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cvpr_2019_oral.csv
1 | Paper ID | Paper Title | Author Names | Primary Subject Area | Secondary Subject Areas | Status | Day.Time.Track | Group | Topic | Track 1 | Track 2 | Track 3 | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | 5 | Finding Task-Relevant Features for Few-Shot Learning by Category Traversal | Hongyang Li (The Chinese University of Hong Kong)*; David Eigen (Clarifai Inc.); Samuel Dodge (Clarifai Inc.); Matt Zeiler (Clarifai Inc.); Xiaogang Wang (Chinese University of Hong Kong, Hong Kong) | Deep Learning | Oral | 1.1.1 | 1 | Deep Learning | Orals 1.1 | Deep Learning | 3D Multiview | Action & Video | ||
3 | 6340 | Edge-Labeling Graph Neural Network for Few-shot Learning | Jongmin Kim (KAIST)*; Taesup Kim (Université de Montréal); Sungwoong Kim (Kakao Brain); Chang D. Yoo (KAIST) | Deep Learning | Recognition: Detection, Categorization, Retrieval; Representation Learning; Segmentation, Grouping a | Oral | 1.1.1 | 1 | Orals 1.2 | Recognition | Synthesis | Scenes & Representation | ||
4 | 5728 | Generating Classification Weights with Graph Neural Networks for Few-Shot Learning | Spyros Gidaris (valeo.ai)*; Nikos Komodakis ("ENPC, France") | Deep Learning | Recognition: Detection, Categorization, Retrieval | Oral | 1.1.1 | 1 | Orals 2.1 | Deep Learning | 3D Single View & RGBD | Motion & Biometrics | ||
5 | 257 | Kervolutional Neural Networks | Chen Wang (Nanyang Technological University)*; JIANFEI YANG (Nanyang Technological University); Prof. Dr. Respected Colleauge (IJCAS Editorial Member); Junsong Yuan ("State University of New York at Buffalo, USA") | Deep Learning | Computer Vision Theory | Oral | 1.1.1 | 2 | Orals 2.2 | Recognition | Language & Reasoning | Comp. Photography & Graphics | ||
6 | 4863 | Why ReLu networks yield high-confidence predictions far away from the training data and how to mitigate the problem | Matthias Hein (University of Tuebingen)*; Maksym Andriushchenko (Saarland University); Julian Bitterwolf (University of Tuebingen) | Deep Learning | Statistical Learning | Oral | 1.1.1 | 2 | Orals 3.1 | Applications | Learning, Physics, Theory, & Datasets | Segmentation & Grouping | ||
7 | 6679 | On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions | Yusuke Tsuzuku (The University of Tokyo/RIKEN)*; Issei Sato (The university of Tokyo/RIKEN) | Deep Learning | Others | Oral | 1.1.1 | 2 | Orals 3.2 | Deep Learning | Face & Body | Low-level & Optimization | ||
8 | 948 | Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization | Siyuan Qiao (Johns Hopkins University)*; Zhe Lin (Adobe Research); Jianming Zhang (Adobe Research); Alan Yuille (Johns Hopkins University) | Deep Learning | Oral | 1.1.1 | 3 | |||||||
9 | 2284 | Hardness-Aware Deep Metric Learning | Wenzhao Zheng (Tsinghua University); Zhaodong Chen (Tsinghua University); Jiwen Lu (Tsinghua University)*; Jie Zhou (Tsinghua University) | Deep Learning | Representation Learning | Oral | 1.1.1 | 3 | ||||||
10 | 1183 | Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation | Chenxi Liu (Johns Hopkins University)*; Liang-Chieh Chen (Google Inc.); Florian Schroff (Google Inc.); Hartwig Adam (Google); Wei Hua (Google); Alan Yuille (Johns Hopkins University); Li Fei-Fei (Stanford University) | Deep Learning | Segmentation, Grouping and Shape | Oral | 1.1.1 | 3 | ||||||
11 | 1535 | Learning to Learn Loss for Active Learning | Donggeun Yoo (Lunit)*; In So Kweon (KAIST) | Deep Learning | Others; Recognition: Detection, Categorization, Retrieval; Vision Applications and Systems | Oral | 1.1.1 | 4 | ||||||
12 | 2230 | Striking the Right Balance with Uncertainty | Salman Khan (Australian National University (ANU)); Munawar Hayat (University of Canberra); Waqas Zamir (IIAI); Jianbing Shen (Beijing Institute of Technology)*; Ling Shao (Inception Institute of Artificial Intelligence) | Deep Learning | Low-level Vision | Oral | 1.1.1 | 4 | ||||||
13 | 2368 | AutoAugment: Learning Augmentation Strategies from Data | Ekin D Cubuk (Google Brain)*; Barret Zoph (Google); Dandelion Mane (Protocol Labs); Vijay Vasudevan (Google Brain); Quoc Le (Google Brain) | Deep Learning | Oral | 1.1.1 | 4 | |||||||
14 | 494 | SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences | Huu Minh Le (Queensland University of Technology)*; Thanh-Toan Do (The University of Liverpool); Tuan NA Hoang (Singapore University of Technology and Design); Ngai-Man Cheung (Singapore University of Technology and Design) | 3D from Multiview and Sensors | Oral | 1.1.2 | 1 | 3D Multiview | ||||||
15 | 2315 | BAD SLAM: Bundle Adjusted Direct RGB-D SLAM | Thomas Schöps (ETH Zurich)*; Torsten Sattler (Chalmers University of Technology); Marc Pollefeys (ETH Zurich / Microsoft) | 3D from Multiview and Sensors | Datasets and Evaluation; RGBD sensors and analytics | Oral | 1.1.2 | 1 | ||||||
16 | 2286 | Revealing Scenes by Inverting Structure from Motion Reconstructions | Francesco Pittaluga (University of Florida)*; Sanjeev J Koppal (University of Florida); Sing Bing Kang (Microsoft Research); Sudipta Sinha (Microsoft Research) | 3D from Multiview and Sensors | Deep Learning ; Image and Video Synthesis | Oral | 1.1.2 | 1 | ||||||
17 | 1185 | Strand-accurate Multi-view Hair Capture | Giljoo Nam (KAIST)*; Chenglei Wu (Facebook Reality Labs); Min H. Kim (KAIST); Yaser Sheikh (Facebook Reality Labs) | 3D from Multiview and Sensors | Vision + Graphics | Oral | 1.1.2 | 2 | ||||||
18 | 6756 | DeepSDF: Learning Continuous Signed Distance Functionsfor Shape Representation | Jeong Joon Park (University of Washington)*; Peter R Florence (MIT); Julian Straub (Facebook Reality Labs); Richard Newcombe (Facebook); Steven Lovegrove (Facebook) | Deep Learning | 3D from Multiview and Sensors; Representation Learning; Vision + Graphics | Oral | 1.1.2 | 2 | ||||||
19 | 2957 | Pushing the Boundaries of View Extrapolation with Multiplane Images | Pratul Srinivasan (UC Berkeley)*; Richard Tucker (Google); Jonathan T Barron (Google Research); Ravi Ramamoorthi (University of California San Diego); Ren Ng (UC Berkeley); Noah Snavely (Cornell University and Google AI) | 3D from Multiview and Sensors | Computational Photography; Image and Video Synthesis; Vision + Graphics | Oral | 1.1.2 | 2 | ||||||
20 | 1935 | GA-Net: Guided Aggregation Net for End-to-end Stereo Matching | Feihu Zhang (University of Oxford)*; Victor Adrian Prisacariu (University of Oxford); Yang Ruigang (Baidu); Philip Torr (University of Oxford) | 3D from Multiview and Sensors | Deep Learning | Oral | 1.1.2 | 3 | ||||||
21 | 2901 | Real-time self-adaptive deep stereo | Alessio Tonioni (University of Bologna); Fabio Tosi (University of Bologna); Matteo Poggi (University of Bologna)*; Stefano Mattoccia (University of Bologna); Luigi Di Stefano (University of Bologna) | 3D from Multiview and Sensors | Deep Learning ; Low-level Vision | Oral | 1.1.2 | 3 | ||||||
22 | 6639 | LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation | Sunok Kim (Yonsei University); Seungryong Kim (Yonsei University); Dongbo Min (Ewha Womans University); Kwanghoon Sohn (Yonsei Univ.)* | 3D from Multiview and Sensors | Others | Oral | 1.1.2 | 3 | ||||||
23 | 3522 | NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences | Chen Zhao (Huazhong University of Science and Technology); Zhiguo Cao (Huazhong Univ. of Sci.&Tech.); chi li (Huazhong University of Science and Technology); Xin Li (West Virginia University); Jiaqi Yang (Huazhong Univ. of Sci.&Tech.)* | 3D from Multiview and Sensors | Representation Learning | Oral | 1.1.2 | 4 | ||||||
24 | 5852 | Coordinate-Free Carlsson-Weinshall Duality and Relative Multi-View Geometry | Matthew Trager (NYU)*; Martial Hebert (Carnegie Mellon University); Jean Ponce (Inria) | 3D from Multiview and Sensors | Computer Vision Theory | Oral | 1.1.2 | 4 | ||||||
25 | 1944 | Deep Reinforcement Learning of Volume-guided Progressive View Inpainting for 3D Point Scene Completion from a Single Depth Image | Xiaoguang Han (Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong (Shenzhen))*; Zhaoxuan Zhang (Dalian University of Technology, Shenzhen Research Institute of Big Data); Dong Du (University of Science and Technology of China, Shenzhen Research Institute of Big Data); Mingdai Yang (Chinese University of Hong Kong, Shenzhen); Jingming Yu (Alibaba); Pan Pan (Alibaba Group); Xin Yang (Dalian University of Technology); Ligang Liu (University of Science and Technology of China); Zixiang Xiong (Texas A&M University); Shuguang Cui (The Chinese University of Hong Kong, Shenzhen ) | 3D from Multiview and Sensors | Deep Learning | Oral | 1.1.2 | 4 | ||||||
26 | 292 | Video Action Transformer Network | Rohit Girdhar (Carnegie Mellon University)*; Joao Carreira (DeepMind); Carl Doersch (DeepMind); Andrew Zisserman (University of Oxford) | Action Recognition | Deep Learning | Oral | 1.1.3 | 1 | Action & Video | |||||
27 | 302 | Timeception for Complex Action Recognition | Noureldien Hussein (University of Amsterdam)*; Stratis Gavves (University of Amsterdam); Arnold W.M. Smeulders (University of Amsterdam) | Action Recognition | Video Analytics | Oral | 1.1.3 | 1 | ||||||
28 | 1670 | STEP: Spatio-Temporal Progressive Learning for Video Action Detection | Xitong Yang (University of Maryland)*; Xiaodong Yang (NVIDIA Research); Ming-Yu Liu (NVIDIA); Fanyi Xiao (University of California Davis); Larry Davis (University of Maryland); Jan Kautz (NVIDIA) | Video Analytics | Oral | 1.1.3 | 1 | |||||||
29 | 1745 | Relational Action Forecasting | Chen Sun (Google)*; Abhinav Shrivastava (University of Maryland); Carl Vondrick (Columbia University); Rahul Sukthankar (Google); Kevin Murphy (Google); Cordelia Schmid (Google) | Action Recognition | Video Analytics | Oral | 1.1.3 | 2 | ||||||
30 | 2310 | Long-Term Feature Banks for Detailed Video Understanding | Chao-Yuan Wu (UT Austin)*; Christoph Feichtenhofer (Facebook AI Research); Haoqi Fan (Facebook AI Research); Kaiming He (Facebook AI Research); Philipp Kraehenbuehl (UT Austin); Ross Girshick (FAIR) | Action Recognition | Deep Learning ; Recognition: Detection, Categorization, Retrieval; Scene Analysis and Understanding; | Oral | 1.1.3 | 2 | ||||||
31 | 229 | Which Way Are You Going? Imitative Decision Learning for Path Forecasting in Dynamic Scenes | Yuke Li (York University)* | Motion and Tracking | Video Analytics | Oral | 1.1.3 | 2 | ||||||
32 | 3106 | What and How Well You Performed? A Multitask Approach To Action Quality Assessment | Paritosh Parmar (UNLV)*; Brendan Morris (UNLV) | Face, Gesture, and Body Pose | Action Recognition ; Datasets and Evaluation; Video Analytics | Oral | 1.1.3 | 3 | ||||||
33 | 1382 | MHP-VOS: Video Object Segmentation with Multiple Hypotheses Propagation | Shuangjie Xu (Huazhong University of Science and Technology); Daizong Liu (Huazhong University of Science and Technology); Linchao Bao (Tencent AI Lab)*; Wei Liu (Tencent); Pan Zhou ( Huazhong University of Science and Technology) | Video Analytics | Motion and Tracking; Segmentation, Grouping and Shape | Oral | 1.1.3 | 3 | ||||||
34 | 1517 | 2.5D Visual Sound | Ruohan Gao (University of Texas at Austin)*; Kristen Grauman (Facebook AI Research & UT Austin) | Video Analytics | Recognition: Detection, Categorization, Retrieval; Representation Learning | Oral | 1.1.3 | 3 | ||||||
35 | 1999 | Language-driven Temporal Activity Localization: A Semantic Matching Reinforcement Learning Model | Weining Wang (Institute of Automation, Chinese Academy of Sciences)*; Yan Huang (Institute of Automation, Chinese Academy of Sciences); Liang Wang (NLPR, China) | Video Analytics | Vision + Language | Oral | 1.1.3 | 4 | ||||||
36 | 5591 | Gaussian Temporal Awareness Networks for Action Localization | Fuchen Long (University of Science and Technology of China); Ting Yao (JD AI Research)*; Zhaofan Qiu (University of Science and Technology of China); Xinmei Tian (USTC); Jiebo Luo (U. Rochester); Tao Mei (AI Research of JD.com) | Video Analytics | Action Recognition | Oral | 1.1.3 | 4 | ||||||
37 | 6940 | Efficient Video Classification Using Fewer Frames | Shweta Bhardwaj (Indian Institute of Technology Madras, Chennai)*; Mukundhan Srinivasan (NVIDIA); Mitesh M. Khapra (Indian Institute of Technology Madras) | Video Analytics | Deep Learning ; Vision Applications and Systems | Oral | 1.1.3 | 4 | ||||||
38 | 16 | Joint Discriminative and Generative Learning for Person Re-identification | Zhedong Zheng (University of Technology Sydney)*; Xiaodong Yang (NVIDIA Research); Zhiding Yu (NVIDIA); Liang Zheng (Australian National University); Yi Yang (University of Technology, Sydney); Jan Kautz (NVIDIA) | Recognition: Detection, Categorization, Retrieval | Face, Gesture, and Body Pose ; Representation Learning | Oral | 1.2.1 | 1 | Recognition | |||||
39 | 522 | Unsupervised Person Re-identification by Soft Multilabel Learning | Hong-Xing Yu (Sun Yat-Sen University); WEI-SHI ZHENG (Sun Yat-sen University, China)*; Ancong Wu (Sun Yat-sen University); Xiaowei Guo (Tencent Youtu Lab); Shaogang Gong (Queen Mary University of London); Jian-Huang Lai (Sun Yat-sen University) | Biometrics | Recognition: Detection, Categorization, Retrieval; Video Analytics | Oral | 1.2.1 | 1 | ||||||
40 | 2262 | Learning Context Graph for Person Search | Yichao Yan (Shanghai Jiao Tong University)*; Qiang Zhang (Shanghai Jiao Tong University); Bingbing Ni (Shanghai Jiao Tong University); Wendong Zhang (Shanghai Jiao Tong University); Minghao Xu (Shanghai Jiaotong University); Xiaokang Yang (Shanghai Jiao Tong University of China) | Recognition: Detection, Categorization, Retrieval | Visual Reasoning | Oral | 1.2.1 | 1 | ||||||
41 | 220 | Gradient Matching Generative Networks for Zero-Shot Learning | Mert Bulent Sariyildiz (Bilkent University)*; Ramazan Gokberk Cinbis (METU) | Recognition: Detection, Categorization, Retrieval | Vision + Language | Oral | 1.2.1 | 2 | ||||||
42 | 4499 | Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval | Sounak Dey (Computer Vision Center)*; Pau Riba (Computer Vision Center); Anjan Dutta (Computer Vision Center); Josep Llados ("Computer Vision Center, Barcelona"); Yi-Zhe Song (Queen Mary University of London) | Recognition: Detection, Categorization, Retrieval | Deep Learning ; Vision + Graphics ; Vision Applications and Systems | Oral | 1.2.1 | 2 | ||||||
43 | 5230 | Zero-Shot Task Transfer | Arghya Pal ( Indian Institute of Technology Hyderabad)*; Vineeth N Balasubramanian (Indian Institute of Technology, Hyderabad) | Representation Learning | Computer Vision Theory; Deep Learning ; Optimization Methods; Vision + Graphics ; Vision Application | Oral | 1.2.1 | 2 | ||||||
44 | 906 | C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection | Fang Wan (University of Chinese Academy of Sciences)*; Chang Liu (University of Chinese Academy of Sciences); Wei Ke (University of Chinese Academy of Sciences); Xiangyang Ji (Tsinghua University); Jianbin Jiao (University of Chinese Academy of Sciences); Qixiang Ye (University of Chinese Academy of Sciences, China) | Recognition: Detection, Categorization, Retrieval | Optimization Methods; Others; Statistical Learning; Visual Reasoning | Oral | 1.2.1 | 3 | ||||||
45 | 2973 | Learning Inter-pixel Relations for Weakly Supervised Instance Segmentation | Jiwoon Ahn (DGIST); Sunghyun Cho (DGIST); Suha Kwak (POSTECH)* | Recognition: Detection, Categorization, Retrieval | Segmentation, Grouping and Shape | Oral | 1.2.1 | 3 | ||||||
46 | 3916 | Attention-based Dropout Layer for Weakly Supervised Object Localization | Junsuk Choe (Yonsei University); Hyunjung Shim (Yonsei University)* | Recognition: Detection, Categorization, Retrieval | Deep Learning ; Scene Analysis and Understanding | Oral | 1.2.1 | 3 | ||||||
47 | 1019 | Domain Generalization by Solving Jigsaw Puzzles | Fabio M. Carlucci (Huawei); Antonio D'Innocente (Sapienza Università di Roma); Silvia Bucci (Italian Institute of Technology); Barbara Caputo (IIT); Tatiana Tommasi (Italian Institute of Technology)* | Recognition: Detection, Categorization, Retrieval | Deep Learning | Oral | 1.2.1 | 4 | ||||||
48 | 3628 | Transferrable Prototypical Networks for Unsupervised Domain Adaptation | Yingwei Pan (JD AI Research)*; Ting Yao (JD AI Research); Yehao Li (Sun Yat-Sen University); Yu Wang (JD AI Research); Chong-Wah Ngo (City University of Hong Kong); Tao Mei (AI Research of JD.com) | Recognition: Detection, Categorization, Retrieval | Oral | 1.2.1 | 4 | |||||||
49 | 1182 | Adversarial Meta-Adaptation Network for Blending-target Domain Adaptation | Ziliang Chen (Sun Yat-sen University)*; Jingyu Zhuang (Sun Yat-sen University); Xiaodan Liang (Sun Yat-sen University); Liang Lin (Sun Yat-sen University) | Recognition: Detection, Categorization, Retrieval | Deep Learning | Oral | 1.2.1 | 4 | ||||||
50 | 1113 | ELASTIC: Improving CNNs by Instance Specific Scaling Policies | Huiyu Wang (Johns Hopkins University)*; Aniruddha Kembhavi (Allen Institute for Artificial Intelligence); Ali Farhadi (University of Washington, Allen Institute for Artificial Intelligence); Alan Yuille (Johns Hopkins University); Mohammad Rastegari (Allen Institute for Artificial Intelligence) | Recognition: Detection, Categorization, Retrieval | Deep Learning | Oral | 1.2.1 | 5 | ||||||
51 | 1782 | ScratchDet: Training Single-Shot Object Detectors from Scratch | Rui Zhu (JD AI Research)*; Shifeng Zhang (CBSR, NLPR, CASIA); Xiaobo Wang (JD AI Research); Longyin Wen (JD Digits); Hailin Shi (JD AI Research); Liefeng Bo (JD Finance); Tao Mei (AI Research of JD.com) | Recognition: Detection, Categorization, Retrieval | Deep Learning | Oral | 1.2.1 | 5 | ||||||
52 | 3294 | SFNet: Learning Object-aware Semantic Correspondence | Junghyup Lee (Yonsei University); DOHYUNG KIM (YONSEI UNIVERSITY); Jean Ponce (Inria); Bumsub Ham (Yonsei University)* | Recognition: Detection, Categorization, Retrieval | Motion and Tracking; Scene Analysis and Understanding | Oral | 1.2.1 | 5 | ||||||
53 | 1294 | Deep Metric Learning Beyond Binary Supervision | Sungyoun Kim (POSTECH); Minkyo Seo (POSTECH); Ivan Laptev (INRIA Paris); Minsu Cho (POSTECH); Suha Kwak (POSTECH)* | Recognition: Detection, Categorization, Retrieval | Oral | 1.2.1 | 6 | |||||||
54 | 1510 | Learning to Cluster Faces on an Affinity Graph | Lei Yang (The Chinese University of Hong Kong)*; Xiaohang Zhan (The Chinese University of Hong Kong); Dapeng Chen (Sensetime Group Limited); Junjie Yan (Sensetime Group Limited); Chen Change Loy (Nanyang Technological University); Dahua Lin (The Chinese University of Hong Kong) | Recognition: Detection, Categorization, Retrieval | Deep Learning ; Face, Gesture, and Body Pose ; Segmentation, Grouping and Shape | Oral | 1.2.1 | 6 | ||||||
55 | 1610 | C2AE: Class Conditioned Auto-Encoder for Open-set Recognition | Poojan B Oza (Johns Hopkins University)*; Vishal Patel (Johns Hopkins University) | Recognition: Detection, Categorization, Retrieval | Vision Applications and Systems | Oral | 1.2.1 | 6 | ||||||
56 | 1426 | Shapes and Context: In-the-wild Image Synthesis & Manipulation | Aayush Bansal (Carnegie Mellon University)*; Yaser Sheikh (CMU); Deva Ramanan (Carnegie Mellon University) | Image and Video Synthesis | Big Data, Large Scale Methods ; Segmentation, Grouping and Shape; Vision + Graphics | Oral | 1.2.2 | 1 | Synthesis | |||||
57 | 462 | Semantics Disentangling for Text-to-Image Generation | Guojun Yin (University of Science and Technology of China); Bin Liu (University of Science and Technology of China); Lu Sheng (The Chinese University of Hong Kong)*; Nenghai Yu (University of Science and Technology of China); Xiaogang Wang (Chinese University of Hong Kong, Hong Kong); Jing Shao (Sensetime) | Image and Video Synthesis | Vision + Language | Oral | 1.2.2 | 1 | ||||||
58 | 2072 | Semantic Image Synthesis with Spatially-Adaptive Normalization | Taesung Park (UC Berkeley)*; Ming-Yu Liu (NVIDIA); Ting-Chun Wang (NVIDIA); Jun-Yan Zhu (MIT) | Image and Video Synthesis | Computational Photography; Deep Learning ; Vision + Graphics | Oral | 1.2.2 | 1 | ||||||
59 | 609 | Progressive Pose Attention Transfer for Person Image Generation | Zhen Zhu (Huazhong University of Science and Technology)*; Tengteng Huang (Huazhong University of Science and Technology); Baoguang Shi (Microsoft); Miao Yu (Huazhong University of Science and Technology); Bofei Wang (ZTE Corporation); Xiang Bai (Huazhong University of Science and Technology) | Image and Video Synthesis | Oral | 1.2.2 | 2 | |||||||
60 | 3269 | Unsupervised Person Image Generation with Semantic Parsing Transformation | Sijie Song (Peking University)*; Wei Zhang (JD AI Research); Jiaying Liu (Peking University); Tao Mei (AI Research of JD.com) | Image and Video Synthesis | Vision Applications and Systems | Oral | 1.2.2 | 2 | ||||||
61 | 2439 | DeepView: View synthesis with learned gradient descent | John P Flynn (Google Inc)*; Michael Broxton (Google); Paul E Debevec (Google VR); Graham Fyffe (Google Inc.); Ryan S. Overbeck (Google Inc.); Noah Snavely (Google); Richard Tucker (Google); Matthew DuVall (Google) | Image and Video Synthesis | 3D from Multiview and Sensors; Computational Photography; Deep Learning ; Optimization Methods | Oral | 1.2.2 | 2 | ||||||
62 | 4908 | Animating Arbitrary Objects via Deep Motion Transfer | Aliaksandr Siarohin (University of Trento)*; Stéphane Lathuiliere (university of Trento); Sergey Tulyakov (Snap Inc); Elisa Ricci (FBK - Technologies of Vision); Nicu Sebe (University of Trento) | Image and Video Synthesis | Deep Learning | Oral | 1.2.2 | 3 | ||||||
63 | 5428 | Textured Neural Avatars | Aliaksandra Shysheya (Samsung); Egor Zakharov (Skoltech); Renat Bashirov (Samsung); Igor Pasechnik (Samsung); Egor Burkov (Skoltech); Dmitry Ulyanov (Skoltech); Yury Malkov (Samsung); Karim Iskakov (Samsung); Kara-Ali Aliev (Samsung); Alexey Ivakhnenko (Samsung); Alexander Vakhitov (Samsung AI Research Center); Victor Lempitsky (Samsung)* | Image and Video Synthesis | Deep Learning ; Vision + Graphics ; Vision Applications and Systems | Oral | 1.2.2 | 3 | ||||||
64 | 3190 | IM-Net for High Resolution Video Frame Interpolation | Tomer Peleg (Samsung Israel R&D Center)*; Pablo Szekely (Samsung Israel R&D Center); Doron Sabo (Samsung Israel R&D Center); Omry Sendik (Samsung Israel R&D Center) | Image and Video Synthesis | Datasets and Evaluation; Deep Learning ; Low-level Vision; Motion and Tracking; Vision Applications | Oral | 1.2.2 | 3 | ||||||
65 | 1240 | Homomorphic Latent Space Interpolation for Unpaired Image-to-image Translation | Yingcong Chen (Chinese University of Hong Kong)*; Xiaogang XU (The Chinese University of Hong Kong); Zhuotao Tian (Chinese University of Hong Kong); Jiaya Jia (Chinese University of Hong Kong) | Image and Video Synthesis | Face, Gesture, and Body Pose ; Vision Applications and Systems | Oral | 1.2.2 | 4 | ||||||
66 | 3069 | Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation | Hao Tang (University of Trento)*; Dan Xu (University of Oxford); Yan Yan (Texas State University); Yanzhi Wang (Northeastern University); Jason J Corso (University of Michigan); Nicu Sebe (University of Trento) | Image and Video Synthesis | Oral | 1.2.2 | 4 | |||||||
67 | 4341 | Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping | Huan Fu (The University of Sydney)*; Mingming Gong (University of Pittsburgh); Chaohui Wang (Laboratoire d'Informatique Gaspard Monge, Université Paris-Est); Kayhan Batmanghelich (University of Pittsburgh / Carnegie Mellon University); Kun Zhang (Carnegie Mellon University); Dacheng Tao (University of Sydney) | Image and Video Synthesis | Deep Learning | Oral | 1.2.2 | 4 | ||||||
68 | 3521 | DeepVoxels: Learning Persistent 3D Feature Embeddings | Vincent Sitzmann (Stanford University)*; Justus Thies (Technical University of Munich); Felix Heide (Princeton University); Matthias Niessner (Technical University of Munich); Gordon Wetzstein (Stanford University); Michael Zollhoefer (Stanford University) | Image and Video Synthesis | Deep Learning | Oral | 1.2.2 | 5 | ||||||
69 | 5944 | Inverse Path Tracing for Joint Material and Lighting Estimation | Dejan Azinovic (Technical University of Munich)*; Tzu-Mao Li (MIT CSAIL); Matthias Niessner (Technical University of Munich); Anton Kaplanyan (Facebook Reality Labs) | Image and Video Synthesis | Computational Photography; Computer Vision Theory; Optimization Methods; Scene Analysis and Understa | Oral | 1.2.2 | 5 | ||||||
70 | 4057 | The Visual Centrifuge: Model-Free Layered Video Representations | Jean-Baptiste Alayrac (DeepMind); Joao Carreira (DeepMind)*; Andrew Zisserman (University of Oxford) | Image and Video Synthesis | Computational Photography; Deep Learning ; Representation Learning; Scene Analysis and Understanding | Oral | 1.2.2 | 5 | ||||||
71 | 5720 | Label-Noise Robust Generative Adversarial Networks | Takuhiro Kaneko (The University of Tokyo)*; Yoshitaka Ushiku (The University of Tokyo); Tatsuya Harada (The University of Tokyo) | Image and Video Synthesis | Deep Learning ; Representation Learning | Oral | 1.2.2 | 6 | ||||||
72 | 5766 | DLOW: Domain Flow for Adaptation and Generalization | Wen Li (ETH Zurich)*; RUI GONG (ETH Zurich); Yuhua Chen (ETH Zurich); Luc Van Gool (ETH Zurich) | Image and Video Synthesis | Deep Learning ; Scene Analysis and Understanding | Oral | 1.2.2 | 6 | ||||||
73 | 6970 | CollaGAN: Collaborative GAN for Missing Image Data Imputation | Dongwook Lee (Korea Advanced Institute of Science and Technology)*; Junyoung Kim (Korea Advanced Institute of Science and Technology); Won-Jin Moon (Konkuk University Medical Center); Jong Chul Ye ("Department of Bio and Brain Engineering, KAIST, Korea") | Image and Video Synthesis | Deep Learning ; Face, Gesture, and Body Pose ; Medical, Biological and Cell Microscopy | Oral | 1.2.2 | 6 | ||||||
74 | 6592 | d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding | Xiang Xu (University of Houston); Xiong Zhou (amazon); Ragav Venkatesan (Amazon)*; Orchid Majumder (Amazon); Guru Swaminathan (Amazon) | Recognition: Detection, Categorization, Retrieval | Representation Learning | Oral | 1.2.3 | 1 | Scenes and Representation | |||||
75 | 197 | Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation | Yawei Luo (University of Technology Sydney)*; Liang Zheng (Australian National University); Tao Guan (Huazhong University of Science and Technology); Junqing Yu (Huazhong University of Science & Technology); Yi Yang (University of Technology, Sydney) | Segmentation, Grouping and Shape | Deep Learning | Oral | 1.2.3 | 1 | ||||||
76 | 396 | ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation | Tuan-Hung VU (Valeo.ai)*; Himalaya Jain (Valeo.ai); Maxime Bucher (Valeo.ai); Matthieu Cord (Sorbonne University); Patrick Pérez (Valeo.ai) | Scene Analysis and Understanding | Deep Learning ; Recognition: Detection, Categorization, Retrieval; Segmentation, Grouping and Shape | Oral | 1.2.3 | 1 | ||||||
77 | 325 | Local Feature Augmentation with Cross-Modality Context | Zixin Luo (HKUST)*; Tianwei Shen (HKUST); Lei Zhou (HKUST); Jiahui Zhang (Tsinghua University); Yao Yao (The Hong Kong University of Science and Technology); Shiwei Li (HKUST); Tian Fang (HKUST); Long Quan (Hong Kong University of Science and Technology) | Representation Learning | Low-level Vision | Oral | 1.2.3 | 2 | ||||||
78 | 556 | Large-scale Long-Tailed Recognition in an Open World | Ziwei Liu (The Chinese University of Hong Kong)*; Zhongqi Miao (UC Berkeley); Xiaohang Zhan (The Chinese University of Hong Kong); Jiayun Wang (UC Berkeley / ICSI); Boqing Gong (Tencent AI Lab); Stella X Yu (UC Berkeley / ICSI) | Representation Learning | Recognition: Detection, Categorization, Retrieval | Oral | 1.2.3 | 2 | ||||||
79 | 5137 | AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data | Liheng Zhang (University of Central Florida); Guo-Jun Qi (Huawei Cloud)*; Liqiang Wang (University of Central Florida); Jiebo Luo (University of Rochester) | Representation Learning | Deep Learning | Oral | 1.2.3 | 2 | ||||||
80 | 576 | SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks | René Schuster (DFKI)*; Oliver Wasenmüller (DFKI); Christian Unger (BMW); Didier Stricker (DFKI) | Representation Learning | Deep Learning ; Motion and Tracking; Robotics + Driving; Scene Analysis and Understanding; Vision Ap | Oral | 1.2.3 | 3 | ||||||
81 | 2746 | Learning Correspondence from the Cycle-consistency of Time | Xiaolong Wang (CMU)*; Allan Jabri (UC Berkeley); Alexei A Efros (UC Berkeley) | Representation Learning | Motion and Tracking; Video Analytics | Oral | 1.2.3 | 3 | ||||||
82 | 2131 | AE^2-Nets: Autoencoder in Autoencoder Networks | Changqing Zhang (Tianjin university)*; liu yeqing (Tianjin University ); Huazhu Fu (Inception Institute of Artificial Intelligence) | Representation Learning | Statistical Learning | Oral | 1.2.3 | 3 | ||||||
83 | 1655 | Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach | Proteek Roy (Michigan State University); Vishnu Boddeti (Michigan State University)* | Representation Learning | Optimization Methods; Statistical Learning | Oral | 1.2.3 | 4 | ||||||
84 | 3877 | Learning Spatial Common Sense with Geometry-Aware Recurrent Networks | Hsiao-Yu Tung (Carnegie Mellon University)*; Ricson Cheng (Carnegie Mellon University); Katerina Fragkiadaki (Carnegie Mellon University) | Representation Learning | 3D from Single Image; Recognition: Detection, Categorization, Retrieval; Scene Analysis and Understa | Oral | 1.2.3 | 4 | ||||||
85 | 3147 | Structured Knowledge Distillation for Semantic Segmentation | Yifan Liu (University of Adelaide); Ke Chen (Microsoft); Chris Liu (Microsoft); Zengchang Qin (Intelligent Computing & Machine Learning Lab, School of ASEE, Beihang University); Zhenbo Luo ( Samsung Research Institute China-Beijing); Jingdong Wang (Microsoft Research)* | Segmentation, Grouping and Shape | Scene Analysis and Understanding | Oral | 1.2.3 | 4 | ||||||
86 | 977 | Scan2CAD: Learning CAD Model Alignment in RGB-D Scans | Armen Avetisyan (Technical University of Munich)*; Manuel Dahnert (Technical University of Munich); Angela Dai (Technical University of Munich); Manolis Savva (Simon Fraser University); Angel X Chang (Eloquent Labs); Matthias Niessner (Technical University of Munich) | Scene Analysis and Understanding | Recognition: Detection, Categorization, Retrieval; Vision + Graphics | Oral | 1.2.3 | 5 | ||||||
87 | 2799 | Towards Scene Understanding: Unsupervised Monocular Depth Estimation with Semantic-aware Representation | Po-Yi Chen (National Taiwan University); Alexander H. Liu (National Taiwan University); Yen-Cheng Liu (Georgia Institute of Technology); Yu-Chiang Frank Wang (National Taiwan University)* | Scene Analysis and Understanding | 3D from Single Image; Representation Learning; Robotics + Driving; Segmentation, Grouping and Shape | Oral | 1.2.3 | 5 | ||||||
88 | 3107 | Tell Me Where I Am: Object-level Scene Context Prediction | Xiaotian Qiao (City University of Hong Kong); Quanlong Zheng (City University of HongKong); Ying Cao (City University of Hong Kong)*; Rynson W.H. Lau (City University of Hong Kong) | Scene Analysis and Understanding | Image and Video Synthesis | Oral | 1.2.3 | 5 | ||||||
89 | 1373 | Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation | He Wang (Stanford University); Srinath Sridhar (Stanford University)*; Jingwei Huang (Stanford University); Julien Valentin (Google); Shuran Song (Princeton); Leonidas Guibas (Stanford University) | Scene Analysis and Understanding | 3D from Single Image; Datasets and Evaluation; Deep Learning ; RGBD sensors and analytics; Vision Ap | Oral | 1.2.3 | 6 | ||||||
90 | 2452 | Supervised Fitting of Geometric Primitives to 3D Point Clouds | Lingxiao Li (Stanford University)*; Minhyuk Sung (Stanford University); Anastasia Dubrovina (Stanford); Li Yi (Stanford); Leonidas Guibas (Stanford University) | Segmentation, Grouping and Shape | Deep Learning ; Vision + Graphics | Oral | 1.2.3 | 6 | ||||||
91 | 4225 | Do Better ImageNet Models Transfer Better? | Simon Kornblith (Google)*; Jon Shlens (Google); Quoc Le (Google Brain) | Representation Learning | Deep Learning | Oral | 1.2.3 | 6 | ||||||
92 | 1213 | Learning Video Representations from Correspondence Proposals | Xingyu Liu (Stanford University)*; Joon-Young Lee (Adobe Research); Hailin Jin (Adobe Research) | Deep Learning | Action Recognition ; Representation Learning | Oral | 2.1.1 | 1 | Deep Learning | |||||
93 | 1503 | SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks | Bo Li (SenseTime Group Limited)*; Wei Wu (SenseTime Group Limited); Junjie Yan (Sensetime Group Limited); Qiang Wang (University of Chinese Academy of Sciences); Fangyi Zhang (Institue of Computing Technology); Junliang Xing (Institute of Automation, Chinese Academy of Sciences) | Deep Learning | Motion and Tracking | Oral | 2.1.1 | 1 | ||||||
94 | 2556 | Sphere Generative Adversarial Network Based on Geometric Moment Matching | Sung Woo Park (Chung-Ang Univ., Korea); Junseok Kwon (Chung-Ang Univ., Korea)* | Deep Learning | Image and Video Synthesis | Oral | 2.1.1 | 1 | ||||||
95 | 1431 | Adversarial Attacks Beyond the Image Space | xiaohui zeng (toronto); Chenxi Liu (Johns Hopkins University)*; Yu-Siang Wang (National Taiwan University); Weichao Qiu (Johns Hopkins University); Lingxi Xie (Johns Hopkins University); Yu-Wing Tai (Tencent); Chi-Keung Tang (Hong Kong University of Science and Technology); Alan Yuille (Johns Hopkins University) | Deep Learning | Vision + Graphics | Oral | 2.1.1 | 2 | ||||||
96 | 5297 | Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks | Yinpeng Dong (Tsinghua University)*; Tianyu Pang (Tsinghua University); Hang Su (Tsinghua Univiersity); Jun Zhu (Tsinghua University) | Deep Learning | Recognition: Detection, Categorization, Retrieval | Oral | 2.1.1 | 2 | ||||||
97 | 6129 | Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses | Jérôme Rony (ÉTS Montréal)*; Luiz Gustavo Hafemann (ÉTS Montréal); Luis Eduardo Oliveira (UFPR); Ismail Ben Ayed (ETS Montreal); Robert Sabourin (Canada); Eric Granger (ETS Montreal ) | Deep Learning | Others; Vision Applications and Systems | Oral | 2.1.1 | 2 | ||||||
98 | 1472 | A General and Adaptive Robust Loss Function | Jonathan T Barron (Google Research)* | Deep Learning | 3D from Single Image; Computer Vision Theory; Image and Video Synthesis; Low-level Vision; Statistic | Oral | 2.1.1 | 3 | ||||||
99 | 2677 | Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration | Yang He (University of Technology Sydney)*; Ping Liu (UTS); Ziwei Wang (Information Science Academy, CETC); Zhilan Hu (Huawei); Yi Yang (University of Technology, Sydney) | Deep Learning | Others | Oral | 2.1.1 | 3 | ||||||
100 | 4595 | Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss | Sangil Jung (Samsung)*; Changyong Son (Samsung); Seohyung Lee (Samsung); Jinwoo Son (Samsung); Jae-Joon Han (Samsung); Youngjun Kwak (Samsung); Sung Ju Hwang (KAIST); Changkyu Choi (Samsung) | Deep Learning | Optimization Methods; Vision Applications and Systems | Oral | 2.1.1 | 3 | ||||||
101 | 1773 | Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection | Ruoqi Sun (Shanghai Jiao Tong University)*; Xinge Zhu (The Chinese University of Hong Kong); Chongruo Wu (UC Davis); Chen Huang (Carnegie Mellon University); Jianping Shi (Sensetime Group Limited); Lizhuang Ma (Shanghai Jiao Tong University) | Deep Learning | Low-level Vision; Segmentation, Grouping and Shape | Oral | 2.1.1 | 4 | ||||||
102 | 3740 | Unsupervised Learning of Dense Shape Correspondence | Oshri Halimi (Technion)*; Or Litany (Facebook AI Research); Emanuele Rodola (Sapienza University of Rome); Alex Bronstein (Tel Aviv University, Israel); Ron Kimmel (Technion) | Deep Learning | Computer Vision Theory; Recognition: Detection, Categorization, Retrieval; Segmentation, Grouping an | Oral | 2.1.1 | 4 | ||||||
103 | 6188 | RePr: Improved Training of Convolutional Filters | Aaditya Prakash (Brandeis University)*; James Storer (Brandeis University); Dinei Florencio (Microsoft Research); Cha Zhang (Microsoft Research) | Deep Learning | Oral | 2.1.1 | 4 | |||||||
104 | 2726 | Balanced Self-Paced Learning for Generative Adversarial Clustering Network | Kamran Ghasedi (University of Pittsburgh)*; Xiaoqian Wang (University of Pittsburgh); Cheng Deng (Xidian University); Heng Huang (University of Pittsburgh) | Deep Learning | Oral | 2.1.1 | 5 | |||||||
105 | 2860 | A Style-Based Generator Architecture for Generative Adversarial Networks | Tero Karras (NVIDIA Research)*; Samuli Laine (NVIDIA Research); Timo Aila (NVIDIA Research) | Deep Learning | Image and Video Synthesis; Representation Learning | Oral | 2.1.1 | 5 | ||||||
106 | 5426 | Parallel Optimal Transport GAN | Gil Avraham (Monash University)*; Yan Zuo (Monash University); Tom Drummond (Monash University) | Deep Learning | Optimization Methods; Representation Learning | Oral | 2.1.1 | 5 | ||||||
107 | 1170 | 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans | Ji Hou (Technical University of Munich); Angela Dai (Technical University of Munich); Matthias Niessner (Technical University of Munich)* | 3D from Multiview and Sensors | Segmentation, Grouping and Shape; Vision Applications and Systems | Oral | 2.1.2 | 1 | 3D Single View & RGBD | |||||
108 | 4854 | Causes and Corrections for Bimodal Multipath Scanning with Structured Light | yu zhang (Nanjing University )*; Daniel Lau (University of Kentucky); Ying Yu (University of Kentucky) | 3D from Multiview and Sensors | RGBD sensors and analytics | Oral | 2.1.2 | 1 | ||||||
109 | 7048 | TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes | Jingwei Huang (Stanford University)*; Haotian Zhang (Stanford University); Li Yi (Stanford); Thomas Funkhouser (Princeton University and Google, Inc.); Matthias Niessner (Technical University of Munich); Leonidas Guibas (Stanford University) | RGBD sensors and analytics | 3D from Multiview and Sensors; Deep Learning ; Vision + Graphics | Oral | 2.1.2 | 1 | ||||||
110 | 704 | PlaneRCNN: 3D Plane Detection and Reconstruction from a Single View | Chen Liu (Washington University in St. Louis)*; Kihwan Kim (NVIDIA); Jinwei Gu (NVIDIA); Yasutaka Furukawa (Simon Fraser University); Jan Kautz (NVIDIA) | 3D from Multiview and Sensors | 3D from Single Image; Scene Analysis and Understanding | Oral | 2.1.2 | 2 | ||||||
111 | 3976 | Occupancy Networks: Learning 3D Reconstruction in Function Space | Lars M Mescheder (MPI-IS and University of Tuebingen)*; Michael Oechsle (MPI-IS, University of Tuebingen and ETAS GmbH); Michael Niemeyer (MPI-IS and University of Tuebingen); Sebastian Nowozin (Google AI Berlin); Andreas Geiger (MPI-IS and University of Tuebingen) | 3D from Single Image | Deep Learning | Oral | 2.1.2 | 2 | ||||||
112 | 2575 | 3D Shape Reconstruction from Images in the Frequency Domain | Weichao Shen (Beijing Institute of Technology)*; Yuwei WU (Beijing Institute of Technology (BIT), China); Yunde Jia (Beijing Institute of Technology) | 3D from Single Image | Oral | 2.1.2 | 2 | |||||||
113 | 1456 | SiCloPe: Silhouette-based Clothed People | Ryota Natsume (Waseda University); Shunsuke Saito (University of Southern California)*; Zeng Huang (University of Southern California); Weikai Chen (USC Institute for Creative Technology); Chongyang Ma (Kwai Inc.); Shigeo Morishima (Waseda Research Institute for Science and Engineering); Hao Li (Pinscreen/University of Southern California/USC ICT) | 3D from Single Image | Face, Gesture, and Body Pose | Oral | 2.1.2 | 3 | ||||||
114 | 3102 | Detailed Human Shape Estimation from a Single Image by Hierarchical Mesh Deformation | Hao Zhu (Nanjing University)*; Xinxin Zuo (University of Kentucky); Sen Wang (Northwestern Polytechnical University); Xun Cao (Nanjing University); Ruigang Yang (University of Kentucky, USA) | 3D from Single Image | Deep Learning | Oral | 2.1.2 | 3 | ||||||
115 | 4841 | Convolutional Mesh Regression for Single-Image Human Shape Reconstruction | Nikos Kolotouros (University of Pennsylvania)*; Georgios Pavlakos (University of Pennsylvania); Kostas Daniilidis (University of Pennsylvania) | 3D from Single Image | Face, Gesture, and Body Pose | Oral | 2.1.2 | 3 | ||||||
116 | 2754 | H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions | Bugra Tekin (Microsoft)*; Federica Bogo (Microsoft); Marc Pollefeys (ETH Zurich / Microsoft) | 3D from Single Image | Face, Gesture, and Body Pose ; Recognition: Detection, Categorization, Retrieval | Oral | 2.1.2 | 4 | ||||||
117 | 3419 | Learning the Depths of Moving People by Watching Frozen People | Zhengqi Li (Cornell University)*; Tali Dekel (Google); Forrester Cole (Google Research); Richard Tucker (Google); Ce Liu (Google); Bill Freeman (Google); Noah Snavely (Cornell University and Google AI) | 3D from Single Image | 3D from Multiview and Sensors; Deep Learning | Oral | 2.1.2 | 4 | ||||||
118 | 3439 | Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion | Zhenpei Yang (The University of Texas at Austin); Kristen Grauman (Facebook AI Research & UT Austin); Qixing Huang (The University of Texas at Austin)*; Linjie Luo (Snap Inc); Xiaowei Zhou (Zhejiang Univ., China); Jeffrey Pan (Austin, Texas) | 3D from Single Image | 3D from Multiview and Sensors; Deep Learning ; Optimization Methods; RGBD sensors and analytics | Oral | 2.1.2 | 4 | ||||||
119 | 1943 | A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Images | Jiapeng Tang (South China University of Technology); Xiaoguang Han (Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong (Shenzhen))*; Junyi Pan (South China University of Technology); Kui Jia (South China University of Technology); Xin Tong (Microsoft) | 3D from Single Image | Deep Learning | Oral | 2.1.2 | 5 | ||||||
120 | 3451 | Structure-And-Motion-Aware Rolling Shutter Correction | Bingbing Zhuang (NUS)*; Quoc-Huy Tran (NEC Labs America); Pan Ji (NEC Labs); Loong Fah Cheong (NUS); Manmohan Chandraker (NEC Labs America) | 3D from Single Image | 3D from Multiview and Sensors | Oral | 2.1.2 | 5 | ||||||
121 | 3871 | PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation | sida peng (Zhejiang University); Yuan Liu (Zhejiang University); Qixing Huang (The University of Texas at Austin); Hujun Bao (Zhejiang University); Xiaowei Zhou (Zhejiang Univ., China)* | 3D from Single Image | Robotics + Driving; Scene Analysis and Understanding | Oral | 2.1.2 | 5 | ||||||
122 | 236 | Learning Optical Flow with Occlusion Hallucination | Pengpeng Liu (The Chinese University of Hong Kong)*; Michael Lyu (The Chinese University of Hong Kong); Irwin King (The Chinese University of Hong Kong); Jia Xu (Tencent AI Lab) | Motion and Tracking | Deep Learning | Oral | 2.1.3 | 1 | Motion & Biometrics | |||||
123 | 3963 | Taking a Deeper Look at the Inverse Compositional Algorithm | Zhaoyang Lv (GEORGIA TECH)*; Frank Dellaert (Georgia Tech); James Rehg (Georgia Institute of Technology); Andreas Geiger (MPI-IS and University of Tuebingen) | Motion and Tracking | 3D from Multiview and Sensors; Optimization Methods | Oral | 2.1.3 | 1 | ||||||
124 | 1197 | Deeper and Wider Siamese Networks for Real-Time Visual Tracking | Zhipeng Zhang (Chinese Academy of Sciences); Houwen Peng (Microsoft Research)* | Motion and Tracking | Oral | 2.1.3 | 1 | |||||||
125 | 952 | High Fidelity Facial Performance Tracking In-the-wild | Jae Shin Yoon (University of Minnestoa)*; Takaaki Shiratori (Facebook Reality Labs); Shoou-I Yu (Oculus Research Pittsburgh); Hyun Soo Park (The University of Minnesota) | Face, Gesture, and Body Pose | 3D from Single Image; Motion and Tracking | Oral | 2.1.3 | 2 | ||||||
126 | 2738 | Diverse Generation for Multi-agent Sports Games | Raymond A Yeh (UIUC)*; Alexander Schwing (UIUC); Jonathan Huang (Google); Kevin Murphy (Google) | Motion and Tracking | Deep Learning | Oral | 2.1.3 | 2 | ||||||
127 | 3444 | Efficient Online Multi-Person 2D Pose Tracking with Recurrent Spatio-Temporal Affinity Fields | Yaadhav Raaj (CMU)*; Haroon Idrees (Carnegie Mellon University); Gines Hidalgo Martinez (Carnegie Mellon University); Yaser Sheikh (CMU) | Face, Gesture, and Body Pose | Action Recognition ; Motion and Tracking; Video Analytics | Oral | 2.1.3 | 2 | ||||||
128 | 1391 | GFrames: Gradient-Based Local Reference Frame for 3D Shape Matching | Simone Melzi (University of Verona)*; Riccardo Spezialetti (Universita' degli studi di Bologna); Federico Tombari (Technical University of Munich, Germany); Michael Bronstein (Università della Svizzera Italiana); Luigi Di Stefano (University of Bologna); Emanuele Rodola (Sapienza University of Rome) | Motion and Tracking | 3D from Multiview and Sensors; Low-level Vision; Recognition: Detection, Categorization, Retrieval; | Oral | 2.1.3 | 3 | ||||||
129 | 6191 | Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking | Pascal Fua (EPFL, Switzerland); Andrii Maksai (EPFL)* | Motion and Tracking | Deep Learning | Oral | 2.1.3 | 3 | ||||||
130 | 3119 | Graph Convolutional Tracking | Junyu Gao (National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences)*; Tianzhu Zhang (CAS, China); Changsheng Xu (CASIA) | Motion and Tracking | Oral | 2.1.3 | 3 | |||||||
131 | 4984 | ATOM: Accurate Tracking by Overlap Maximization | Martin Danelljan (ETH Zurich)*; Goutam Bhat (ETH Zurich); Fahad Shahbaz Khan (Inception Institute of Artificial Intelligence); Michael Felsberg (Linköping University) | Motion and Tracking | Oral | 2.1.3 | 4 | |||||||
132 | 1202 | Visual Tracking via Adaptive Spatially-Regularized Correlation Filters | Kenan Dai (Dalian University of Technology); Dong Wang (Dalian University of Technology)*; Huchuan Lu (Dalian University of Technology); Chong Sun (Dalian University of Technology); Jianhua Li (Dalian University of Technology) | Motion and Tracking | Oral | 2.1.3 | 4 | |||||||
133 | 496 | Deep Tree Learning for Zero-shot Face Anti-Spoofing | Yaojie Liu (Michigan State University)*; Joel Stehouwer (Michigan State University); Amin Jourabloo (Michigan State University); Xiaoming Liu (Michigan State University) | Biometrics | Face, Gesture, and Body Pose | Oral | 2.1.3 | 4 | ||||||
134 | 1140 | ArcFace: Additive Angular Margin Loss for Deep Face Recognition | Jiankang Deng (Imperial College London)*; Jia Guo (DeepInsight); Niannan Xue (Imperial College London); Stefanos Zafeiriou (Imperial College Londong) | Biometrics | Face, Gesture, and Body Pose ; Recognition: Detection, Categorization, Retrieval | Oral | 2.1.3 | 5 | ||||||
135 | 1617 | Learning Joint Unique-Gait and Cross-Gait Representation by Minimizing Quintuplet Loss | Kaihao Zhang (Australian National University)*; Wenhan Luo (Tencent AI Lab); Lin Ma (Tencent AI Lab); Wei Liu (Tencent); HONGDONG LI (Australian National University, Australia) | Biometrics | Oral | 2.1.3 | 5 | |||||||
136 | 4898 | Gait Recognition via Disentangled Representation Learning | Ziyuan Zhang (Michigan State University)*; Luan Tran (Michigan State University); Xi Yin (Microsoft Could & AI); Yousef A Atoum (Yarmouk University); Xiaoming Liu (Michigan State University); Nanxin Wang (Ford Motor Company); Jian Wan (Ford Motor Company) | Biometrics | Face, Gesture, and Body Pose ; Representation Learning; Vision Applications and Systems | Oral | 2.1.3 | 5 | ||||||
137 | 1462 | Panoptic Feature Pyramid Network | Alexander Kirillov (Facebook AI Reserach)*; Kaiming He (Facebook AI Research); Ross Girshick (FAIR); Piotr Dollar (FAIR) | Recognition: Detection, Categorization, Retrieval | Segmentation, Grouping and Shape | Oral | 2.2.1 | 1 | Recognition | |||||
138 | 2705 | Mask Scoring R-CNN | Zhaojin Huang (Huazhong University of Science and Technology); Lichao Huang (Horizon Robotics); Yongchao Gong (Horizon Robotics ); Chang Huang (Horizon Robotics); Xinggang Wang (Huazhong Univ. of Science and Technology)* | Recognition: Detection, Categorization, Retrieval | Oral | 2.2.1 | 1 | |||||||
139 | 3864 | Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection | Hang Xu (Huawei Noah's Ark Lab); ChenHan Jiang (Sun Yat-sen University); Xiaodan Liang (Sun Yat-sen University)*; Liang Lin (Sun Yat-sen University); Zhenguo Li (Huawei Noah's Ark Lab) | Recognition: Detection, Categorization, Retrieval | Deep Learning ; Visual Reasoning | Oral | 2.2.1 | 1 | ||||||
140 | 1476 | Cross-Modality Personalization for Retrieval | Nils Murrugarra-Llerena (University of Pittsburgh)*; Adriana Kovashka (University of Pittsburgh) | Recognition: Detection, Categorization, Retrieval | Datasets and Evaluation; Vision + Language | Oral | 2.2.1 | 2 | ||||||
141 | 2623 | Composing Text and Image for Image Retrieval - An Empirical Odyssey | Nam Vo (Georgia Institute of Technology)*; Lu Jiang (Google); Chen Sun (Google); Kevin Murphy (Google); Li-Jia Li (Stanford); Li Fei-Fei (Stanford University); James Hays (Georgia Institute of Technology, USA) | Recognition: Detection, Categorization, Retrieval | Datasets and Evaluation; Representation Learning; Vision + Language | Oral | 2.2.1 | 2 | ||||||
142 | 3524 | Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation | Xiaobing Wang (Samsung Research Institute China-Beijing)*; yingying jiang ( Samsung Research China,Beijing); Zhenbo Luo ( Samsung Research Institute China-Beijing); Cheng-lin Liu (Institute of Automation of Chinese Academy of Sciences); Hyunsoo Choi (SAMSUNG ELECTRONICS CO.,LTD); Sungjin Kim (SAMSUNG ELECTRONICS CO.,LTD) | Recognition: Detection, Categorization, Retrieval | Vision Applications and Systems | Oral | 2.2.1 | 2 | ||||||
143 | 2657 | Adaptive NMS: Refining Pedestrian Detection in a Crowd | Songtao Liu (BUAA); Di Huang (Beihang University, China)*; Yunhong Wang (State Key Laboratory of Virtual Reality Technology and System, Beihang University, Beijing 100191, China) | Recognition: Detection, Categorization, Retrieval | Face, Gesture, and Body Pose | Oral | 2.2.1 | 3 | ||||||
144 | 3517 | Point in, Box out: Beyond Counting Persons in Crowds | yuting liu (sichuan university)*; Miaojing Shi (Inria Rennes); Qijun Zhao (Sichuan University); Xiaofang Wang (Inria Rennes) | Recognition: Detection, Categorization, Retrieval | Oral | 2.2.1 | 3 | |||||||
145 | 6264 | Locating Objects Without Bounding Boxes | Javier Ribera (Purdue University)*; David Güera (Purdue University); Yuhao Chen (Purdue University); Edward Delp (Purdue University) | Recognition: Detection, Categorization, Retrieval | Oral | 2.2.1 | 3 | |||||||
146 | 3333 | FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery | Krishna Kumar Singh (University of California Davis)*; Utkarsh Ojha (University of California, Davis); Yong Jae Lee (University of California, Davis) | Recognition: Detection, Categorization, Retrieval | Image and Video Synthesis | Oral | 2.2.1 | 4 | ||||||
147 | 3505 | Mutual Learning of Complementary Networks via Residual Correction for Improving Semi-Supervised Classification | Si Wu (South China University of Technology)*; Jichang Li (South China University of Technology); Cheng Liu (City University of Hong Kong); Zhiwen Yu (South China University of Technology); Hau San Wong (City University of Hong Kong) | Recognition: Detection, Categorization, Retrieval | Deep Learning | Oral | 2.2.1 | 4 | ||||||
148 | 4012 | Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects | Yusuke Niitani (Preferred Networks, Inc.)*; Takuya Akiba (Preferred Networks, Inc.); Tommi Kerola (Preferred Networks, Inc.); Toru Ogawa (Preferred Networks, Inc.); Shotaro Sano (Preferred Networks, Inc.); Shuji Suzuki (Preferred Networks, Inc.) | Recognition: Detection, Categorization, Retrieval | Deep Learning | Oral | 2.2.1 | 4 | ||||||
149 | 4099 | Curls & Whey: Boosting Black-Box Adversarial Attacks | Yucheng Shi (Tianjin University); Siyu Wang (Tianjin University); Yahong Han (Tianjin University)* | Recognition: Detection, Categorization, Retrieval | Vision Applications and Systems | Oral | 2.2.1 | 5 | ||||||
150 | 5988 | Barrage of Random Transforms for Adversarially Robust Defense | Edward Raff (Booz Allen Hamilton)*; Jared Sylvester (Booz Allen Hamilton); Steven Forsyth (Nvidia); Mark McLean (Laboratory for Physical Sciences) | Recognition: Detection, Categorization, Retrieval | Big Data, Large Scale Methods ; Deep Learning | Oral | 2.2.1 | 5 | ||||||
151 | 4648 | Aggregation Cross-Entropy for Sequence Recognition | Zecheng Xie (South China University of Technology); Yaoxiong Huang (South China University of Technology); Yuanzhi Zhu (South China University of Technology); Lianwen Jin (South China University of Technology)*; Yuliang Liu (South China University of Technology); Lele Xie (South China University of Technology) | Recognition: Detection, Categorization, Retrieval | Deep Learning ; Document Analysis ; Statistical Learning; Vision Applications and Systems | Oral | 2.2.1 | 5 | ||||||
152 | 4674 | LaSO: Label-Set Operations networks for multi-label few-shot learning | Amit Alfassy (IBM-Research); Leonid Karlinsky (IBM-Research)*; Amit Aides (IBM); Joseph Shtok (IBM-Reseach); Sivan Harary (IBM-Research); Rogerio Feris (IBM Research AI, MIT-IBM Watson AI Lab); Raja Giryes (Tel Aviv University); Alex Bronstein (Technion) | Recognition: Detection, Categorization, Retrieval | Deep Learning | Oral | 2.2.1 | 6 | ||||||
153 | 5352 | Few-Shot Learning with Localization in Realistic Settings | Davis Wertheimer (Cornell)*; Bharath Hariharan (Cornell University) | Recognition: Detection, Categorization, Retrieval | Deep Learning ; Segmentation, Grouping and Shape | Oral | 2.2.1 | 6 | ||||||
154 | 5575 | AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs | Massimiliano Mancini (Sapienza University of Rome)*; Samuel Rota Bulò (Mapillary Research); Barbara Caputo (IIT); Elisa Ricci (FBK - Technologies of Vision) | Recognition: Detection, Categorization, Retrieval | Deep Learning | Oral | 2.2.1 | 6 | ||||||
155 | 12 | Grounded Video Description | Luowei Zhou (University of Michigan)*; Yannis Kalantidis (Facebook Research); Xinlei Chen (Facebook AI Research); Jason J Corso (University of Michigan); Marcus Rohrbach (Facebook AI Research) | Vision + Language | Oral | 2.2.2 | 1 | Language & Reasoning | ||||||
156 | 3566 | Streamlined Dense Video Captioning | Jonghwan Mun (POSTECH)*; Linjie Yang (ByteDance AI Lab); Zhou Ren (Snap Inc.); Ning Xu (Snap); Bohyung Han (Seoul National University) | Vision + Language | Deep Learning ; Video Analytics | Oral | 2.2.2 | 1 | ||||||
157 | 5612 | Adversarial Inference for Multi-Sentence Video Description | Jae Sung Park (UC Berkeley); Marcus Rohrbach (Facebook AI Research); Trevor Darrell (UC Berkeley); Anna Rohrbach (UC Berkeley)* | Vision + Language | Oral | 2.2.2 | 1 | |||||||
158 | 4705 | Unified Visual-Semantic Emebddings: Bridging Vision and Language with Structured Meaning Representations | Hao Wu (Fudan University)*; Jiayuan Mao (Tsinghua University); Yufeng Zhang (Fudan University); Weiwei Sun (" Fudan University, China"); Yuning Jiang (Bytedance); Lei Li (ByteDance AI Lab); Weiying Ma (Bytedance) | Vision + Language | Representation Learning | Oral | 2.2.2 | 2 | ||||||
159 | 3640 | Learning to Compose Dynamic Tree Structures for Visual Contexts | Kaihua Tang (Nanyang Technological University)*; Hanwang Zhang (Nanyang Technological University); Baoyuan Wu (Tencent AI Lab); Wenhan Luo (Tencent AI Lab); Wei Liu (Tencent) | Vision + Language | Scene Analysis and Understanding; Visual Reasoning | Oral | 2.2.2 | 2 | ||||||
160 | 5104 | Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation | Xin Wang (University of California, Santa Barbara)*; Qiuyuan Huang (Microsoft Research AI); Asli Celikyilmaz (Microsoft Research AI); Jianfeng Gao (Microsoft Research); Dinghan Shen (Duke University); Yuan-Fang Wang (UC Santa Barbara); William Yang Wang (UC Santa Barbara); Lei Zhang (Microsoft Research) | Vision + Language | Robotics + Driving; Vision Applications and Systems; Visual Reasoning | Oral | 2.2.2 | 2 | ||||||
161 | 1824 | Dynamic Fusion with Intra- and Inter-modality Attention Flow for Visual Question Answering | gao peng (Chinese university of hong kong)*; Hongsheng Li (Chinese University of Hong Kong); Haoxuan You (Tsinghua University); Zhengkai Jiang (Institute of Automation,Chinese Academy of Sciences); Pan Lu (Tsinghua University); Steven Hoi (SMU); Xiaogang Wang (Chinese University of Hong Kong, Hong Kong) | Vision + Language | Vision Applications and Systems; Visual Reasoning | Oral | 2.2.2 | 3 | ||||||
162 | 3454 | Cycle-Consistency for Robust Visual Question Answering | Meet Shah (Facebook AI Research)*; Xinlei Chen (Facebook AI Research); Marcus Rohrbach (Facebook AI Research); Devi Parikh (Georgia Tech & Facebook AI Research) | Vision + Language | Oral | 2.2.2 | 3 | |||||||
163 | 135 | Embodied Question Answering in Photorealistic Environments with Point Cloud Perception | Erik Wijmans (Georgia Tech)*; Samyak Datta (Georgia Tech); Oleksandr Maksymets (Facebook AI Research); Abhishek Das (Georgia Tech); Georgia Gkioxari (Facebook); Stefan Lee (Georgia Institute of Technology); Irfan Essa (Georgia Institute of Technology); Dhruv Batra (Georgia Tech & Facebook AI Research); Devi Parikh (Georgia Tech & Facebook AI Research) | Vision + Language | Oral | 2.2.2 | 3 | |||||||
164 | 3909 | Reasoning Visual Dialogs with Structural and Partial Observations | Zilong Zheng (UCLA); Wenguan Wang (Inception Institute of Artificial Intelligence)*; Siyuan Qi (UCLA); Song-Chun Zhu (UCLA) | Vision + Language | Oral | 2.2.2 | 4 | |||||||
165 | 3129 | Recursive Visual Attention in Visual Dialog | Yulei Niu (Renmin University of China); Manli Zhang (Renmin University of China); Jianhong Zhang (Renmin University of China); Zhiwu Lu (Renmin University of China)*; Ji-Rong Wen (Renmin University of China); Hanwang Zhang (Nanyang Technological University) | Vision + Language | Visual Reasoning | Oral | 2.2.2 | 4 | ||||||
166 | 3820 | Two Body Problem: Collaborative Visual Task Completion | Unnat Jain (UIUC)*; Luca Weihs (Allen Institute for Artificial Intelligence); Eric Kolve (Allen AI); Mohammad Rastegari (Allen Institute for Artificial Intelligence); Svetlana Lazebnik (UIUC); Ali Farhadi (University of Washington, Allen Institute for Artificial Intelligence); Alexander Schwing (UIUC); Aniruddha Kembhavi (Allen Institute for Artificial Intelligence) | Visual Reasoning | Recognition: Detection, Categorization, Retrieval; Scene Analysis and Understanding | Oral | 2.2.2 | 4 | ||||||
167 | 7021 | GQA: a new dataset for compositional question answering over real-world images | Drew A Hudson (Stanford University)*; Chris Manning (Stanford) | Visual Reasoning | Datasets and Evaluation; Deep Learning ; Scene Analysis and Understanding; Vision + Language | Oral | 2.2.2 | 5 | ||||||
168 | 1530 | Text2Scene: Generating Compositional Scenes from Textual Descriptions | Fuwen Tan (University of Virginia)*; Song Feng (IBM Research); Vicente Ordonez (University of Virginia) | Vision + Language | Image and Video Synthesis | Oral | 2.2.2 | 5 | ||||||
169 | 5126 | From Recognition to Cognition: Visual Commonsense Reasoning | Rowan Zellers (University of Washington)*; Yonatan Bisk (University of Washington); Ali Farhadi (University of Washington, Allen Institute for Artificial Intelligence); Yejin Choi (University of Washington) | Vision + Language | Recognition: Detection, Categorization, Retrieval; Scene Analysis and Understanding; Visual Reasonin | Oral | 2.2.2 | 5 | ||||||
170 | 3587 | The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation | Chih-Yao Ma (Georgia Institute of Technology)*; Zuxuan Wu (UMD); Ghassan AlRegib (Georgia Institute of Technology �); Caiming Xiong (Salesforce Research); Zsolt Kira (Georgia Institute of Technology) | Vision + Language | Visual Reasoning | Oral | 2.2.2 | 6 | ||||||
171 | 6287 | Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation | Liyiming Ke (University of Washington); Xiujun Li (Microsoft Research)*; Yonatan Bisk (University of Washington); Ari Holtzman (University of Washington); Zhe Gan (Microsoft); Jingjing Liu (Microsoft); Jianfeng Gao (Microsoft Research); Yejin Choi (University of Washington); Siddhartha Srinivasa (University of Washington) | Vision + Language | Robotics + Driving | Oral | 2.2.2 | 6 | ||||||
172 | 1770 | Learning to Learn How to Learn: Self-Adaptive Visual Navigation using Meta-Learning | Mitchell N Wortsman (Allen Institute for Artificial Intelligence); Kiana Ehsani (University of Washington); Mohammad Rastegari (Allen Institute for Artificial Intelligence); Ali Farhadi (University of Washington, Allen Institute for Artificial Intelligence); Roozbeh Mottaghi (Allen Institute for AI)* | Visual Reasoning | Scene Analysis and Understanding; Statistical Learning | Oral | 2.2.2 | 6 | ||||||
173 | 1064 | From One Photon to a Billion: High Flux Imaging with Single-Photon Sensors | Atul N Ingle (University of Wisconsin-Madison)*; Andreas Velten (University of Wisconsin - Madison); Mohit Gupta ("University of Wisconsin-Madison, USA ") | Computational Photography | Physics-based Vision and Shape-from-X | Oral | 2.2.3 | 1 | Comp. Photography & Graphics | |||||
174 | 1624 | Photon-Flooded Single-Photon 3D Cameras | Anant Gupta (University of Wisconsin Madison)*; Atul N Ingle (University of Wisconsin-Madison); Andreas Velten (University of Wisconsin - Madison); Mohit Gupta ("University of Wisconsin-Madison, USA ") | Computational Photography | Physics-based Vision and Shape-from-X | Oral | 2.2.3 | 1 | ||||||
175 | 2059 | Acoustic Non-Line-of-Sight Imaging | David Lindell (Stanford University)*; Gordon Wetzstein (Stanford University); Vladlen Koltun (Intel Labs) | Computational Photography | Oral | 2.2.3 | 1 | |||||||
176 | 3310 | Steady-state Non-Line-of-Sight Imaging | Wenzheng Chen (University of Toronto); Simon Daneau (Algolux)*; Colin Brosseau (Algolux); Felix Heide (Princeton University) | Computational Photography | Low-level Vision; Physics-based Vision and Shape-from-X | Oral | 2.2.3 | 2 | ||||||
177 | 2427 | A Theory of Fermat Paths for Non-Line-of-Sight Shape Reconstruction | Shumian Xin (Carnegie Mellon University); Sotiris Nousias (University College London); Kyros Kutulakos (University of Toronto); Aswin Sankaranarayanan (Carnegie Mellon University); Srinivasa G Narasimhan (Carnegie Mellon University); Ioannis Gkioulekas (Carnegie Mellon University)* | Computational Photography | Physics-based Vision and Shape-from-X | Oral | 2.2.3 | 2 | ||||||
178 | 474 | End-to-end Projector Photometric Compensation | Bingyao Huang (Temple University)*; Haibin Ling (Temple University) | Vision + Graphics | Computational Photography; Deep Learning ; Others | Oral | 2.2.3 | 2 | ||||||
179 | 2605 | Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera | Liyuan Pan (The Australian National University)*; cedric scheerlinck (The Australian National University); RICHARD HARTLEY (Australian National University, Australia); Miaomiao Liu (The Australian National University); Yuchao Dai (Northwestern Polytechnical University); Xin Yu (Australian National University) | Computational Photography | Image and Video Synthesis; Vision Applications and Systems | Oral | 2.2.3 | 3 | ||||||
180 | 5932 | Bringing Alive Blurred Moments! | Kuldeep Purohit (Indian Institute of Technology Madras)*; Anshul Shah (University of Maryland, College Park); Rajagopalan N Ambasamudram (Indian Institute of Technology Madras) | Computational Photography | Low-level Vision | Oral | 2.2.3 | 3 | ||||||
181 | 1607 | Learning to Synthesize Motion Blur | Tim Brooks (Google)*; Jonathan T Barron (Google Research) | Computational Photography | Deep Learning ; Image and Video Synthesis; Motion and Tracking; Vision + Graphics | Oral | 2.2.3 | 3 | ||||||
182 | 2861 | Underexposed Photo Enhancement using Deep Illumination Estimation | Ruixing Wang (The Chinese University of Hong Kong); Qing Zhang ( Sun Yat-sen University); Chi-Wing Fu (The Chinese University of Hong Kong); Xiaoyong Shen (Tencent); WEI-SHI ZHENG (Sun Yat-sen University, China)*; Jiaya Jia (Chinese University of Hong Kong) | Computational Photography | Deep Learning ; Low-level Vision | Oral | 2.2.3 | 4 | ||||||
183 | 2843 | Blind Visual Motif Removal from a Single Image | Amir Hertz (Tel Aviv University)*; Sharon Fogel (Tel-Aviv university); Rana Hanocka (TAU); Raja Giryes (Tel Aviv University); Danny Cohen-Or (Tel Aviv University) | Vision + Graphics | Deep Learning | Oral | 2.2.3 | 4 | ||||||
184 | 6541 | Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising | Wei He (RIKEN AIP)*; Quanming Yao (4Paradigm); Chao Li (RIKEN); Naoto Yokoya (RIKEN Center for Advanced Intelligence Project (AIP)); Qibin Zhao (RIKEN) | Low-level Vision | Vision + Graphics | Oral | 2.2.3 | 4 | ||||||
185 | 4943 | Total Scene Capture: Neural Rerendering in the Wild | Moustafa Meshry (University of Maryland)*; Ricardo Martin-Brualla (Google); Noah Snavely (Cornell University and Google AI); Hugues Hoppe (Google Inc.); Sameh Khamis (Google); Rohit Pandey (Google); Dan B Goldman (Google, Inc.) | Vision + Graphics | 3D from Multiview and Sensors; Image and Video Synthesis | Oral | 2.2.3 | 5 | ||||||
186 | 430 | GeoNet: Deep Geodesic Networks for Point Cloud Analysis | Tong He (UCLA)*; Haibin Huang (Face++ (Megvii)); Li Yi (Stanford); Yuqian Zhou (UIUC); QIHAO WU (Face++ (Megvii)); jue wang (Face++ (Megvii)); Stefano Soatto (UCLA) | Vision + Graphics | Deep Learning ; Representation Learning; Segmentation, Grouping and Shape | Oral | 2.2.3 | 5 | ||||||
187 | 2440 | MeshAdv: Adversarial Meshes for Visual Recognition | CHAOWEI XIAO (University of Michigan, Ann Arbor); Dawei Yang (University of Michigan, Ann Arbor)*; Bo Li (University of Illinois at Urbana–Champaign); Jia Deng (Princeton University); mingyan liu (university of Michigan, Ann Arbor) | Vision + Graphics | Deep Learning | Oral | 2.2.3 | 5 | ||||||
188 | 4701 | Fast Spatially-Varying Indoor Lighting Estimation | Mathieu Garon (Université Laval); Kalyan Sunkavalli (Adobe Research); Nathan Carr (Adobe); Sunil Hadap (Adobe); Jean-Francois Lalonde (Université Laval)* | Vision + Graphics | Computational Photography; Deep Learning | Oral | 2.2.3 | 6 | ||||||
189 | 1188 | Neural Illumination: Lighting Prediction for Indoor Environments | Shuran Song (Princeton)*; Thomas Funkhouser (Princeton University and Google, Inc.) | Vision + Graphics | 3D from Single Image; Scene Analysis and Understanding | Oral | 2.2.3 | 6 | ||||||
190 | 4363 | Deep Sky Modeling for Single Image Outdoor Lighting Estimation | Yannick Hold-Geoffroy (Adobe Research)*; Akshaya Athwale (Indian Institute of Technology Dhanbad); Jean-Francois Lalonde (Université Laval) | Vision + Graphics | Computational Photography; Deep Learning ; Scene Analysis and Understanding | Oral | 2.2.3 | 6 | ||||||
191 | 2180 | Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning from Radiology Reports and Label Ontology | Ke Yan (National Institutes of Health)*; Yifan Peng (NIH); Veit Sanfort (NIH); Mohammadhadi Bagheri (National Institutes of Health); Zhiyong Lu (NLM/NCBI/NIH); Ronald Summers (National Institutes of Health, Bethesda, Maryland, United States) | Medical, Biological and Cell Microscopy | Datasets and Evaluation; Deep Learning ; Recognition: Detection, Categorization, Retrieval; Represen | Oral | 3.1.1 | 1 | Applications | |||||
192 | 4246 | Robust Histopathology Image Analysis: to Label or to Synthesize? | Le Hou (Stony Brook University)*; Ayush Agarwal (Stanford University); Dimitris Samaras (Stony Brook University); Tahsin Kurc (Stony Brook University); Rajarsi Gupta (Stony Brook University); Joel Saltz (Stony Brook University) | Medical, Biological and Cell Microscopy | Segmentation, Grouping and Shape; Vision Applications and Systems | Oral | 3.1.1 | 1 | ||||||
193 | 6477 | Data augmentation with spatial and appearance transforms for one-shot medical image segmentation | Amy Zhao (MIT)*; Guha Balakrishnan (MIT); Fredo Durand (MIT); John Guttag (MIT); Adrian V Dalca (MIT) | Medical, Biological and Cell Microscopy | Image and Video Synthesis | Oral | 3.1.1 | 1 | ||||||
194 | 1853 | Shifting More Attention to Video Salient Object Detection | Deng-Ping Fan (Nankai University); Wenguan Wang (Inception Institute of Artificial Intelligence); Ming-Ming Cheng (Nankai University)*; Jianbing Shen (Beijing Institute of Technology) | Vision Applications and Systems | Low-level Vision | Oral | 3.1.1 | 2 | ||||||
195 | 864 | Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration | De-An Huang (Stanford University)*; Suraj Nair (Stanford University); Danfei Xu (Stanford University); Yuke Zhu (Stanford University); Animesh Garg (Stanford University); Li Fei-Fei (Stanford University); Silvio Savarese (Stanford University); Juan Carlos Niebles (Stanford University) | Robotics + Driving | Visual Reasoning | Oral | 3.1.1 | 2 | ||||||
196 | 1296 | Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry | Fei Xue (Peking University)*; Xin Wang (Peking University); Shunkai Li (Peking University); Qiuyuan Wang (Peking University); Junqiu Wang (Beijing Changcheng Aviation Measurement and Control Institute); Hongbin Zha (Peking University, China) | Robotics + Driving | Deep Learning ; Motion and Tracking | Oral | 3.1.1 | 2 | ||||||
197 | 3139 | Image Generation from Layout | Bo Zhao (University of British Columbia)*; Lili Meng (University of British Columbia); Weidong Yin (University of British Columbia); Leonid Sigal (University of British Columbia) | Vision Applications and Systems | Vision + Graphics | Oral | 3.1.1 | 3 | ||||||
198 | 4603 | Multimodal Explanations by Predicting Counterfactuality in Videos | Atsushi Kanehira (The University of Tokyo)*; Kentaro Takemoto (University of Tokyo); Sho Inayoshi (The University of Tokyo); Tatsuya Harada (The University of Tokyo) | Vision Applications and Systems | Video Analytics; Vision + Language | Oral | 3.1.1 | 3 | ||||||
199 | 4606 | Learning to Explain with Complemental Examples | Atsushi Kanehira (The University of Tokyo)*; Tatsuya Harada (The University of Tokyo) | Vision Applications and Systems | Vision + Language | Oral | 3.1.1 | 3 | ||||||
200 | 3441 | HAQ: Hardware-Aware Automated Quantization | Kuan Wang (MIT); Zhijian Liu (MIT); Yujun Lin (MIT); Ji Lin (MIT); Song Han (MIT)* | Vision Applications and Systems | Deep Learning | Oral | 3.1.1 | 4 | ||||||
201 | 4965 | Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution Channels | Pawel Korus (New York University)*; Nasir Memon (New York University) | Vision Applications and Systems | Low-level Vision | Oral | 3.1.1 | 4 | ||||||
202 | 5712 | Inverse Procedural Modeling of Knitwear | Elena Trunz (University of Bonn)*; Sebastian Merzbach (University of Bonn); Jonathan Klein (University of Bonn); Thomas Schulze (University of Bonn); Michael Weinmann (University of Bonn); Reinhard Klein (University of Bonn) | Vision Applications and Systems | Oral | 3.1.1 | 4 |