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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