CVPR2018资源汇总
CVPR 2018大会将于2018年6月18~22日于美国犹他州的盐湖城(Salt Lake City)举办。
CVPR2018论文集下载:http://openaccess.thecvf.com/menu.py
目前CVPR2018论文还不能打包下载,但可以看到收录论文标题的清单,感兴趣的可以自行google/baidu下载
详细可以点击链接:https://github.com/amusi/daily-paper-computer-vision/blob/master/2018/cvpr2018-paper-list.csv
cvpr2018论文解读集锦
https://zhuanlan.zhihu.com/p/35131736
CVPR 2017 论文解读集锦
http://cvmart.net/community/article/detail/69
ICCV 2017 论文解读集锦
http://cvmart.net/community/article/detail/153
CVPR2018 GAN相关论文汇总
链接:https://zhuanlan.zhihu.com/p/36436452
1. 数目统计:
风格迁移/cycleGAN/domain adaptation 13篇
去雾/去遮挡/超像素重建/Photo Enhancement 7篇
GAN优化 6篇
图像合成 10篇
人脸相关 7篇
姿态相关 4篇
行人重识别 3篇
其他类 <3篇
2. 分析:今年GAN的山头还是被domain adaptation和CycleGAN相关研究拿下,除此之外,图像合成和视觉病态问题也是GAN应用热点,人脸,行人识别异军突起,说明落地型工作开始增多。剩下几篇都属于挖坑型工作。
风格迁移/cycleGAN/domain adaptation:
1.PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup:
Huiwen Chang (); Jingwan Lu (Adobe Research); Fisher Yu (UC Berkeley); Adam Finkelstein (Princeton Univ.)
2.CartoonGAN: Generative Adversarial Networks for Photo Cartoonization:
Yang Chen (Tsinghua Univ.); Yu-Kun Lai (Cardiff Univ.); Yong-Jin Liu ()
3.StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation:
Yunjey Choi (Korea Univ.); Minje Choi (Korea Univ.); Munyoung Kim (College of New Jersey); Jung-Woo Ha (NAVER); Sunghun Kim (Hong Kong Univ. of Science and Technology); Jaegul Choo (Korea Univ.)
4.Generate to Adapt: Aligning Domains Using Generative Adversarial Networks:
Swami Sankaranarayanan (Univ. of Maryland); Yogesh Balaji (Univ. of Maryland); Carlos D. Castillo (); Rama Chellappa (Univ. of Maryland)
5.Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation:
Qingchao Chen (Unviersity College London); Yang Liu (Univ. of Cambridge); Zhaowen Wang (Adobe); Ian Wassell (); Kevin Chetty ()
6.Multi-Content GAN for Few-Shot Font Style Transfer:
Samaneh Azadi (UC Berkeley); Matthew Fisher (Adobe); Vladimir G. Kim (Adobe Research); Zhaowen Wang (Adobe); Eli Shechtman (Adobe Research); Trevor Darrell (UC Berkeley)
7.DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks:
Shuang Ma (SUNY Buffalo); Jianlong Fu (); Chang Wen Chen (); Tao Mei ()
8.Adversarial Feature Augmentation for Unsupervised Domain Adaptation:
Riccardo Volpi (Istituto Italiano di Tecnologia); Pietro Morerio (Istituto Italiano di Tecnologia); Silvio Savarese (); Vittorio Murino (Istituto Italiano di Tecnologia)
9.Domain Generalization With Adversarial Feature Learning:
Haoliang Li (Nanyang Technological Univ.); Sinno Jialin Pan (Nanyang Technological Univ.); Shiqi Wang (City Univ. of Hong Kong); Alex C. Kot ()
10:Image to Image Translation for Domain Adaptation:
Zak Murez (UC San Diego); Soheil Kolouri (HRL Laboratories); David Kriegman (UC San Diego); Ravi Ramamoorthi (UC San Diego); Kyungnam Kim (HRL Laboratories)
11.Partial Transfer Learning With Selective Adversarial Networks:
Zhangjie Cao (Tsinghua Univ.); Mingsheng Long (Tsinghua Univ.); Jianmin Wang (); Michael I. Jordan (UC Berkeley)
12.Duplex Generative Adversarial Network for Unsupervised Domain Adaptation:
Lanqing Hu (ICT, CAS); Meina Kan (); Shiguang Shan (Chinese Academy of Sciences); Xilin Chen ()
13.Conditional Generative Adversarial Network for Structured Domain Adaptation:
去雾/去遮挡/超像素重建/Photo Enhancement :
1.Single Image Dehazing via Conditional
Generative Adversarial Network:
Runde Li (Nanjing Univ. of Science and
Technology ); Jinshan Pan (UC Merced); Zechao Li (Nanjing Univ. of Science and
Technology ); Jinhui Tang ()
2.DeblurGAN: Blind Motion Deblurring
Using Conditional Adversarial Networks:
Orest Kupyn (Ukrainian Catholic Univ.);
Volodymyr Budzan (Ukrainian Catholic Univ.); Mykola Mykhailych (Ukrainian
Catholic Univ.); Dmytro Mishkin (Czech Technical Univ.); Jiří Matas ()
3.Deep Photo Enhancer: Unpaired Learning
for Image Enhancement From Photographs With GANs:
Yu-Sheng Chen (National Taiwan Univ.);
Yu-Ching Wang (National Taiwan Univ.); Man-Hsin Kao (National Taiwan Univ.);
Yung-Yu Chuang (National Taiwan Univ.)
4.SeGAN: Segmenting and Generating the
Invisible:
Kiana Ehsani (Univ. of Washington); Roozbeh
Mottaghi (Allen Institute for AI); Ali Farhadi (Allen Institute for AI, Univ.
of Washington)
5.Image Blind Denoising With Generative
Adversarial Network Based Noise Modeling:
Jingwen Chen (Sun Yat-sen Univ.); Jiawei
Chen (Sun Yat-sen Univ.); Hongyang Chao (Sun Yat-sen Univ.); Ming Yang ()
6.Attentive Generative Adversarial
Network for Raindrop Removal From a Single Image:
Rui Qian (Peking Univ.); Robby T. Tan
(Yale-NUS College; National Univ. of Singapore); Wenhan Yang (Peking Univ.);
Jiajun Su (Peking Univ.); Jiaying Liu (Peking Univ.)
7.Stacked Conditional Generative
Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal:
Jifeng Wang (Nanjing Univ. of Science and
Technology); Xiang Li (Nanjing Univ. of Science and Technology); Jian Yang
(Nanjing Univ. of Science and Technology)
GAN优化:
1.SGAN: An Alternative Training of
Generative Adversarial Networks:
Tatjana Chavdarova (Idiap and EPFL);
François Fleuret (Idiap Research Inst.)
2.Multi-Agent Diverse Generative
Adversarial Networks:
Arnab Ghosh (Univ. of Oxford); Viveka
Kulharia (Univ. of Oxford); Vinay P. Namboodiri (Indian Inst. of Technology
Kanpur); Philip H.S. Torr (Oxford); Puneet K. Dokania (Univ. of Oxford)
3.Generative Adversarial Image Synthesis
With Decision Tree Latent Controller:
Takuhiro Kaneko (NTT); Kaoru Hiramatsu
(NTT); Kunio Kashino (NTT)
4.Unsupervised Deep Generative
Adversarial Hashing Network:
Kamran Ghasedi Dizaji (Univ. of
Pittsburgh); Feng Zheng (Univ. of Pittsburgh); Najmeh Sadoughi (UT Dallas);
Yanhua Yang (Xidian Univ.); Cheng Deng (Xidian Univ.); Heng Huang (Univ. of
Pittsburgh)
5.Global Versus Localized Generative
Adversarial Nets:
Guo-Jun Qi (Univ. of Central Florida);
Liheng Zhang (Univ. of Central Florida); Hao Hu (Univ. of Central Florida);
Marzieh Edraki (Univ. of Central Florida ); Jingdong Wang (Microsoft Research);
Xian-Sheng Hua (Microsoft Research)
6.GAGAN: Geometry-Aware Generative
Adversarial Networks:
Jean Kossaifi (Imperial College London);
Linh Tran (Imperial College London); Yannis Panagakis (); Maja Pantic (Imperial
College London)
图像合成:
1.ST-GAN: Spatial Transformer Generative
Adversarial Networks for Image Compositing:
Chen-Hsuan Lin (Carnegie Mellon Univ.);
Ersin Yumer (Argo AI); Oliver Wang (Adobe); Eli Shechtman (Adobe Research);
Simon Lucey ()
2.SketchyGAN: Towards Diverse and
Realistic Sketch to Image Synthesis:
Wengling Chen (Georgia Inst. of
Technology); James Hays (Georgia Tech)
3.Translating and Segmenting Multimodal
Medical Volumes With Cycle- and Shape-Consistency Generative Adversarial
Network:
Zizhao Zhang (Univ. of Florida); Lin Yang
(); Yefeng Zheng (Simens )
4.High-Resolution Image Synthesis and
Semantic Manipulation With Conditional GANs:
Ting-Chun Wang (NVIDIA); Ming-Yu Liu
(NVIDIA); Jun-Yan Zhu (UC Berkeley); Andrew Tao (NVIDIA); Jan Kautz (NVIDIA);
Bryan Catanzaro (NVIDIA)
5.TextureGAN: Controlling Deep Image
Synthesis With Texture Patches:
Wenqi Xian (); Patsorn Sangkloy (Georgia
Inst. of Technology); Varun Agrawal (); Amit Raj (Georgia Inst. of Technology);
Jingwan Lu (Adobe Research); Chen Fang (Adobe Research); Fisher Yu (UC
Berkeley); James Hays (Georgia Tech)
6.Eye In-Painting With Exemplar
Generative Adversarial Networks:
Brian Dolhansky (Facebook); Cristian Canton
Ferrer (Facebook)
7.Photographic Text-to-Image Synthesis
With a Hierarchically-Nested Adversarial Network:
Zizhao Zhang (Univ. of Florida); Yuanpu Xie
(Univ. of Florida); Lin Yang ()
8.Logo Synthesis and Manipulation With
Clustered Generative Adversarial Networks:
Alexander Sage (ETH Zürich); Eirikur Agustsson (ETH Zürich); Radu
Timofte (ETH Zürich); Luc Van Gool (ETH Zürich)
9.Cross-View Image Synthesis Using
Conditional GANs:
Krishna Regmi (Univ. of Central Florida);
Ali Borji (Univ. of Central Florida)
10.AttnGAN: Fine-Grained Text to Image
Generation With Attentional Generative Adversarial Networks:
Tao Xu (Lehigh Univ.); Pengchuan Zhang ();
Qiuyuan Huang (); Han Zhang (Rutgers); Zhe Gan (); Xiaolei Huang (Lehigh );
Xiaodong He ()
人脸相关:
1.Finding Tiny Faces in the Wild With
Generative Adversarial Network:
Yancheng Bai (KAUST/Iscas); Yongqiang Zhang
(Harbin Inst. of Technology/KAUST); Mingli Ding (); Bernard Ghanem ()
2.Learning Face Age Progression: A
Pyramid Architecture of GANs:
Hongyu Yang (Beihang Univ.); Di Huang ();
Yunhong Wang (); Anil K. Jain (MSU)
3.Super-FAN: Integrated Facial Landmark
Localization and Super-Resolution
of Real-World Low Resolution Faces in
Arbitrary Poses With GANs:
Adrian Bulat (); Georgios Tzimiropoulos ()
4.Face Aging With Identity-Preserved
Conditional Generative Adversarial Networks:
Zongwei Wang (); Xu Tang (Baidu); Weixin
Luo (ShanghaiTech Univ.); Shenghua Gao (ShanghaiTech Univ.)
5.Towards Open-Set Identity Preserving
Face Synthesis:
Jianmin Bao (Univ. of Science and
Technology of China); Dong Chen (Microsoft Research Asia); Fang Wen ();
Houqiang Li (); Gang Hua
(Microsoft Research)
6.Weakly Supervised Facial Action Unit
Recognition Through Adversarial Training:
Guozhu Peng (Univ. of Science and
Technology of China); Shangfei Wang ()
7.FaceID-GAN: Learning a Symmetry
Three-Player GAN for Identity-Preserving Face Synthesis:
Yujun Shen (Chinese Univ. of Hong Kong);
Ping Luo (Chinese Univ. of Hong Kong); Junjie Yan (); Xiaogang Wang (Chinese
Univ. of Hong Kong); Xiaoou Tang (Chinese Univ. of Hong Kong)
人体姿态相关:
1.GANerated Hands for Real-Time 3D Hand
Tracking From Monocular RGB:
Franziska Mueller (MPI Informatics);
Florian Bernard (MPI Informatics); Oleksandr Sotnychenko (MPI Informatics);
Dushyant Mehta (MPI Informatics); Srinath Sridhar (); Dan Casas (MPI Informatics);
Christian Theobalt (MPI Informatics)
2.Multistage Adversarial Losses for
Pose-Based Human Image Synthesis:
Chenyang Si (Inst. of Automation, Chinese
Academy of Sciences); Wei Wang (); Liang Wang (); Tieniu Tan (NLPR)
3.Deformable GANs for Pose-Based Human
Image Generation:
Aliaksandr Siarohin (DISI, Univ. of
Trento); Enver Sangineto (Univ. of Trento); Stéphane
Lathuilière (INRIA); Nicu Sebe (Univ. of Trento)
4.Social GAN: Socially Acceptable
Trajectories With Generative Adversarial Networks:
Agrim Gupta (Stanford Univ.); Justin
Johnson (Stanford Univ.); Li Fei-Fei (Stanford Univ.); Silvio Savarese ();
Alexandre Alahi (EPFL)
行人重识别:
1.Person Transfer GAN to Bridge Domain
Gap for Person Re-Identification:
Longhui Wei (Peking Univ.); Shiliang Zhang
(Peking Univ.); Wen Gao (); Qi Tian ()
2.Disentangled Person Image Generation:
Liqian Ma (KU Leuven); Qianru Sun (MPI
Informatics); Stamatios Georgoulis (KU Leuven); Luc Van Gool (KU Leuven); Bernt
Schiele (MPI Informatics); Mario Fritz (MPI Informatics)
3.Image-Image Domain Adaptation With
Preserved Self-Similarity and Domain-Dissimilarity for Person Re-Identification:
Weijian Deng (Univ. of Chinese Academy);
Liang Zheng (UT San Antonio); Qixiang Ye (); Guoliang Kang (Univ. of Technology
Sydney); Yi Yang (); Jianbin Jiao ()
目标跟踪:
1.VITAL: VIsual Tracking via Adversarial
Learning:
Yibing Song (Tencent AI Lab); Chao Ma ();
Xiaohe Wu (Harbin Inst. of Technology); Lijun Gong (City Univ. of Hong Kong);
Linchao Bao (Tencent AI Lab); Wangmeng Zuo (Harbin Inst. of Technology);
Chunhua Shen (Univ. of Adelaide); Rynson W.H. Lau (City Univ. of Hong Kong);
Ming-Hsuan Yang (UC Merced)
2.SINT++: Robust Visual Tracking via
Adversarial Positive Instance Generation:
Xiao Wang (Anhui Univ.); Chenglong Li
(Anhui Univ.); Bin Luo (); Jin Tang ()
目标检测:
1.Generative Adversarial Learning
Towards Fast Weakly Supervised Detection:
Yunhan Shen (Xiamen Univ.); Rongrong Ji ();
Shengchuan Zhang (); Wangmeng Zuo (Harbin Inst. of Technology); Yan Wang
(Microsoft)
特征可解释性:
1.Visual Feature Attribution Using
Wasserstein GANs:
Christian F. Baumgartner (ETH Zürich); Lisa M. Koch (ETH Zürich); Kerem Can
Tezcan (ETH Zürich); Jia Xi Ang (ETH Zürich); Ender Konukoglu (ETH Zürich)
图像检索:
1.HashGAN: Deep Learning to Hash With
Pair Conditional Wasserstein GAN:
Yue Cao (Tsinghua Univ.); Bin Liu (Tsinghua
Univ.); Mingsheng Long (Tsinghua Univ.); Jianmin Wang ()
视频合成:
1.Learning to Generate Time-Lapse Videos
Using Multi-Stage Dynamic Generative Adversarial Networks:
Wei Xiong (Univ. of Rochester); Wenhan Luo
(Tencent AI Lab); Lin Ma (Tencent AI Lab); Wei Liu (); Jiebo Luo (Univ. of
Rochester)
2.MoCoGAN: Decomposing Motion and
Content for Video Generation:
Sergey Tulyakov (); Ming-Yu Liu (NVIDIA);
Xiaodong Yang (NVIDIA); Jan Kautz (NVIDIA)
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