hit2sjtu/cvpr_2017_notes

CVPR 2017 notes

CVPR 2017 notes

This is my personal notes and focuses on several aspects as listed below.

If you want to see all CVPR 2017 papers, please refer to the CVF open access of all CVPR 2017 papers:

• Main Conference Papers
• Workshop Papers

Table of Contents

Low Level vision

1. Sensor & Calibration
2. Multiple View Geometry
3. 3D Reconstruction
4. 3D vision and Deep Learning

Middle level vision

1. Computational Photography
2. Optical Flow
3. 2D/3D feature detection and matching
4. SLAM
5. Image Retrieval

High Level vision

1. Human Analysis
2. Face Analysis
3. Object Tracking
4. 6 DOF Object detection & tracking
5. Object Detection

ML and vision

1. Transfer Learning & Domain Adaptation
2. DL model compression
3. Machine Learning theory

Topics not included here but I am also interested with:

1. Image/video Captioning
2. Visual Question Answering (VQA)
3. Vision & Sports
4. Edge detection & semantic segmentation
5. Video / temporal analysis
6. Denoising/deblurring
7. Texture/style network

Sensor & Calibration

• A Practical Method for Fully Automatic Intrinsic Camera Calibration Using Directionally Encoded Light, Mahdi Abbaspour Tehrani, Thabo Beeler, Anselm Grundhöfer

• Radiometric Calibration for Internet Photo Collections, Zhipeng Mo, Boxin Shi, Sai-Kit Yeung, Yasuyuki Matsushita

• Intel RealSense Stereoscopic Depth Cameras. Intel

• Self-Calibration-Based Approach to Critical Motion Sequences of Rolling-Shutter Structure From Motion, Eisuke Ito, Takayuki Okatani

• Dynamic Time-Of-Flight. Michael Schober, Amit Adam, Omer Yair, Shai Mazor, Sebastian Nowozin

Multiple View Geometry

• 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

• Efficient Solvers for Minimal Problems by Syzygy-Based Reduction, Viktor Larsson, Kalle Åström, Magnus Oskarsson

• A Minimal Solution for Two-View Focal-Length Estimation Using Two Affine Correspondences, Daniel Barath, Tekla Toth, Levente Hajder

• A Clever Elimination Strategy for Efficient Minimal Solvers. Zuzana Kukelova, Joe Kileel, Bernd Sturmfels, Tomas Pajdla

• An Efficient Algebraic Solution to the Perspective-Three-Point Problem, Tong Ke, Stergios I. Roumeliotis

• The Misty Three Point Algorithm for Relative Pose, Tobias Palmér, Kalle Åström, Jan-Michael Frahm

• Consensus Maximization With Linear Matrix Inequality Constraints, Pablo Speciale, Danda Pani Paudel, Martin R. Oswald, Till Kroeger, Luc Van Gool, Marc Pollefeys

3D Reconstruction

Dataset & Benchmark

• ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes, Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, Matthias Nießner

• 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, Andreas Geiger

Single View Reconstruction

• Using Locally Corresponding CAD Models for Dense 3D Reconstructions from a Single Image. Chen Kong, Chen-Hsuan Lin,Simon Lucey. Carnegie Mellon University

• Multi-View Supervision for Single-View Reconstruction via Differentiable Ray Consistency. Shubham Tulsiani, Tinghui Zhou, Alexei A. Efros, Jitendra Malik

• A Point Set Generation Network for 3D Object Reconstruction From a Single Image, Haoqiang Fan, Hao Su, Leonidas J. Guibas

• Transformation-Grounded Image Generation Network for Novel 3D View Synthesis, Eunbyung Park, Jimei Yang, Ersin Yumer, Duygu Ceylan, Alexander C. Berg

• SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks, Ayan Sinha, Asim Unmesh, Qixing Huang, Karthik Ramani

• IM2CAD, Hamid Izadinia, Qi Shan, Steven M. Seitz

• Learning Category-Specific 3D Shape Models From Weakly Labeled 2D Images, Dingwen Zhang, Junwei Han, Yang Yang, Dong Huang

Monocular Depth Map Prediction

• Unsupervised Monocular Depth Estimation With Left-Right Consistency, Clément Godard, Oisin Mac Aodha, Gabriel J. Brostow

• Semi-Supervised Deep Learning for Monocular Depth Map Prediction, Yevhen Kuznietsov, Jörg Stückler, Bastian Leibe

Traditional 3D Reconstruction

• HSfM: Hybrid Structure-from-Motion, Hainan Cui, Xiang Gao, Shuhan Shen, Zhanyi Hu

• KillingFusion: Non-Rigid 3D Reconstruction without Correspondences, Miroslava Slavcheva, Maximilian Baust, Daniel Cremers, Slobodan Ilic

3D Vision and Deep Learning

• Convolutional Neural Network Architecture for Geometric Matching. INRIA. Ignacio Rocco, Relja Arandjelović, Josef Sivic

• FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence, Seungryong Kim, Dongbo Min, Bumsub Ham, Sangryul Jeon, Stephen Lin, Kwanghoon Sohn

• Inverse Compositional Spatial Transformer Networks. Chen-Hsuan Lin, Simon Lucey.

• Geometric Loss Functions for Camera Pose Regression with Deep Learning. Alex Kendall, Roberto Cipolla.

• DSAC - Differentiable RANSAC for Camera Localization, Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother

• Learning Non-Maximum Suppression, Jan Hosang, Rodrigo Benenson, Bernt Schiele

• Improved Stereo Matching With Constant Highway Networks and Reflective Confidence Learning, Amit Shaked, Lior Wolf

• End-To-End Training of Hybrid CNN-CRF Models for Stereo. Patrick Knobelreiter, Christian Reinbacher, Alexander Shekhovtsov, Thomas Pock

Computational Photography

• A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation. KAIST & Tecent Youtu

• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi.

• Depth From Defocus in the Wild, Huixuan Tang, Scott Cohen, Brian Price, Stephen Schiller, Kiriakos N. Kutulakos

Optical Flow

• FlowNet 2.0: Evolution of Optical Flow Estimation With Deep Networks Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, Thomas Brox

• Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data, Joel Janai, Fatma Güney, Jonas Wulff, Michael J. Black, Andreas Geiger

• CNN-Based Patch Matching for Optical Flow With Thresholded Hinge Embedding Loss, Christian Bailer, Kiran Varanasi, Didier Stricker

• Optical Flow Estimation Using a Spatial Pyramid Network, Anurag Ranjan, Michael J. Black

• CLKN: Cascaded Lucas-Kanade Networks for Image Alignment, Che-Han Chang, Chun-Nan Chou, Edward Y. Chang

• S2F: Slow-To-Fast Interpolator Flow, Yanchao Yang, Stefano Soatto

• Filter Flow Made Practical: Massively Parallel and Lock-Free, Sathya N. Ravi, Yunyang Xiong, Lopamudra Mukherjee, Vikas Singh

• Accurate Optical Flow via Direct Cost Volume Processing, Jia Xu, René Ranftl, Vladlen Koltun

• InterpoNet, a Brain Inspired Neural Network for Optical Flow Dense Interpolation, Shay Zweig, Lior WolfI

• Optical Flow in Mostly Rigid Scenes, Jonas Wulff, Laura Sevilla-Lara, Michael J. Black

• Optical Flow Requires Multiple Strategies (but Only One Network), Tal Schuster, Lior Wolf, David Gadot

2D/3D Feature Detection and Matching

Feature Detection

• Learning Discriminative and Transformation Covariant Local Feature Detectors, Xu Zhang, Felix X. Yu, Svebor Karaman, Shih-Fu Chang

• Quad-Networks: Unsupervised Learning to Rank for Interest Point Detection, Nikolay Savinov, Akihito Seki, Lubor Ladicky, Torsten Sattler, Marc Pollefeys

Feature descriptor

• Learning Deep Binary Descriptor With Multi-Quantization, Yueqi Duan, Jiwen Lu, Ziwei Wang, Jianjiang Feng, Jie Zhou

• L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space, Yurun Tian, Bin Fan, Fuchao Wu

• 3DMatch: Learning Local Geometric Descriptors From RGB-D Reconstructions. Andy Zeng, Shuran Song, Matthias Niessner, Matthew Fisher, Jianxiong Xiao, Thomas Funkhouser

Feature Matching

• GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence. JiaWang Bian, Daniel Lin, Yasuyuki Matsushita, Sai-Kit Yeung, Tan Dat Nguyen, Ming-Ming Cheng

• Alternating Direction Graph Matching, D. Khuê Lê-Huu, Nikos Paragios

• SGM-Nets: Semi-Global Matching With Neural Networks, Akihito Seki, Marc Pollefey

Semantic Matching

• AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching, David Novotny, Diane Larlus, Andrea Vedaldi

• Deep Semantic Feature Matching, Nikolai Ufer, Björn Ommer

Comparison

• Comparative Evaluation of Hand-Crafted and Learned Local Features. Johannes L. Schönberger,Hans Hardmeier, Torsten Sattler, Marc Pollefeys

• HPatches: A benchmark and evaluation of handcrafted and learned local descriptors, Vassileios Balntas*, Karel Lenc*, Andrea Vedaldi and Krystian Mikolajczyk.

SLAM

SLAM System

• CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction.

• NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance.Geoffrey Pascoe, Will Maddern, Michael Tanner, Pedro Piniés, Paul Newman.

• DeMoN: Depth and Motion Network for Learning Monocular Stereo, Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, Thomas Brox

• Unsupervised Learning of Depth and Ego-Motion From Video, Tinghui Zhou, Matthew Brown, Noah Snavely, David G. Lowe

Image base localization

• A Dataset for Benchmarking Image-Based Localization Xun Sun, Yuanfan Xie, Pei Luo, Liang Wang

• 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

• Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization? Torsten Sattler, Akihiko Torii, Josef Sivic, Marc Pollefeys, Hajime Taira, Masatoshi Okutomi, Tomas Pajdla

Event Camera

• Event-Based Visual Inertial Odometry, Alex Zihao Zhu, Nikolay Atanasov, Kostas Daniilidis

SLAM for X

• Cognitive Mapping and Planning for Visual Navigation. Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik

• Visual-Inertial-Semantic Scene Representation for 3D Object Detection. Jingming Dong, Xiaohan Fei, Stefano Soatto

Image Retrieval

Hashing

• AMVH: Asymmetric Multi-Valued Hashing. Cheng Da, Shibiao Xu, Kun Ding, Gaofeng Meng, Shiming Xiang, Chunhong Pan

• Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval, Li Liu, Fumin Shen, Yuming Shen, Xianglong Liu, Ling Shao

• Bayesian Supervised Hashing, Zihao Hu, Junxuan Chen, Hongtao Lu, Tongzhen Zhang

• Simultaneous Feature Aggregating and Hashing for Large-Scale Image Search, Thanh-Toan Do, Dang-Khoa Le Tan, Trung T. Pham, Ngai-Man Cheung

• Discretely Coding Semantic Rank Orders for Supervised Image Hashing, Li Liu, Ling Shao, Fumin Shen, Mengyang Yu

Other

• Deep Visual-Semantic Quantization for Efficient Image Retrieval, Yue Cao, Mingsheng Long, Jianmin Wang, Shichen Liu

• Spatial-Semantic Image Search by Visual Feature Synthesis, Long Mai, Hailin Jin, Zhe Lin, Chen Fang, Jonathan Brandt, Feng Liu

• Asymmetric Feature Maps With Application to Sketch Based Retrieval, Giorgos Tolias, Ondřej Chum

• Fried Binary Embedding for High-Dimensional Visual Features, Weixiang Hong, Junsong Yuan, Sreyasee Das Bhattacharjee

• Learning Multifunctional Binary Codes for Both Category and Attribute Oriented Retrieval Tasks, Haomiao Liu, Ruiping Wang, Shiguang Shan, Xilin Chen

• Kernel Square-Loss Exemplar Machines for Image Retrieval, Rafael S. Rezende, Joaquin Zepeda, Jean Ponce, Francis Bach, Patrick Pérez

Human Analysis

Bottom-up

• Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields, Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh

• Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly, Hao Jiang, Kristen Grauman

TOP-Down

• Towards Accurate Multi-Person Pose Estimation in the Wild, George Papandreou, Tyler Zhu, Nori Kanazawa, Alexander Toshev, Jonathan Tompson, Chris Bregler, Kevin Murphy

• Joint Multi-Person Pose Estimation and Semantic Part Segmentation, Fangting Xia, Peng Wang, Xianjie Chen, Alan L. Yuille

Human in Video

• Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos, Jie Song, Limin Wang, Luc Van Gool, Otmar Hilliges

• PoseTrack: Joint Multi-Person Pose Estimation and Tracking, Umar Iqbal, Anton Milan, Juergen Gall

• ArtTrack: Articulated Multi-Person Tracking in the Wild, Eldar Insafutdinov, Mykhaylo Andriluka, Leonid Pishchulin, Siyu Tang, Evgeny Levinkov, Bjoern Andres, Bernt Schiele

• On Human Motion Prediction Using Recurrent Neural Networks, Julieta Martinez, Michael J. Black, Javier Romero

3D Pose

• Unite the People: Closing the Loop Between 3D and 2D Human Representations, Christoph Lassner, Javier Romero, Martin Kiefel, Federica Bogo, Michael J. Black, Peter V. Gehler

• Deep Multitask Architecture for Integrated 2D and 3D Human Sensing, Alin-Ionut Popa, Mihai Zanfir, Cristian Sminchisescu

• Recurrent 3D Pose Sequence Machines, Mude Lin, Liang Lin, Xiaodan Liang, Keze Wang, Hui Cheng

• 3D Human Pose Estimation = 2D Pose Estimation + Matching, Ching-Hang Chen, Deva Ramanan

• Lifting From the Deep: Convolutional 3D Pose Estimation From a Single Image, Denis Tome, Chris Russell, Lourdes Agapito

• Coarse-To-Fine Volumetric Prediction for Single-Image 3D Human Pose, Georgios Pavlakos, Xiaowei Zhou, Konstantinos G. Derpanis, Kostas Daniilidis

• LCR-Net: Localization-Classification-Regression for Human Pose. Gregory Rogez, Philippe Weinzaepfel, Cordelia Schmid

• Learning From Synthetic Humans, Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev, Cordelia Schmid

Action Recognition

• Deep Learning on Lie Groups for Skeleton-Based Action Recognition, Zhiwu Huang, Chengde Wan, Thomas Probst, Luc Van Gool

• Weakly Supervised Action Learning With RNN Based Fine-To-Coarse Modeling, Alexander Richard, Hilde Kuehne, Juergen Gall

Hand

• Hand Keypoint Detection in Single Images Using Multiview Bootstrapping, Tomas Simon, Hanbyul Joo, Iain Matthews, Yaser Sheikh

Dataset & Benchmark

• BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis, Shanxin Yuan, Qi Ye, Björn Stenger, Siddhant Jain, Tae-Kyun Kim

• Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset, João Carreira, Andrew Zisserman

• 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

Face Analysis

Face Recognition

• Disentangled Representation Learning GAN for Pose-Invariant Face Recognition, Luan Tran, Xi Yin, Xiaoming Liu

• Level Playing Field for Million Scale Face recognition, Aaron Nech, Ira Kemelmacher-Shlizerman

• Neural Aggregation Network for Video Face Recognition, Jiaolong Yang, Peiran Ren, Dongqing Zhang, Dong Chen, Fang Wen, Hongdong Li, Gang Hua

• Pose-Aware Person Recognition, Vijay Kumar, Anoop Namboodiri, Manohar Paluri, C. V. Jawahar

Face Synthesis

• Learning Residual Images for Face Attribute Manipulation, Wei Shen, Rujie Liu

• Photorealistic Facial Texture Inference Using Deep Neural Networks, Shunsuke Saito, Lingyu Wei, Liwen Hu, Koki Nagano, Hao Li

• Synthesizing Normalized Faces From Facial Identity Features, Forrester Cole, David Belanger, Dilip Krishnan, Aaron Sarna, Inbar Mosseri, William T. Freeman

• Neural **Face Editing **With Intrinsic Image Disentangling, Zhixin Shu, Ersin Yumer, Sunil Hadap, Kalyan Sunkavalli, Eli Shechtman, Dimitris Samaras

• 3D Face Morphable Models “In-The-Wild”, James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, Stefanos Zafeiriou

• Parametric T-Spline Face Morphable Model for Detailed Fitting in Shape Subspace, Weilong Peng, Zhiyong Feng, Chao Xu, Yong Su

• Learning Detailed Face Reconstruction From a Single Image, Elad Richardson, Matan Sela, Roy Or-El, Ron Kimmel

Other Face-related

• Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild, Shan Li, Weihong Deng, JunPing Du

• DenseReg: Fully Convolutional Dense Shape Regression In-The-Wild, Rıza Alp Güler, George Trigeorgis, Epameinondas Antonakos, Patrick Snape, Stefanos Zafeiriou, Iasonas Kokkinos

Object Tracking

Correlation Filter

• Context-Aware Correlation Filter Tracking, Matthias Mueller, Neil Smith, Bernard Ghanem

• Discriminative Correlation Filter With Channel and Spatial Reliability, Alan Lukežič, Tomáš Vojíř, Luka Čehovin Zajc, Jiří Matas, Matej Kristan

• End-To-End Representation Learning for Correlation Filter Based Tracking, Jack Valmadre, Luca Bertinetto, João Henriques, Andrea Vedaldi, Philip H. S. Torr

• Attentional Correlation Filter Network for Adaptive Visual Tracking, Jongwon Choi, Hyung Jin Chang, Sangdoo Yun, Tobias Fischer, Yiannis Demiris, Jin Young Choi

Other Tracking

• Template Matching With Deformable Diversity Similarity, Itamar Talmi, Roey Mechrez, Lihi Zelnik-Mano

• Large Margin Object Tracking With Circulant Feature Maps, Mengmeng Wang, Yong Liu, Zeyi Huang

• Robust Visual Tracking Using Oblique Random Forests, Le Zhang, Jagannadan Varadarajan, Ponnuthurai Nagaratnam Suganthan, Narendra Ahuja, Pierre Moulin

• ECO: Efficient Convolution Operators for Tracking, Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg

6 DOF Object Detection & Tracking

Detection

• PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning. Alexander Krull, Eric Brachmann, Sebastian Nowozin, Frank

• Global Hypothesis Generation for 6D Object Pose Estimation. Frank Michel, Alexander Kirillov, Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother.

• BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition. Jacob Chan, Jimmy Addison Lee, Qian Kemao

• 3D Bounding Box Estimation Using Deep Learning and Geometry, Arsalan Mousavian, Dragomir Anguelov, John Flynn, Jana Košecká

Tracking

• Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core. Wadim Kehl, Federico Tombari, Slobodan Ilic, Nassir Navab

Object Detection

Single-stage Systems

• YOLO9000: Better, Faster, Stronger, Joseph Redmon, Ali Farhadi

• Accurate Single Stage Detector Using Recurrent Rolling Convolution. SenseTime. Jimmy Ren, Xiaohao Chen, Jianbo Liu, Wenxiu Sun, Jiahao Pang, Qiong Yan, Yu-Wing Tai, Li Xu

Two-stage systems

• Feature Pyramid Networks for Object Detection. Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie

• 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, Sergio Guadarrama, Kevin Murphy

• Object Detection in Videos With Tubelet Proposal Networks, Kai Kang, Hongsheng Li, Tong Xiao, Wanli Ouyang, Junjie Yan, Xihui Liu, Xiaogang Wang

• Perceptual Generative Adversarial Networks for Small Object Detection, Jianan Li, Xiaodan Liang, Yunchao Wei, Tingfa Xu, Jiashi Feng, Shuicheng Yan

• FastMask: Segment Multi-Scale Object Candidates in One Shot, Hexiang Hu, Shiyi Lan, Yuning Jiang, Zhimin Cao, Fei Sha

• Multiple Instance Detection Network With Online Instance Classifier Refinement, Peng Tang, Xinggang Wang, Xiang Bai, Wenyu Liu

• Weakly Supervised Cascaded Convolutional Networks, Ali Diba, Vivek Sharma, Ali Pazandeh, Hamed Pirsiavash, Luc Van Gool

• RON: Reverse Connection With Objectness Prior Networks for Object Detection, Tao Kong, Fuchun Sun, Anbang Yao, Huaping Liu, Ming Lu, Yurong Chen

• Learning Detection With Diverse Proposals, Samaneh Azadi, Jiashi Feng, Trevor Darrell

• Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes From 2D Ones in RGB-Depth Images. Zhuo Deng, Longin Jan Latecki

Datasets

• YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video, Esteban Real, Jonathon Shlens, Stefano Mazzocchi, Xin Pan, Vincent Vanhoucke

Others

• Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features, Arthur Daniel Costea, Robert Varga, Sergiu Nedevschi

• LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems. Qualcomm & UCSD

• Multi-View 3D Object Detection Network for Autonomous Driving, Xiaozhi Chen, Huimin Ma, Ji Wan, Bo Li, Tian Xia

• Straight to Shapes: Real-Time Detection of Encoded Shapes, Saumya Jetley, Michael Sapienza, Stuart Golodetz, Philip H. S. Torr

Transfer Learning & Domain Adaptation

Zero-shot Learning

• Fine-Grained Recognition of Thousands of Object Categories With Single-Example Training, Leonid Karlinsky, Joseph Shtok, Yochay Tzur, Asaf Tzadok

• Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning, Xing Xu, Fumin Shen, Yang Yang, Dongxiang Zhang, Heng Tao Shen, Jingkuan Song

• Semantically Consistent Regularization for Zero-Shot Recognition, Pedro Morgado, Nuno Vasconcelos

• Unified Embedding and Metric Learning for Zero-Exemplar Event Detection. Noureldien Hussein, Efstratios Gavves, Arnold W.M. Smeulders

• Zero-Shot Learning - the Good, the Bad and the Ugly,Yongqin Xian, Bernt Schiele, Zeynep Akata

• Zero Shot Learning via Multi-Scale Manifold Regularization, Shay Deutsch, Soheil Kolouri, Kyungnam Kim, Yuri Owechko, Stefano Soatto

• From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis, Yang Long, Li Liu, Ling Shao, Fumin Shen, Guiguang Ding, Jungong Han

Transfer Learning

• Borrowing Treasures From the Wealthy: Deep Transfer Learning Through Selective Joint Fine-Tuning. Weifeng Ge, Yizhou Yu .

• Growing a Brain: Fine-Tuning by Increasing Model Capacity, Yu-Xiong Wang, Deva Ramanan, Martial Hebert

Domain Adaptation

• Adversarial Discriminative Domain Adaptation, Eric Tzeng, Judy Hoffman, Kate Saenko, Trevor Darrell

• Quality Aware Network for Set to Set Recognition, Yu Liu, Junjie Yan, Wanli Ouyang, Sensetime

• Correlational Gaussian Processes for Cross-Domain Visual Recognition, Chengjiang Long, Gang Hua

• Joint Geometrical and Statistical Alignment for Visual Domain Adaptation, Jing Zhang, Wanqing Li, Philip Ogunbona

• Deep Hashing Network for Unsupervised Domain Adaptation, Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, Sethuraman Panchanathan

• A Gift From Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning, Junho Yim, Donggyu Joo, Jihoon Bae, Junmo Kim

• Domain Adaptation by Mixture of Alignments of Secondor Higher-Order Scatter Tensors, Piotr Koniusz, Yusuf Tas, Fatih Porikli

DL Model Compression

Quantization

• Network Sketching: Exploiting Binary Structure in Deep CNNs, Yiwen Guo, Anbang Yao, Hao Zhao, Yurong Chen

• Deep Learning With Low Precision by Half-Wave Gaussian Quantization, Zhaowei Cai, Xiaodong He, Jian Sun, Nuno Vasconcelos

• Weighted-Entropy-Based Quantization for Deep Neural Networks, Eunhyeok Park, Junwhan Ahn, Sungjoo Yoo

• Fixed-Point Factorized Networks, Peisong Wang, Jian Cheng

Pruning

• Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning, Tien-Ju Yang, YuHsin Chen, Vivienne Sze

Low-rank Approximation

• On Compressing Deep Models by Low Rank and Sparse Decomposition, Xiyu Yu, Tongliang Liu, Xinchao Wang, Dacheng Tao

• LCNN: Lookup-Based Convolutional Neural Network, Hessam Bagherinezhad, Mohammad Rastegari, Ali Farhad

Distill

• Mimicking Very Efficient Network for Object Detection, Quanquan Li, Shengying Jin, Junjie Yan

Machine Learning

• Densely Connected Convolutional Networks, Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger

• Learning From Simulated and Unsupervised Images Through Adversarial Training, Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Joshua Susskind, Wenda Wang, Russell Webb

• Network Dissection: Quantifying Interpretability of Deep Visual Representations, David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, Antonio Torralba

• AGA: Attribute-Guided Augmentation, Mandar Dixit, Roland Kwitt, Marc Niethammer, Nuno Vasconcelos

• Kernel Pooling for Convolutional Neural Networks, Yin Cui, Feng Zhou, Jiang Wang, Xiao Liu, Yuanqing Lin, Serge Belongie

• Local Binary Convolutional Neural Networks, Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides

• 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

• Newton-Type Methods for Inference in Higher-Order Markov Random Fields, Hariprasad Kannan, Nikos Komodakis, Nikos Paragios