee:

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

Chen Yu:

【Action recognition】Describing Videos by Exploiting Temporal Structure

Li Yao:

Atousa Torabi:

Kyunghyun Cho:

Nicolas Ballas:

Christopher Pal:

Hugo Larochelle:

Aaron Courville:

【Action recognition】P-CNN: Pose-based CNN Features for Action Recognition

Guilhem Chéron:

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

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

【Action recognition】Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders

Huan Yang:

Baoyuan Wang:

Stephen Lin:

David Wipf:

Minyi Guo:

Baining Guo:

【Action recognition】Unsupervised Tube Extraction using Transductive Learning and Dense Trajectories

Mihai Puscas:

Enver Sangineto:

Dubravko Culibrk:

Nicu Sebe:

【Action recognition】Predicting Multiple Structured Visual Interpretations

Debadeepta Dey:

Varun Ramakrishna:

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

  1. Andrew Bagnell:

【Action recognition】Storyline Representation of Egocentric Videos with an Applications to Story-based Search

Bo Xiong:

Leonid Sigal:

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

【Action recognition】Learning by Playing: Learning Common Sense Via Visual Abstractions

Shanmukha Ramakrishn Vedantam:

Xiao Lin:

Tanmay Batra:

Larry Zitnick:

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

【Action recognition】Sequence to Sequence Video to Text

Subhashini Venugopalan:

Marcus Rohrbach:

Raymond Mooney:

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

Kate Saenko:

【Action recognition】Actionness-assisted Recognition of Actions

Ye Luo:

Loong-Fah Cheong:

An Tran:

【Action recognition】Automatic Concept Discovery from Parallel Text and Visual Corpora

Chen Sun:

Chuang Gan:

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

【Action recognition】Action Localization in Videos through Context Walk

Khurram Soomro:

Haroon Idrees:

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

【Action recognition】Understanding Everyday Hands in Action from RGB-D Images

Gregory Rogez:

James Supancic III:

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

【Action recognition】Context Aware Online Adaptation of Activity Recognition Models

MAHMUDUL HASAN:

Amit Roy-Chowdhury:

【Action recognition】Action Recognition by Hierarchical Mid-level Action Elements

Yuke Zhu:

Amir Zamir:

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

Tian Lan:

【Action recognition】Action Detection by Implicit Intentional Motion Clustering

Wei Chen:

Jason Corso:

【Action recognition】Objects2action: Classifying and localizing actions without any video example

Mihir Jain:

Jan van Gemert:

Thomas Mensink:

Cees Snoek:

【Action recognition】Human Action Recognition using Factorized Spatio-Temporal Convolutional Networks

LIN SUN:

Kui Jia:

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

Bertram Shi:

【Action recognition】Listening by Eyes: Towards a Practical Visual Speech Recognition System Using Deep Boltzmann Machines

Chao Sui:

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

Roberto Togneri:

【Action recognition】Human Pose Estimation in Videos

Dong Zhang:

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

【Crowd analysis】Pedestrian Travel Time Estimation in Crowded Scenes

Shuai Yi:

Hongsheng Li:

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

【Crowd analysis】Live Repetition Counting

Ofir Levy:

Lior Wolf:

【Crowd analysis】Joint Probabilistic Data Association Revisited

Seyed Hamid Rezatofighi:

Anton Milan:

Zhen Zhang:

Qinfeng Shi:

Anthony Dick:

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

【Crowd analysis】COUNT Forest: CO-voting Uncertain Number of Targets using Random Forest for Crowd Density Estimation

Viet Pham:

Osamu Yamaguchi:

Tatsuo Kozakaya:

Ryuzo Okada:

【Crowd analysis】Bayesian Model Adaptation for Crowd Counts

Bo Liu:

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

【Human detection】Learning Complexity-Aware Cascades for Deep Pedestrian Detection

Zhaowei Cai:

Mohammad Saberian:

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

【Human detection】Partial Person Re-identification

Wei-Shi Zheng:

Xiang Li:

Tao Xiang:

Shengcai Liao:

Jianhuang Lai:

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

【Human detection】Confidence Preserving Machine for Facial Action Unit Detection

Jiabei Zeng:

Wen-Sheng Chu:

Fernando De la Torre:

Jeffrefy Cohn:

Zhang Xiong:

【Human detection】Deep Learning Strong Parts for Pedestrian Detection

Yonglong Tian:

【Human detection】Person Re-identification with Correspondence Structure Learning

Yang Shen:

Weiyao Lin:

Junchi Yan:

Mingliang Xu:

Jianxin Wu:

Jingdong Wang:

【Human detection】Convolutional Channel Features For Pedestrian, Face and Edge Detection

Bin Yang:

Junjie Yan:

Zhen Lei:

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

【Human detection】Efficient PSD Constrained Asymmetric Metric Learning for Person Re-identification

Shengcai Liao:

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

【Human detection】Context-aware CNNs for person detection

Tuan-Hung Vu:

Anton Osokin:

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

【Human detection】Scalable Person Re-identification: A Benchmark

Liang Zheng:

Shengjin Wang:

Jingdong Wang:

Liyue Shen:

Jiahao Bu:

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

【Human detection】Highly articulated human detection using a Markov Random Field model of poselets and variational method

Thanh Nguyen:

Khoi Tran:

Sai-Kit Yeung:

【Human detection】Multi-Task Learning with Low Rank Attribute Embedding for Person Re-identification

Chi Su:

Fan Yang:

Shiliang Zhang:

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

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

Wen Gao: http://www.jdl.ac.cn/

【Human detection】Multi-scale Learning for Low-resolution Person Re-identification

Xiang Li:

Wei-Shi Zheng:

Xiaojuan Wang:

Tao Xiang:

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

【Human detection】Person Re-Identification with Discriminatively Trained Viewpoint Invariant Dictionaries

Srikrishna Karanam:

Yang Li:

Richard Radke:

【Human detection】A SPATIO-TEMPORAL APPEARANCE REPRESENTATION FOR VIDEO-BASED PEDESTRIAN RE-IDENTIFICATION

Kan Liu:

Bingpeng Ma:

Wei Zhang:

Rui Huang:

【Human detection】Person Recognition in Personal Photo Collections

Seong Joon Oh:

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

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

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

【Human detection】Person Re-Identification Ranking Optimisation by Discriminant Context Information Analysis

Jorge García:

Niki Martinel:

christian Micheloni:

Alfredo Gardel:

【Human parsing】Human Parsing with Contextualized Convolutional Neural Network

Xiaodan Liang:

Chunyan Xu:

Xiaohui Shen:

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

Liu Si:

jinhui Tang:

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

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

【Human parsing】Where to Buy It: Matching Street Clothing Photos in Online Shops

Mohammadhadi Kiapour:

Xufeng Han:

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

Alex Berg:

Tamara Berg:

【Human parsing】Training a Feedback Loop for Hand Pose Estimation

Markus Oberweger:

Paul Wohlhart:

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

【Human parsing】Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose

Danhang Tang:

Jonathan Taylor:

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

Cem Keskin:

Tae-Kyun Kim:

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

【Human parsing】Uncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-formations from Surveillance Videos

Elisa Ricci:

Jagannadan Varadarajan:

Ramanathan Subramanian:

Samuel Rota Bulò:

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

Oswald Lanz:

【Human parsing】Attributed Grammars for Joint Estimation of Human Attributes, Part and Pose

Seyoung Park:

Song-Chun Zhu:

【Human parsing】Temporal Subspace Clustering for Human Motion Segmentation

Sheng Li:

Kang Li:

Yun Fu:

【Human parsing】Depth-based hand pose estimation: data, methods, and challenges

James Supancic III:

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

Gregory Rogez:

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

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

【Human parsing】3D Hand Pose Estimation Using Randomized Decision Forest with Segmentation Index Points

PEIYI LI:

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

【Human parsing】Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation

Sijin Li:

Weichen Zhang:

Antoni Chan:

【Human parsing】Semi- and Weakly-supervised Human Pose Estimation

Norimichi Ukita:

【Human parsing】Beyond Tree Structure Models: A New Occlusion Aware Graphical Model for Human Pose Estimation

Lianrui Fu:

Junge Zhang:

Kaiqi Huang:

【Human parsing】Learning Visual Clothing Style with Heterogeneous Dyads from Co-occurrence

Andreas Veit:

Balazs Kovacs:

Sean Bell:

Julian Mc Auley:

Kavita Bala:

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

【Human parsing】A collaborative filtering approach to real-time hand pose estimation

Chiho Choi:

Ayan Sinha:

Joon Hee Choi:

Sujin Jang:

Karthik Ramani:

【Face recognition】Face Flow

Patrick Snape:

Anastasios Roussos:

Yannis Panagakis:

Stefanos Zafeiriou:

【Face recognition】Selective Encoding for Recognizing Unreliably Localized Faces

Ang Li:

Vlad Morariu:

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

【Face recognition】Conditional Convolutional Neural Network for Modality-aware Face Recognition

Chao Xiong:

Xiaowei Zhao:

Danhang Tang:

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

Tae-Kyun Kim:

【Face recognition】Simultaneous Binary Feature Learning and Encoding for Face Recognition

Jiwen Lu:

Venice Erin Liong:

Jie Zhou:

【Face recognition】Deep Temporal Appearance-Geometry Network for Facial Expression Recognition

Heechul Jung:

Sihaeng Lee:

JUNHO YIM:

Sunjeong Park:

Junmo Kim:

【Face detection】From Facial Part Responses to Face Detection: A Deep Learning Approach

Shuo Yang:

Ping Luo:

Chen-Change Loy:

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

【Face detection】Visual Phrases for Exemplar Face Detection

Vijay Kumar Reddy:

Anoop Namboodiri:

C.V. Jawahar:

【Face parsing】Robust Model-based 3D Head Pose Estimation

Gregory Meyer:

Shalini Gupta:

Iuri Frosio:

Dikpal Reddy:

Jan Kautz:

【Face parsing】Robust Facial Landmark Detection under Significant Head Poses and Occlusion

Yue Wu:

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

【Face parsing】Pose-Invariant 3D Face Alignment

Amin Jourabloo:

Xiaoming Liu:

【Face parsing】Fast and Accurate Head Pose Estimation via Random Projection Forests

Donghoon Lee:

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

Songhwai Oh:

【Face parsing】Deep Learning Face Attributes in the Wild

Ziwei Liu:

Ping Luo:

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

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

【Face parsing】Regressing a 3D Face Shape from a Single Image

Sergey Tulyakov:

Nicu Sebe:

【Face parsing】Face Director: Continuous Control of Facial Performance in Video

Charles Malleson:

Jean-Charles Bazin:

Oliver Wang:

Thabo Beeler:

Derek Bradley:

Adrian Hilton:

Alexander Sorkine-Hornung:

【Face parsing】Learning to transfer: transferring latent task structures and its application to person-specific facial action unit detection

Timur Almaev:

Brais Martinez:

Michel Valstar:

【Face parsing】Pairwise Conditional Random Forests for Facial Expression Recognition

Arnaud Dapogny:

Kevin Bailly:

Severine Dubuisson:

【Face parsing】Joint Facial Action Unit Detection with Multi-conditional Latent Variable Models

Stefanos Eleftheriadis:

Ognjen Rudovic:

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

【Face parsing】Leveraging Datasets with Varying Annotations for Face Alignment via Deep Regression Network

Jie Zhang:

Meina Kan:

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

Xilin Chen:

【Face parsing】Two Birds, One Stone: Jointly Learning Binary Code for Large-scale Face Image Retrieval and Attributes Prediction

Yan Li:

Ruiping Wang:

Haomiao Liu:

Huajie Jiang:

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

Xilin Chen:

【Face parsing】Regressive Tree Structured Model for Facial Landmark Localization

Gee-Sern Hsu:

Kai-Hsiang Chang:

Shih-Chieh Huang:

【Face parsing】Robust Statistical Face Frontalization

Christos Sagonas:

Yannis Panagakis:

Stefanos Zafeiriou:

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

【Face parsing】PIEFA: Personalized Incremental and Ensemble Face Alignment in the Wild

Xi Peng:

Yu Yang:

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

Dimitris Metaxas:

【Face parsing】Example-Based Modeling of Facial Texture from Deficient Data

Arnaud Dessein:

William Smith:

Richard Wilson:

Edwin Hancock:

【Object recognition】Discovering the Spatial Extent of Relative Attributes

Fanyi Xiao:

Yong Jae Lee:

【Object recognition】Bilinear CNN Models for Fine-grained Visual Recognition

Tsung-Yu Lin:

Aruni Roy Chowdhury:

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

【Object recognition】Weakly Supervised Learning of Part Detectors for Visual Recognition

Thibaut Durand:

Nicolas Thome:

Matthieu Cord:

【Object recognition】Multiple Granularity Descriptors for Fine-grained Categorization

Dequan Wang:

Zhiqiang Shen:

Jie Shao:

Wei Zhang:

Xiangyang Xue:

Zheng Zhang:

【Object recognition】Pose Induction for Novel Object Categories

Shubham Tulsiani:

Joao Carreira:

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

【Object recognition】Multi-class Multi-annotator Active Learning with Robust Gaussian Process for Visual Recognition

Chengjiang Long:

Gang Hua:

【Object recognition】Learning to See by Moving

Pulkit Agrawal:

Joao Carreira:

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

【Object recognition】Scene-Domain Active Part Models for Object Representation

Zhou Ren:

Chaohui Wang:

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

【Object recognition】Augmenting Strong Supervision Using Web Data for Fine-grained Categorization

Zhe Xu:

SJTU:

Shaoli Huang:

Ya Zhang:

Dacheng Tao:

【Object recognition】Multi-modal Sharable and Specific Feature Learning for RGB-D Object Recognition

Anran Wang:

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

Jiwen Lu:

Tat-Jen Cham:

【Object recognition】Unsupervised Generation of a Viewpoint Annotated Car Dataset from Videos

Nima Sedaghat Alvar:

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

【Object recognition】Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks

Marcel Simon:

Erik Rodner:

【Object recognition】Multi-scale recognition with DAG-CNNs

Songfan Yang:

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

【Object recognition】Relaxed Multiple-Instance SVM with Application to Object Discovery

Xinggang Wang:

Zhuotun Zhu:

Cong Yao:

Xiang Bai:

【Object recognition】Deep Boxes: Hunting Objects by Cascading Deep Convolutional Layers

Amir Ghodrati:

Marco Pedersoli:

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

Ali Diba:

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

【Object recognition】Im2Calories: towards an automated mobile vision food diary

Alex Gorban:

Nick Johnston:

Anoop Korattikara:

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

Vivek Rathod:

Kevin Murphy:

Sergio Guadarrama:

【Object recognition】Multi-view convolutional neural networks for 3D shape recognition

Hang Su:

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

Erik Miller:

Evangelos Kalogerakis:

【Object recognition】Query Adaptive Similarity Measure for RGB-D Object Recognition

Yanhua Cheng:

CASIA:

Rui Cai:

Zhiwei Li:

Xin Zhao:

Kaiqi Huang:

Yong Rui:

【Object recognition】Multi-view Domain Generalization for Visual Recognition

Li Niu:

Wen Li:

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

【Object detection】Object Detection Using Generalization and Efficiency Balanced Co-occurrence Features

Haoyu Ren:

Ze-Nian Li:

【Object detection】Towards Computational Baby Learning: A Weakly-supervised Approach for Object Detection

Xiaodan Liang:

Si Liu:

Yunchao Wei:

Luoqi Liu:

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

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

【Object detection】Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection

David Novotny:

Jiri Matas:

【Object detection】Domain Generalization for Object Recognition with Multi-task Autoencoders

Muhammad Ghifary:

  1. Bastiaan Kleijn:

Mengjie Zhang:

David Balduzzi:

【Object detection】Efficient Square Localization for Object Detection

Cewu Lu:

Yongyi Lu:

Hao Chen:

Chi Keung Tang:

【Object detection】Box Aggregation for Proposal Decimation: Last Mile of Object Detection

Shu Liu:

Cewu Lu:

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

【Object detection】Spatial Semantic Regularisation for Large Scale Object Detection

Damian Mrowca:

Marcus Rohrbach:

Judy Hoffman:

Ronghang Hu:

Kate Saenko:

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

【Object detection】Structural Kernel Learning for Large Scale Multiclass Object Co-Detection

Zeeshan Hayder:

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

Mathieu Salzmann:

【Object detection】Attention Net: Aggregating Weak Directions for Accurate Object Detection

Donggeun Yoo:

Sunggyun Park:

Joon-Young Lee:

Anthony Paek:

In So Kweon: http://rcv.kaist.ac.kr/

【Object detection】Simultaneous foreground detection and classification with hybrid features

Jaemyun Kim:

Adin Ramirez Rivera:

Byungyong Ryu:

Oksam Chae:

【Object detection】Learning Deep Object Detectors from 3D Models

Xingchao Peng:

Kate Saenko:

Baochen Sun:

Karim Ali: http://www.karimali.org/

【Saliency detection】Minimum Barrier Salient Object Detection at 80 FPS

Jianming Zhang:

Stan Sclaroff:

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

Xiaohui Shen:

Brian Price:

Radomir Mech:

【Saliency detection】A Data-driven Metric for Comprehensive Evaluation of Saliency Models

Jia Li:

Changqun Xia:

Yafei Song:

Shu Fang:

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

【Saliency detection】Understanding and Predicting Memorability at a Large-scale

Aditya Khosla:

Akhil Raju:

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

Aude Oliva:

【Saliency detection】Generic Promotion of Diffusion-Based Salient Object Detection

Peng Jiang:

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

Jingliang Peng:

【Saliency detection】Just Noticeable Differences in Visual Attributes

Aron Yu:

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

【Saliency detection】SALICON: Bridging the Semantic Gap in Saliency Prediction

Xun Huang:

Chengyao Shen:

Xavier Boix:

Qi Zhao:

【Saliency detection】Oriented Object Proposals

Shengfeng He:

Rynson Lau:

【Saliency detection】Boosting Object Proposals: From Pascal to COCO

Jordi Pont-Tuset:

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

【Saliency detection】HARF: Hierarchy-associated Rich Features for Salient Object Detection

Wenbin Zou:

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

【Saliency detection】The Middle Child Problem: Revisiting Parametric Min-cut for Fast Object Proposals

Ahmad Humayun:

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

James Rehg: http://www.cc.gatech.edu/~rehg/

【Saliency detection】Deep Box: Learning Objectness with Convolutional Networks

WEICHENG KUO:

Bharath Hariharan:

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

【Saliency detection】Semantic Guidance of Visual Attention for Localizing Objects in Scenes

Juan Caicedo:

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

【Saliency detection】What makes an object memorable?

Rachit Dubey:

Joshua Peterson:

Aditya Khosla:

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

Bernard Ghanem:

【Saliency detection】Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks

Chunshui Cao:

Xianming Liu:

Jiang Wang:

Yinan Yu:

Wei Xu:

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

【Saliency detection】Structured Learning for Generating Region Proposals with Mid-level Cues

Tom Lee:

Sanja Fidler:

Sven Dickinson:

【Saliency detection】A Self-paced Multiple-instance Learning Framework for Co-saliency Detection

Dingwen Zhang:

Deyu Meng:

Chao Li:

Lu Jiang:

Qian Zhao:

Junwei Han:

【Saliency detection】Learning to Predict Saliency on Face Images

Mai Xu:

Yun Ren:

【Saliency detection】Cluster-based point set saliency

Flora Ponjou Tasse:

Jiri Kosinka:

Neil Dodgson:

【Saliency detection】Contour Box: Rejecting Object Proposals Without Explicit Closed Contours

Cewu Lu:

Chi Keung Tang:

【Scene recognition】Learning image representations equivariant to ego-motion

Dinesh Jayaraman:

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

【Scene recognition】Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

Yukun Zhu:

Ryan Kiros:

Rich Zemel:

Ruslan salakhutdinov:

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

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

Sanja Fidler:

【Scene recognition】Unsupervised Visual Representation Learning by Context Prediction

Carl Doersch:

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

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

【Scene recognition】Weakly-Supervised Alignment of Video With Text

Piotr Bojanowski:

Rémi Lajugie:

Edouard Grave:

Francis Bach:

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

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

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

【Scene recognition】HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition

Zhicheng Yan:

Hao Zhang:

Robinson Piramuthu:

Vignesh Jagadeesh:

Dennis De Coste:

Wei Di:

Yizhou Yu: http://i.cs.hku.hk/~yzyu/

【Scene recognition】Guided Long-Short Term Memory for Image Caption Generation

Xu Jia:

Stratis Gavves:

Basura Fernando:

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

【Scene recognition】Automatic Thumbnail Generation Based on Visual Representativeness and Foreground Recognizability

Jingwei Huang:

Huarong Chen:

Bin Wang:

Stephen Lin:

【Scene recognition】Improving Image Classification with Location Context

Kevin Tang:

Manohar Paluri:

Fei-Fei Li:

Rob Fergus: http://cs.nyu.edu/~fergus/

Lubomir Bourdev:

【Scene recognition】Delving Deep into Rectifiers: Surpassing Human-Level Performance on Image Net Classification

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

Xiangyu Zhang:

Shaoqing Ren:

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

【Scene recognition】Unsupervised Learning of Visual Representations using Videos

Xiaolong Wang:

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

【Scene recognition】A Unified Multiplicative Framework for Attribute Learning

Kongming Liang:

Hong Chang:

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

Xilin Chen:

【Scene recognition】Task-Driven Feature Pooling for Image Classification

Guo-Sen Xie:

Xu-Yao Zhang:

Xiangbo Shu:

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

Cheng-Lin Liu:

【Scene recognition】Relaxing from Vocabulary: Robust Weakly-Supervised Deep Learning for Vocabulary-Free Image Tagging

Jianlong Fu:

Yue Wu:

Tao Mei:

Jinqiao Wang:

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

Yong Rui:

【Scene recognition】Multimodal Convolutional Neural Networks for Matching Image and Sentence

Lin Ma:

Zhengdong Lu:

Lifeng Shang:

Hang Li:

【Scene recognition】Per-Sample Kernel Adaptation for Visual Recognition and Grouping

Borislav Antic:

Bjorn Ommer:

【Scene recognition】3D Indoor Scene Labelling with Hierarchical Higher-order Regression Forest Fields

Trung Pham:

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

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

Yasir Latif:

【Scene recognition】Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification

Ruobing Wu:

Baoyuan Wang:

Yizhou Yu: http://i.cs.hku.hk/~yzyu/

【Text recognition】Extraction of Illusory Baselines from Distorted Document Images Using Curvilinear Projection

Gaofeng MENG:

Zuming HUANG:

Yonghong SONG:

Shiming XIANG:

Chunhong PAN:

【Text recognition】Dynamic Texture Recognition via Structured Tensor Dictionary Learning

YUHUI QUAN:

Yan Huang:

Hui Ji:

【Text recognition】FASTex: Efficient Unconstrained Scene Text Detector

Michal Busta:

Lukas Neumann:

Jiri Matas:

【Text recognition】Text Flow: A Unified Text Detection System in Natural Scene Images

Shangxuan Tian:

Yifeng Pan:

Chang Huang:

Shijian Lu:

Kai Yu: http://idl.baidu.com/en/index.html

Chew Lim Tan:

【Image retrieval】Web-scale image clustering revisited

Yannis Avrithis:

Yannis Kalantidis:

Evangelos Anagnostopoulos:

Ioannis Emiris:

【Image retrieval】Love Thy Neighbors: Image Annotation by Exploiting Social Metadata

Lamberto Ballan:

Justin Johnson:

Fei-Fei Li:

【Image retrieval】Learning Deep Representation with Large-scale Attributes

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

Hongyang Li:

Xingyu Zeng:

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

【Image retrieval】Learning Social Relation Traits from Face Images

Zhanpeng Zhang:

Ping Luo:

Chen-Change Loy:

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

【Image retrieval】Top Rank Supervised Binary Coding for Visual Search

Dongjin Song:

Rongrong Ji:

David Meyer:

John Smith:

Wei Liu:

【Image retrieval】Adaptive Hashing for Fast Similarity Search

Fatih Cakir:

Stan Sclaroff:

【Image retrieval】Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network

Junshi Huang:

Rogerio Feris: http://rogerioferis.com/

Qiang Chen:

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

【Image retrieval】Attribute-Graph: A Graph based approach to Image Ranking

Nikita Prabhu:

Venkatesh Babu Radhakrishnan:

【Image retrieval】Local Convolutional Features with Unsupervised Training for Image Retrieval

Mattis Paulin:

Matthijs Douze:

Zaid Harchaoui:

Julien Mairal:

Florent Perronin:

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

【Image retrieval】PQTable: Fast Exact Asymmetric Distance Neighbor Search for Product Quantization using Hash Tables

Yusuke Matsui:

Toshihiko Yamasaki:

Kiyoharu Aizawa:

【Image retrieval】Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face

Jungseock Joo:

Francis Steen:

Song-Chun Zhu:

【Image retrieval】k NN Hashing with Factorized Neighborhood Representation

Kun Ding:

Chunlei Huo:

Bin Fan:

Chunhong PAN:

【Image retrieval】Multi-View Complementary Hash Tables for Nearest Neighbor Search

Xianglong Liu:

Lei Huang:

Cheng Deng:

Jiwen Lu:

Bo Lang:

【Image retrieval】Multi-Label Cross-modal Retrieval

Viresh Ranjan:

Nikhil Rasiwasia:

C.V. Jawahar:

【Image retrieval】Guaranteed Outlier Removal for Rotation Search

Alvaro Parra:

Tat-Jun Chin:

【Image retrieval】Exploiting Object Similarity in Large-Scale 3D Reconstruction

Chen Zhou:

Fatma Güney:

Yizhou Wang:

Andreas Geiger:

【Image retrieval】Learning Binary Codes for Maximum Inner Product Search

Fumin Shen:

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

Heng Tao Shen:

Wei Liu:

【Image retrieval】An NMF perspective on Binary Hashing

Lopamudra Mukherjee:

Sathya Ravi:

Vamsi Ithapu:

【Image retrieval】Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models

Bryan Plummer:

Liwei Wang:

Chris Cervantes:

Juan Caicedo:

Julia Hockenmaier:

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

【Image retrieval】Selecting Relevant Web Trained Concepts for Automated Event Retrieval

Bharat Singh:

Xintong Han:

Zhe Wu:

Vlad Morariu:

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

【Image retrieval】Aggregating local deep features for image retrieval

Artem Babenko:

Victor Lempitsky:

【Image retrieval】Scalable Nonlinear Embeddings for Semantic Category-based Image Retrieval

Gaurav Sharma:

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

【3D modeling】3D Time-Lapse Reconstruction from Internet Photos

Ricardo Martin:

Steve Seitz:

David Gallup:

【3D modeling】Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views

Hao Su:

Charles R. Qi:

Leonidas Guibas:

Yangyan Li:

【3D modeling】Structured Indoor Modeling

Yasutaka Furukawa:

Hang Yang:

Satoshi Ikehata:

【3D modeling】On the Visibility of Point Clouds

Sagi Katz:

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

【3D modeling】Global, Dense Multiscale Reconstruction for a Billion Points

Benjamin Ummenhofer:

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

【3D modeling】Airborne Three-Dimensional Cloud Tomography

Aviad Levis:

Amit Aides:

Yoav Schechner:

Anthony Davis:

【3D modeling】3D-Assisted Image Feature Synthesis for Novel Views of an Object

Hao Su:

Fan Wang:

Li Yi:

Leonidas Guibas:

【3D modeling】3D Object Reconstruction from Hand-Object Interactions

Dimitrios Tzionas:

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

【3D modeling】Minimal Solvers for 3D Geometry from Satellite Imagery

Enliang Zheng:

Ke Wang:

Enrique Dunn:

Jan-Michael Frahm:

【3D modeling】Sparse Dynamic 3D Reconstruction from Unsynchronized Videos

Enliang Zheng:

Dinghuang Ji:

Enrique Dunn:

Jan-Michael Frahm:

【3D modeling】Resolving Scale Ambiguity Via XSlit Aspect Ratio Analysis

Wei Yang:

Haiting Lin:

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

Jingyi Yu:

【3D modeling】Single-shot Specular Surface Reconstruction with Gonio-plenoptic Imaging

Lingfei Meng:

Liyang Lu:

Noah Berdard:

Kathrin Berkner:

【3D modeling】Point triangulation through polyhedron collapse using the L-infinity norm

Simon Donné:

Bart Goossens:

Wilfried Philips:

【3D modeling】Distilling the Visual World: Foveated imaging for visual discovery

Kevin Matzen:

Noah Snavely:

【3D modeling】Intrinsic Scene Decomposition from RGB-D images

Mohammed Hachama:

Bernard Ghanem:

Peter Wonka:

【3D modeling】Single Image 3D Without a Single 3D Image

David Fouhey:

Muhammad Wajahat Hussain:

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

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

【3D modeling】Photogeometric Scene Flow for High-Detail Dynamic 3D Reconstruction

Paulo Gotardo:

Tomas Simon:

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

Iain Matthews:

【3D modeling】Variational Patch Match Multi View Reconstruction and Refinement

Philipp Heise:

Brian Jensen:

Sebastian Klose:

Alois Knoll:

【3D modeling】General Dynamic Scene Reconstruction from Multiple View Video

Armin Mustafa:

Jean-Yves Guillemaut:

Hansung Kim:

Adrian Hilton:

【3D modeling】Dense, Direct and Deformable: Non-Rigid 3D Reconstruction from RGB Video

Rui Yu:

Chris Russell:

Neill Campbell:

Lourdes Agapito:

【3D modeling】Procedural Editing of Building Point Clouds

Ilke Demir:

Daniel Aliaga:

Bedrich Benes:

【3D modeling】Semantically-Aware Aerial Reconstruction from Multi-Modal Data

Randi Cabezas:

Julian Straub:

John Fisher III:

【3D modeling】Deformable 3D Fusion: From Partial Dynamic 3D Observations to Complete 4D Models

Weipeng Xu:

Mathieu Salzmann:

Yongtian Wang:

Yue Liu:

【3D modeling】Building Dynamic Cloud Maps from the Ground Up

Calvin Murdock:

Nathan Jacobs:

Robert Pless:

【3D modeling】Interactive Visual Hull Refinement for Specular and Transparent Object Surface Reconstruction

Xinxin Zuo:

University of Kentucky:

Chao Du:

Sen Wang:

Jiangbin Zheng:

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

【3D modeling】Learning where to position parts in 3D

Marco Pedersoli:

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

【3D modeling】Dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture

Dat Tien Ngo:

Sanghyuk Park:

Anne Jorstad:

Alberto Crivellaro:

Chang Yoo:

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

【3D modeling】Detailed Full-Body Reconstructions of Moving People from Monocular RGB-D Sequences

Federica Bogo:

Javier Romero:

Matthew Loper:

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

【3D modeling】Component-wise modeling of articulated objects

Valsamis Ntouskos:

Marta Sanzari:

Bruno Cafaro:

Federico Nardi:

Fabrizio Natola:

Fiora Pirri:

Manuel Ruiz Garcia:

【Feature matching】Learning Spatiotemporal Features with 3D Convolutional Networks

Du Tran:

Lubomir Bourdev:

Rob Fergus: http://cs.nyu.edu/~fergus/

Lorenzo Torresani:

Manohar Paluri:

【Feature matching】Understanding deep features with computer-generated imagery

Mathieu Aubry:

Bryan Russell:

【Feature matching】Low Dimensional Explicit Feature Maps

Ondrej Chum:

【Feature matching】RIDE: Reversal Invariant Descriptor Enhancement

Lingxi Xie:

Jingdong Wang:

Weiyao Lin:

Bo Zhang:

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

【Feature matching】Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD

Hyo Jin Kim:

Enrique Dunn:

Jan-Michael Frahm:

【Feature matching】Deep Convolutional Feature Point Descriptors

Edgar Simo-Serra:

Eduard Trulls:

Luis Ferraz:

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

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

Francesc Moreno-Noguer:

【Feature matching】Multiple-hypotheses affine region estimation by anisotropic Lo G filter

Takahiro Hasegawa:

Mitsuru Ambai:

Gou Koutaki:

Kohta Ishikawa:

Yuji Yamauchi:

Takayoshi Yamashita:

Hironobu Fujiyoshi:

【Feature matching】Beyond Covariance: Feature Representation with Nonlinear Kernel matrices

Lei Wang:

Jianjia Zhang:

Luping Zhou:

Chang Tang:

Wanqing Li:

【Feature matching】Infinite Feature Selection

Giorgio Roffo:

Simone Melzi:

Marco Cristani:

【Feature matching】Learning a Descriptor-specific 3D Keypoint Detector

Samuele Salti:

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

Riccardo Spezialetti:

Luigi Di Stefano:

【Feature matching】A Supervised Low-rank Method for Learning Invariant Subspaces

Gianfranco Doretto:

【Feature matching】Structured Feature Selection

Tian Gao:

Ziheng Wang:

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

【Feature matching】Learning Image and User Features for Recommendation in Social Networks

Xue Geng:

Hanwang Zhang:

Jingwen Bian:

Tag-Seng Chua:

【Pose estimation】Camera Pose Voting for Large-Scale Image-Based Localization

Bernhard Zeisl:

Torsten Sattler:

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

【Pose estimation】Lost Shopping! Monocular Localization in Large Indoor Spaces

Shenlong Wang:

Sanja Fidler:

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

【Pose estimation】Real-Time Pose Estimation Piggybacked on Object Detection

Roman Juranek:

Marketa Dubska:

Adam Herout:

Pavel Zemcik:

【Pose estimation】On Linear Structure from Motion for Light Field Cameras

Ole Johannsen:

Antonin Sulc:

Bastian Goldluecke:

【Pose estimation】Learning Affordance for Direct Perception in Autonomous Driving

Chenyi Chen:

Ari Seff:

Alain Kornhauser:

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

【Pose estimation】Structure from Motion Using Structure-less Resection

Enliang Zheng:

Changchang Wu:

【Pose estimation】An Efficient Minimal Solution for Multi-Camera Motion

Jonathan Ventura:

Clemens Arth:

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

【Pose estimation】Robust RGB-D Odometry Using Point and Line Features

Yan Lu:

Dezhen Song:

【Pose estimation】A Versatile Scene Model with Differentiable Visibility Applied to Generative Pose Estimation

Helge Rhodin:

Nadia Robertini:

Christian Richardt:

Hans-Peter Seidel:

Christian Theobalt:

【Pose estimation】Semantic Pose using Deep Networks Trained on Synthetic RGB-D

Jeremie Papon:

Markus Schoeler:

【Pose estimation】Localize me Anywhere, Anytime: A Multi-task Point-Retrieval Approach

Guoyu Lu:

Yan Yan:

Li Ren:

Jingkuan Song:

Nicu Sebe:

Chandra Kambhamettu:

【Pose estimation】Continuous Pose Estimation with a Spatial Ensemble of Fisher Regressors

Michele Fenzi:

Laura Leal-Taixe:

Joern Ostermann:

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

【Pose estimation】Optimizing the Viewing Graph for Structure-from-Motion

Chris Sweeney:

Torsten Sattler:

Matthew Turk:

Tobias Hollerer:

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

【Pose estimation】Wide-Area Image Geolocalization with Aerial Reference Imagery

Scott Workman:

Richard Souvenir:

Nathan Jacobs:

【Pose estimation】Accurate Camera Calibration Robust to Defocus using a Smartphone

Hyowon Ha:

Yunsu Bok:

Kyungdon Joo:

Jiyoung Jung:

In So Kweon: http://rcv.kaist.ac.kr/

【Pose estimation】High Quality Structure from Small Motion for Rolling Shutter Cameras

Sunghoon Im:

Hyowon Ha:

Gyeongmin Choe:

Hae-Gon Jeon:

Kyungdon Joo:

In So Kweon: http://rcv.kaist.ac.kr/

【Pose estimation】Global Structure-from-Motion by Similarity Averaging

Zhaopeng Cui:

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

【Pose estimation】Convolutional networks for real-time 6-DOF camera relocalization

Alex Kendall:

Matthew Grimes:

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

【Pose estimation】Integrating Dashcam Views through Inter-Video Mapping

Hsin-Yi Chen:

Bing-Yu Chen:

Yi-Ling Chen:

Wei-Tse Lee:

Fan Wang:

【Pose estimation】Hyperpoints and Fine Vocabularies for Large-Scale Location Recognition

Torsten Sattler:

Michal Havlena:

Filip Radenovic:

Konrad Schindler:

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

【Pose estimation】See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOG

Wei-Chen Chiu:

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

【Pose estimation】Merging the Unmatchable: Stitching Visually Disconnected Sf M Models

Andrea Cohen:

Torsten Sattler:

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

【Pose estimation】Non-Parametric Structure-Based Calibration of Radially Symmetric Cameras

Federico Camposeco Paulsen:

Torsten Sattler:

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

【Pose estimation】Realtime edge-based visual odometry for a monocular camera

Juan Tarrio:

Sol Pedre:

【Pose estimation】RGB-W: When Vision Meets Wireless

Alexandre Alahi:

Albert Haque:

Fei-Fei Li:

【Pose estimation】Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images

Alexander Krull:

Eric Brachmann:

Frank Michel:

Michael Ying Yang:

Stefan Gumhold:

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

【Pose estimation】Co-interest of Multiple Wearable Cameras

Yuewei Lin:

Kareem Abdelfatah:

Youjie Zhou:

xiaochuan Fan:

Hongkai Yu:

Hui Qian:

Song Wang:

【Pose estimation】Efficient Solution to the Relative Pose Problem for Radially Distorted Cameras

Zuzana Kukelova:

Jan Heller:

Martin Bujnak:

Andrew Fitzgibbon:

Tomas Pajdla:

【Pose estimation】On the Equivalence of Moving Entrance Pupil and Radial Distortion for Camera Calibration

Avinash Kumar:

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

【Pose estimation】A Linear Generalized Camera Calibration from Three Intersecting Reference Planes

Mai Nishimura:

Shohei Nobuhara:

Takashi Matsuyama:

【Pose estimation】Towards Pointless Structure from Motion: 3D reconstruction and camera calibration from general 3D curves

Irina Nurutdinova:

Andrew Fitzgibbon:

【Stereo matching】Polarized 3D: High-Quality Depth Sensing with Polarization Cues

Achuta Kadambi:

Vage Taamazyan:

Boxin Shi:

Ramesh Raskar:

【Stereo matching】Mesh Stereo: A Global Stereo Model with Mesh Alignment Regularization for View Interpolation

Chi Zhang:

Zhiwei Li:

Hongyang CHAO:

Rui Cai:

【Stereo matching】Photometric Stereo in a Scattering Medium

Zak Murez:

Ravi Ramamoorthi:

Tali Treibitz:

David Kriegman:

【Stereo matching】Joint Camera Clustering and Surface Segmentation for Large-scale Multi-view Stereo

Runze Zhang:

Shiwei Li:

Tian Fang:

Siyu Zhu:

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

【Stereo matching】Depth Recovery from Light Field Using Focal Stack Symmetry

Haiting Lin:

Can Chen:

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

Jingyi Yu:

【Stereo matching】Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution

Williem Williem:

In Kyu Park:

【Stereo matching】Exploiting high level scene cues in stereo reconstruction

Simon Hadfield:

Richard Bowden:

【Stereo matching】Dense Optical Flow Prediction from a Static Image

Jacob Walker:

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

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

【Stereo matching】Photometric Stereo with Small Angular Variations

Jian Wang:

Yasuyuki Matsushita:

Boxin Shi:

Aswin Sankaranarayanan:

【Stereo matching】Occlusion-aware depth estimation using light-field cameras

Ting-Chun Wang:

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

Ravi Ramamoorthi:

【Stereo matching】Extended Depth of Field Catadioptric Imaging Using Focal Sweep

Ryunosuke Yokoya:

Shree Nayar:

【Stereo matching】Intrinsic Depth: Improving Depth Transfer with Intrinsic Images

Naejin Kong:

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

【Stereo matching】Gipuma: Massively parallel multiview stereopsis

Silvano Galliani:

Katrin Lasinger:

Konrad Schindler:

【Stereo matching】Beyond RMS: Geometry-Aware Performance Evaluation of Stereo Algorithms

Katrin Honauer:

Lena Maier-Hein:

Daniel Kondermann:

【Stereo matching】MAP Disparity Estimation using Hidden Markov Trees

Eric Psota:

Jedrzej Kowalczuk:

Mateusz Mittek:

Lance Pérez:

【Stereo matching】Wide Baseline Stereo Matching with Convex Bounded-Distortion Constraints

Meirav Galun:

Tal Amir:

Tal Hassner:

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

Yaron Lipman:

【Stereo matching】Reflection Modeling for Passive Stereo

Rahul Nair:

Andrew Fitzgibbon:

Daniel Kondermann:

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

【Stereo matching】A Deep Visual Correspondence Embedding Model for Stereo Matching Costs

Zhuoyuan Chen:

Xun Sun:

Yinan Yu:

Wang Liang:

Chang Huang:

Haoyuan Gao:

Kai Yu: http://idl.baidu.com/en/index.html

【Optical flow】Flow Fields: Dense Unregularized Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation

Christian Bailer:

Bertram Taetz:

Didier Stricker:

【Optical flow】Learning Optical Flow with Convolutional Neural Networks

Philipp Fischer:

Alexey Dosovitskiy:

Eddy Ilg:

Philip Häusser:

Caner Hazırbaş:

Vladimir Golkov:

Patrick van der Smagt:

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

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

【Optical flow】Dual-Feature Warping-based Motion Model Estimation

Shiwei Li:

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

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

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

【Optical flow】Oriented Light-Field Windows for Scene Flow

Pratul Srinivasan:

Michael Tao:

Ren Ng:

Ravi Ramamoorthi:

【Optical flow】Large Displacement 3D Scene Flow with Occlusion Reasoning

Andrei Zanfir:

Cristian Sminchisescu:

【Region matching】Robust Nonrigid Registration by Convex Optimization

Qifeng Chen:

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

【Region matching】Dense Semantic Correspondence where Every Pixel is a Classifier

Hilton Bristow:

Jack Valmadre:

Simon Lucey:

【Region matching】Registering Images to Untextured Geometry using Average Shading Gradients

Tobias Plötz:

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

【Region matching】Multi-Image Matching via Fast Alternating Minimization

Xiaowei Zhou:

Menglong Zhu:

Kostas Danilidiis:

【Region matching】Robust and Optimal So S-based Point-to-Plane Registration of Image Sets and Structured Scenes

Danda Pani Paudel:

Adlane Habed:

Cédric Demonceaux:

Pascal Vasseur:

【Region matching】A Groupwise Multilinear Correspondence Optimization for 3D Faces

Timo Bolkart:

Stefanie Wuhrer:

【Region matching】Patch Match-based Automatic Lattice Detection for Near-Regular Textures

Siying Liu:

Tian-Tsong Ng:

Minh Do:

Kalyan Sunkavalli:

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

Nathan Carr:

【Region matching】Mining And-Or Graphs for Graph Matching

Quanshi Zhang:

Yingnian Wu:

Song-Chun Zhu:

【Region matching】Projection onto the Manifold of Elongated Structures for Accurate Extraction

Amos Sironi:

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

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

【Region matching】Learning Ordinal Relationships for Mid-Level Vision

Daniel Zoran:

Phillip Isola:

Dilip Krishnan:

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

【Region matching】Globally Optimal 2D / 3D Registration from Points or Lines Without Correspondences

Mark Brown:

David Windridge:

Jean-Yves Guillemaut:

【Region matching】3D Fragment Reassembly using Integrated Template Guidance and Fracture-Region Matching

Kang Zhang:

Wuyi Yu:

Mary Manhein:

Waggenspack Warren:

Xin Li:

【Region matching】Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs

Mehrtash Harandi:

Mathieu Salzmann:

Mahsa Baktashmotlagh:

【Region matching】Discrete Tabu Search for Graph Matching

Kamil Adamczewski:

Yumin Suh:

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

【Region matching】Peeking Template Matching for Depth Extension

Simon Korman:

Eyal Ofek:

Shai Avidan:

【Region matching】Discriminative Pose-Free Descriptors for Face and Object Matching

Soubhik Sanyal:

Sivaram Mudunuri:

Soma Biswas:

【Image editing】Mutual-Structure for Joint Filtering

Xiaoyong Shen:

Chao Zhou:

Li Xu:

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

【Image editing】Learning Discriminative Reconstructions for Unsupervised Outlier Removal

Yan Xia:

Xudong Cao:

Fang Wen:

Gang Hua:

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

【Image editing】Removing rain from a single image via discriminative sparse coding

Yu Luo:

Yong Xu:

Hui Ji:

【Image editing】Leave-One-Out Kernel Optimization for Shadow Detection

Tomas F Yago Vicente:

Minh Hoai:

Dimitris Samaras:

【Image editing】A Comprehensive Multi-illuminant Dataset for Benchmarking of the Intrinsic Image Algorithms

Shida Beigpour:

Andreas Kolb:

Sven Kunz:

【Image editing】Nighttime Haze Removal with Glow and Multiple Light Colors

YU LI:

Robby Tan:

Michael Brown: http://www.comp.nus.edu.sg/~brown/

【Image editing】Conformal and Low-Rank Sparse Representation for Image Restoration

Jianwei Li:

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

Dongqing Zou:

Bo Gao:

Bin Zhou:

【Image editing】Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising

Jun Xu:

Lei Zhang:

Wangmeng Zuo:

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

Xiangchu Feng:

【Image editing】Learning Nonlinear Spectral Filters for Color Image Reconstruction

Julia Diebold:

Michael Moeller:

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

Guy Gilboa:

【Image editing】Beyond White: Ground Truth Colors for Color Constancy Correction

Dongliang Cheng:

Brian Price:

Scott Cohen:

Michael Brown: http://www.comp.nus.edu.sg/~brown/

【Image editing】Learning a Discriminative Model for the Perception of Realism using Composite Images

Jun-yan Zhu:

Philipp Krahenbuhl:

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

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

【Image editing】Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition

Tinghui Zhou:

Philipp Krahenbuhl:

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

【Image editing】RGB-Guided Hyperspectral Image Upsampling

Hyeokhyen Kwon:

Yu-Wing Tai:

【Image editing】Naive Bayes Super-Resolution Forest

Jordi Salvador:

Eduardo Pérez-Pellitero:

【Image editing】POP image fusion – derivative domain image fusion without reintegration

Graham Finlayson:

Alex Hayes:

【Image editing】Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising

Ying Fu:

Antony Lam:

Imari Sato:

Yoichi Sato:

【Image editing】Fully Connected Guided Image Filtering

Longquan Dai:

Mengke Yuan:

Feihu Zhang:

Xiaopeng Zhang:

【Image editing】Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing

Feihu Zhang:

Longquan Dai:

Shiming XIANG:

Xiaopeng Zhang:

【Image editing】Deeply Improved Sparse Coding for Image Super-Resolution

Zhaowen Wang:

Ding Liu:

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

Wei Han:

Thomas Huang:

【Image editing】Convolutional Color Constancy

Jonathan Barron:

【Image editing】Thin Structure Estimation with Curvature Regularization

Dmitrii Marin:

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

Yuchen Zhong:

【Image editing】Deep Colorization

Zezhou Cheng:

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

Bin Sheng:

【Image editing】Personalized Age Progression with Aging Dictionary

Xiangbo Shu:

jinhui Tang:

Hanjiang Lai:

Luoqi Liu:

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

【Image editing】Separating Fluorescent and Reflective Components by Using a Single Hyperspectral Image

Yinqiang Zheng:

Ying Fu:

Antony Lam:

Imari Sato:

Yoichi Sato:

【Image editing】Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting

Hanzi Wang:

Guobao Xiao:

Yan Yan:

David Suter:

【Image editing】Complementary Sets of Shutter Sequences for Motion Deblurring

Hae-Gon Jeon:

Joon-Young Lee:

Yudeog Han:

Seon Joo Kim:

In So Kweon: http://rcv.kaist.ac.kr/

【Image editing】Blur-aware Disparity Estimation from Defocus Stereo Images

Ching-Hui Chen:

Hui Zhou:

Timo Ahonen:

【Image editing】Adaptive Exponential Smoothing for Online Filtering of Pixel Prediction Maps

Kang Dang:

JIONG YANG:

Junsong Yuan:

【Image editing】Intrinsic decomposition of image sequences from local temporal variations

Pierre-Yves Laffont:

Jean-Charles Bazin:

【Image editing】Low-Rank Tensor Approximation with Laplacian Scale Mixture Modeling for Multiframe Image Denoising

Weisheng Dong:

Guangyu Li:

Guangming Shi:

Xin LI:

Yi Ma: http://yima.csl.illinois.edu/

【Image editing】Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding

Yongbo Li:

Weisheng Dong:

Guangming Shi:

【Image editing】The joint image handbook

Matthew Trager:

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

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

【Image editing】Improving Image Restoration with Soft-Rounding

Xing Mei:

Honggang Qi:

Baogang Hu:

Siwei Lyu:

【Image editing】Spe Do: 6 DOF Ego-Motion Sensor Using Speckle Defocus Imaging

Kensei Jo:

Mohit Gupta:

Shree Nayar:

【Image editing】Detection and Segmentation of 2D Curved Symmetric Structures

Ching Teo:

Cornelia Fermuller:

Yiannis Aloimonos: http://www.cfar.umd.edu/~yiannis/

【Image editing】Rendering of Eyes for Eye-Shape Registration and Gaze Estimation

Erroll Wood:

Tadas Baltrušaitis:

Peter Robinson:

Andreas Bulling:

Yusuke Sugano:

Xucong Zhang:

【Image editing】An Efficient Statistical Method for Image Noise Level Estimation

Guangyong Chen:

Fengyuan Zhu:

Pheng ann Heng:

【Image editing】Appearance Mosaics from Internet Photo-Collections

Dinghuang Ji:

Enrique Dunn:

Jan-Michael Frahm:

【Image editing】Class-Specific Image Deblurring

Saeed Anwar:

Cong Phuoc Huynh:

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

【Image editing】Variational Depth Superresolution using Example-Based Edge Representations

David Ferstl:

Gernot Riegler:

Matthias Rüther:

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

【Image editing】Conditioned Regression Models for Non-Blind Single Image Super-Resolution

Gernot Riegler:

Samuel Schulter:

Matthias Rüther:

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

【Image editing】Convolutional Sparse Coding for Image Super-resolution

Shuhang Gu:

Lei Zhang:

Wangmeng Zuo:

Xiangchu Feng:

Deyu Meng:

Qi Xie:

【Image editing】Multi-Frame Super-Resolution via Draft-Ensemble Learning

Renjie Liao:

Xin Tao:

Ziyang Ma:

Ruiyu Li:

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

【Image editing】Pan-sharpening with a Hyper-Laplacian Penalty

Yiyong Jiang:

Xinghao Ding:

Delu Zeng:

Yue Huang:

John Paisley:

【Image editing】Hot Or Not: What Images Tell Us About Temperature

Daniel Glasner:

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

Todd Zickler:

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

【Image editing】Geometry-aware Deep Transform

Jiaji Huang:

Qiang Qiu:

Robert Calderbank:

Guillermo Sapiro:

【Image editing】Video Restoration against Yin-Yang Phasing

Xiaolin Wu:

Zhenhao Li:

Xiaowei Deng:

【Image editing】Rolling Shutter Super-Resolution

Abhijith Punnappurath:

Vijay Rengarajan:

A N Rajagopalan:

【Image editing】Learning Large-Scale Automatic Image Colorization

Aditya Deshpande:

Jason Rock:

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

【Image editing】Compression Artifacts Reduction by Deep Convolutional Network

Chao Dong:

Yubin Deng:

Chen-Change Loy:

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

【Image editing】Amodal Completion and Size Constancy in Natural Scenes

Abhishek Kar:

Shubham Tulsiani:

Joao Carreira:

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

【Image editing】Fine-Grained Change Detection of Misaligned Scenes with Varied Illuminations

Wei Feng:

Fei-Peng Tian:

Qian Zhang:

Nan Zhang:

Liang Wan:

Jizhou Sun:

【Image editing】Classical Scaling Revisited

Gil Shamai:

Yonathan Aflalo:

Ron Kimmel:

Michael Zibulevsky:

【Image editing】Hyperspectral Super-Resolution by Coupled Spectral Unmixing

Charis Alexandros Lanaras:

Emmanuel Baltsavias:

Konrad Schindler:

【Image editing】External patch prior guided internal clustering for image denoising

Fei Chen:

Lei Zhang:

Huimin Yu:

【Computational photography】Active oneshot scan for wide depth range using a light-field projector based on coded aperture

Hiroshi Kawasaki:

Yuki Horita:

Yuki Shiba:

Satoshi Ono:

Ryo Furukawa:

Shinsaku Hiura:

【Computational photography】Depth Selective Camera: A Direct, On-chip, Programmable Technique for Depth Selectivity in Photography

Ryuichi Tadano:

Adithya Pediredla:

Ashok Veeraraghavan:

【Computational photography】Self-calibration of optical lenses

Michael Hirsch:

Bernhard Schölkopf:

【Computational photography】Illumination Robust Color Naming via Label Propagation

yuanliu liu:

Zejian Yuan:

【Data clustering】Low-Rank Tensor Constrained Multiview Subspace Clustering

Changqing Zhang:

Si Liu:

Huazhu Fu:

Guangcan Liu:

Xiaochun Cao:

【Data clustering】Unsupervised Trajectory Clustering via Adaptive Multi-Kernel-based Shrinkage

Hongteng Xu:

Yang Zhou:

Weiyao Lin:

Hongyuan Zha:

【Data clustering】Multi-Modal Subspace Clustering

Hongchang Gao:

Feiping Nie:

Heng Huang:

【Space reduction】Shell PCA: statistical shape modelling in shell space

Chao Zhang:

William Smith:

Behrend Heeren:

Martin Rumpf:

【Space reduction】Secrets of Matrix Factorization: Approximations, Numerics and Manifold Optimization

Je Hyeong Hong:

Andrew Fitzgibbon:

【Machine learning】SPM-BP: Sped-up Patch Match Belief Propagation for Continuous MRFs

YU LI:

Dongbo Min:

Jiangbo Lu:

Michael Brown: http://www.comp.nus.edu.sg/~brown/

Minh Do:

【Machine learning】Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data

Pan Ji:

Mathieu Salzmann:

Hongdong Li:

【Machine learning】Panoptic Studio: A Massively Multiview System for Social Motion Capture

Hanbyul Joo:

Hao Liu:

Lei Tan:

Lin Gui:

Shohei Nobuhara:

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

【Machine learning】CV-HAZOP: Introducing Test Data Validation for Computer Vision

Oliver Zendel:

Markus Murschitz:

Martin Humenberger:

Wolfgang Herzner:

【Machine learning】Low-rank Matrix Factorization under General Mixture Noise Distributions

Xiangyong Cao:

Yang Chen:

Qian Zhao:

Deyu Meng:

Yao Wang:

Dong Wang:

Zongben Xu:

【Machine learning】Semantic Component Analysis

Calvin Murdock:

Fernando De la Torre:

【Machine learning】Learning Query and Image Similarities with Ranking Canonical Correlation Analysis

Ting Yao:

Tao Mei:

Chong-Wah Ngo:

【Machine learning】Fill and Transfer: A Simple Physics-based Approach for Containability Reasoning

Lap-Fai Yu:

Noah Duncan:

Sai-Kit Yeung:

【Machine learning】Active Learning Revisited: Reusing Past Datasets for Future Tasks

Stratis Gavves:

Thomas Mensink:

Tatiana Tommasi:

Cees Snoek:

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

【Machine learning】Learning Concept Embeddings with Combined Human-Machine Expertise

Michael Wilber:

Iljung Kwak:

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

【Machine learning】A Matrix Decomposition Perspective to Multiple Graph Matching

Junchi Yan:

Hongteng Xu:

Hongyuan Zha:

Xiaokang Yang:

【Machine learning】Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning

Chun-Guang Li:

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

Honggang Zhang:

Jun Guo:

【Machine learning】A Novel Sparsity Measure for Tensor Recovery

Qian Zhao:

Deyu Meng:

Xu Kong:

Qi Xie:

Wenfei Cao:

Yao Wang:

Zongben Xu:

【Machine learning】VQA: Visual Question Answering

Stanislaw Antol:

Aishwarya Agrawal:

Jiasen Lu:

Margaret Mitchell:

Dhruv Batra:

Larry Zitnick:

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

【Machine learning】Adaptive Dither Voting for Robust Spatial Verification

Xiaomeng Wu:

Kunio Kashino:

【Machine learning】Learning-to-Rank based on Subsequences

Basura Fernando:

Stratis Gavves:

Damien Muselet:

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

【Machine learning】Alternating Co-Quantization for Cross-modal Hashing

Go Irie:

Hiroyuki Arai:

Yukinobu Taniguchi:

【Machine learning】Entropy Minimization for Convex Relaxation Approaches

Mohamed Souiai:

Martin Oswald:

Youngwook Kee:

Junmo Kim:

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

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

【Machine learning】A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis

Sotirios Chatzis:

Dimitrios Kosmopoulos:

【Machine learning】Robust Principal Component Analysis on Graphs

Nauman Shahid:

Vassilis Kalofolias:

Xavier Bresson:

Michael Bronstein:

Pierre Vandergheynst:

【Machine learning】Projection Bank: From High-dimensional Data to Medium-length Binary Codes

Li Liu:

Mengyang Yu:

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

【Machine learning】Robust Optimization for Deep Regression

Vasileios Belagiannis:

Christian Rupprecht:

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

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

【Machine learning】What Makes Kevin Spacey Look Like Kevin Spacey

Supasorn Suwajanakorn:

Ira Kemelmacher:

Steve Seitz:

【Machine learning】Unsupervised Domain Adaptation for Zero-Shot Learning

Elyor Kodirov:

Tao Xiang:

Zhenyong Fu:

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

【Machine learning】Know Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models

Ashesh Jain:

Hema Koppula:

Bharad Raghavan:

Shane Soh:

Ashutosh Saxena: http://www.cs.cornell.edu/~asaxena/

【Machine learning】Additive Nearest Neighbor Feature Maps

WANG Zhenzhen:

YUAN Xiao-Tong:

LIU Qingshan:

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

【Machine learning】Unsupervised Cross-modal Synthesis of Subject-specific Scans

Raviteja Vemulapalli:

Hien Nguyen:

Kevin Zhou:

【Machine learning】Interpolation on the manifold of $k$ component GMMs

Hyunwoo Kim:

Nagesh Adluru:

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

Baba Vemuri:

Monami Banerjee:

Sarah Turner:

David Fuller:

John Forder:

【Machine learning】Highly-Expressive Spaces of Well-Behaved Transformations: Keeping It Simple

Oren Freifeld:

Soren Hauberg:

Nematollah Batmanghelich:

John Fisher III:

【Machine learning】Entropy-based Latent Structured Output Prediction

Diane Bouchacourt:

Sebastian Nowozin:

  1. Pawan Kumar:

【Machine learning】Fast Orthogonal Projection Based on Kronecker Product

Xu Zhang:

Felix Yu:

Sanjiv Kumar:

Shengjin Wang:

Shi-Fu Chang:

Ruiqi Guo:

【Machine learning】Similarity Gaussian Process Latent Variable Model for Multi-Modal Data Analysis

Guoli Song:

Shuhui Wang:

Qingming Huang:

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

【Machine learning】Learning Ensemble Latent Structured Models in Functional Space by Gradient Boosting

Hossein Hajimirsadeghi:

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

【Machine learning】Unsupervised Learning of Spatiotemporally Coherent Metrics

Ross Goroshin:

Joan Bruna:

Jonathan Tompson:

David Eigen:

Yann Le Cun: http://yann.lecun.com/

【Machine learning】A Multiscale Variable-grouping Framework for MRF Energy Minimization

Omer Meir:

Meirav Galun:

Stav Yagev:

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

Irad Yavneh:

【Machine learning】Inferring M-Best Diverse Labelings in a Single One

Alexander Kirillov:

Bogdan Savchynskyy:

Dmitrij Schlesinger:

Dmitry Vetrov:

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

【Machine learning】Probabilistic Label Relation Graphs with Ising Models

Nan Ding:

Jia Deng:

Kevin Murphy:

Hartmut Neven:

【Machine learning】Improving ferns ensembles by pruning leaves and quantising posterior probabilities

Antonio Rodriguez:

Vitor Sequeira:

【Machine learning】One Shot Learning via Compositions of Meaningful Patches

Alex Wong:

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

【Machine learning】Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior

Lei Zhang:

Wei Wei:

Yanning Zhang:

Qinfeng Shi:

Chunhua Shen:

Fei Li:

【Machine learning】An Accurate Iris Segmentation Framework under Relaxed Imaging Constraints using Total Variation Model

Zijing Zhao:

Kumar Ajay:

【Machine learning】Unsupervised Domain Adaptation with Imbalanced Cross-Domain Data

Tzu Ming Harry Hsu:

Cheng-An Hou:

Wei Yu Chen:

Yi-Ren Yeh:

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

【Machine learning】A Gaussian Process Latent Variable Model for BRDF Inference

Stamatios Georgoulis:

Vincent Vanweddingen:

Marc Proesmans:

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

【Machine learning】Weakly-supervised Structured Output Learning with Flexible and Latent Graphs using High-order Loss Functions

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

tingying Peng:

Christine Bayer:

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

【Machine learning】Simpler non-parametric methods provide as good or better results to multiple-instance learning.

Ragav Venkatesan:

Parag Chandakkar:

Baoxin Li:

【Machine learning】LEWIS: Latent Embeddings for Word Images and their Semantics

Albert Gordo:

Jon Almazan:

Naila Murray:

Florent Perronin:

【Machine learning】Zero-Shot Learning via Semantic Similarity Embedding

Ziming Zhang:

Venkatesh Saligrama:

【Machine learning】A Wavefront Marching Method for Solving the Eikonal Equation on Cartesian Grids

Brais Cancela:

Marcos Ortega:

Manuel Penedo:

【Machine learning】Bayesian non-parametric inference for manifold based Mo Cap representation

Fabrizio Natola:

Valsamis Ntouskos:

Fiora Pirri:

Marta Sanzari:

【Machine learning】The Likelihood-Ratio Test and Efficient Robust Estimation

Andrea Cohen:

Christopher Zach:

【Machine learning】Semi-supervised Zero-Shot Classification with Label Representation Learning

Xin Li:

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

Dale Schuurmans:

【Machine learning】Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts

Margret Keuper:

Evgeny Levinkov:

Nicolas Bonneel:

Guillaume Lavoue:

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

Bjoern Andres:

【Machine learning】Recursive Frechet Mean Computation on the Grassmann and its Applications to Computer Vision

Rudrasis Chakraborty:

Baba Vemuri:

【Machine learning】A Projection free method for Generalized Eigenvalue Problem with a nonsmooth Regularizer

Seong Jae Hwang:

Maxwell Collins:

Sathya Ravi:

Vamsi Ithapu:

Nagesh Adluru:

Sterling Johnson:

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

【Machine learning】Optimizing expected Intersection-over-Union with candidate-constrained CRFs

Faruk Ahmed:

Vittal Premachandran:

Dany Tarlow:

Dhruv Batra:

【Machine learning】Higher-Order Inference for Multi-class Log-supermodular Models

Jian Zhang:

Josip Djolonga:

Andreas Krause:

【Machine learning】Group Membership Prediction

Ziming Zhang:

Yuting Chen:

Venkatesh Saligrama:

【Deep learning】Webly Supervised Learning of Convolutional Networks

Xinlei Chen:

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

【Deep learning】Ask Your Neurons: A Neural-based Approach to Answering Questions about Images

Mateusz Malinowski:

Marcus Rohrbach:

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

【Deep learning】Fast R-CNN

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

【Deep learning】Deep Fried Convnets

Zichao Yang:

Marcin Moczulski:

Misha Denil:

Nando Freitas: http://www.cs.ubc.ca/~nando/

Alex Smola:

Le Song:

Ziyu Wang:

【Deep learning】Deep Convolutional Neural Decision Forests

Madalina Fiterau:

Peter Kontschieder:

Antonio Criminisi:

Samuel Rota Bulò:

【Deep learning】Deep Multiple Instance Network

Xin Lu:

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

Xiaohui Shen:

Radomir Mech:

James Wang:

【Deep learning】Deep Dynamic Convolutional Networks

Jiashi Feng:

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

【Deep learning】Conditional Random Fields as Recurrent Neural Networks

Shuai Zheng:

Sadeep Jayasumana:

Bernardino Romera-Paredes:

Vibhav Vineet:

Zhizhong Su:

Dalong Du:

Chang Huang:

philip Torr:

【Deep learning】Adaptively Unified Semi-Supervised Dictionary Learning with Active Points

Xiaobo Wang:

Xiaojie Guo:

Yangjun Lu:

Shiming XIANG:

【Deep learning】An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections

Yu Cheng:

Felix Yu:

Rogerio Feris: http://rogerioferis.com/

Sanjiv Kumar:

Shi-Fu Chang:

【Deep learning】Contractive Rectifier Networks for Nonlinear Maximum Margin Classification

Senjian An:

Munawar Hayat:

Salman Khan:

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

Farid Boussaid:

Ferdous Sohel:

【Deep learning】Bi-shifting Auto-Encoder for Unsupervised Domain Adaptation

Meina Kan:

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

Xilin Chen:

【Deep learning】You Are Here: Mimicking the Human Thinking Process in Reading Floor-Plans

Hang Chu:

Dong Ki Kim:

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

【Deep learning】Matrix Backpropagation for Deep networks with Structured Layers

Catalin Ionescu:

Orestis Vantzos:

Cristian Sminchisescu:

【Deep learning】Direct Intrinsics: Albedo and Shading Decomposition by CNN Regression

Takuya Narihira:

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

Stella Yu:

【Deep learning】On Statistical Analysis of Neuroimages with Imperfect Registration

Won Hwa Kim:

Sathya Ravi:

Ozioma Okonkwo:

Sterling Johnson:

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

【Deep learning】Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions

Jimmy Ba:

Kevin Swersky:

Ruslan salakhutdinov:

Sanja Fidler:

【Deep learning】Conditional High-order Boltzmann Machine: A Supervised Learning Model for Relation Learning

Yan Huang:

Wei Wang:

Liang Wang: