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)