CVPR 2018 大会资源集锦
一、一些视觉会议论文集链接
计算机视觉会议论文集下载：http://openaccess.thecvf.com/menu.py
| CVPR 2018, Salt Lake City Utah [Main Conference] [Workshops] |
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| ICCV 2017, Venice Italy [Main Conference] [Workshops] |
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| CVPR 2017, Honolulu Hawaii [Main Conference] [Workshops] | |
| CVPR 2016, Las Vegas Nevada [Main Conference] [Workshops] | |
| ICCV 2015, Santiago Chile [Main Conference] [Workshops] |
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| CVPR 2015, Boston Massachusetts [Main Conference] [Workshops] | |
| CVPR 2014, Columbus Ohio [Main Conference] [Workshops] | |
| ICCV 2013, Sydney Australia [Main Conference] [Workshops] | |
| CVPR 2013, Portland Oregon [Main Conference] [Workshops] | |
二、CVPR 2018 论文地址
http://openaccess.thecvf.com/CVPR2018.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
CVPR 2018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 ConditionalGenerative Adversarial Network：
Runde Li (Nanjing Univ. of Science andTechnology ); Jinshan Pan (UC Merced); Zechao Li (Nanjing Univ. of Science andTechnology ); Jinhui Tang ()
2.DeblurGAN: Blind Motion DeblurringUsing Conditional Adversarial Networks：
Orest Kupyn (Ukrainian Catholic Univ.);Volodymyr Budzan (Ukrainian Catholic Univ.); Mykola Mykhailych (UkrainianCatholic Univ.); Dmytro Mishkin (Czech Technical Univ.); Jiří Matas ()
3.Deep Photo Enhancer: Unpaired Learningfor 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 theInvisible：
Kiana Ehsani (Univ. of Washington); RoozbehMottaghi (Allen Institute for AI); Ali Farhadi (Allen Institute for AI, Univ.of Washington)
5.Image Blind Denoising With GenerativeAdversarial Network Based Noise Modeling：
Jingwen Chen (Sun Yat-sen Univ.); JiaweiChen (Sun Yat-sen Univ.); Hongyang Chao (Sun Yat-sen Univ.); Ming Yang ()
6.Attentive Generative AdversarialNetwork 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 GenerativeAdversarial Networks for Jointly Learning Shadow Detection and Shadow Removal：
Jifeng Wang (Nanjing Univ. of Science andTechnology); Xiang Li (Nanjing Univ. of Science and Technology); Jian Yang(Nanjing Univ. of Science and Technology)
GAN优化：
1.SGAN: An Alternative Training ofGenerative Adversarial Networks：
Tatjana Chavdarova (Idiap and EPFL);François Fleuret (Idiap Research Inst.)
2.Multi-Agent Diverse GenerativeAdversarial Networks:
Arnab Ghosh (Univ. of Oxford); VivekaKulharia (Univ. of Oxford); Vinay P. Namboodiri (Indian Inst. of TechnologyKanpur); Philip H.S. Torr (Oxford); Puneet K. Dokania (Univ. of Oxford)
3.Generative Adversarial Image SynthesisWith Decision Tree Latent Controller：
Takuhiro Kaneko (NTT); Kaoru Hiramatsu(NTT); Kunio Kashino (NTT)
4.Unsupervised Deep GenerativeAdversarial Hashing Network：
Kamran Ghasedi Dizaji (Univ. ofPittsburgh); Feng Zheng (Univ. of Pittsburgh); Najmeh Sadoughi (UT Dallas);Yanhua Yang (Xidian Univ.); Cheng Deng (Xidian Univ.); Heng Huang (Univ. ofPittsburgh)
5.Global Versus Localized GenerativeAdversarial 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 GenerativeAdversarial Networks：
Jean Kossaifi (Imperial College London);Linh Tran (Imperial College London); Yannis Panagakis (); Maja Pantic (ImperialCollege London)
图像合成：
1.ST-GAN: Spatial Transformer GenerativeAdversarial 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 andRealistic Sketch to Image Synthesis：
Wengling Chen (Georgia Inst. ofTechnology); James Hays (Georgia Tech)
3.Translating and Segmenting MultimodalMedical Volumes With Cycle- and Shape-Consistency Generative AdversarialNetwork：
Zizhao Zhang (Univ. of Florida); Lin Yang(); Yefeng Zheng (Simens )
4.High-Resolution Image Synthesis andSemantic 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 ImageSynthesis With Texture Patches:
Wenqi Xian (); Patsorn Sangkloy (GeorgiaInst. of Technology); Varun Agrawal (); Amit Raj (Georgia Inst. of Technology);Jingwan Lu (Adobe Research); Chen Fang (Adobe Research); Fisher Yu (UCBerkeley); James Hays (Georgia Tech)
6.Eye In-Painting With ExemplarGenerative Adversarial Networks：
Brian Dolhansky (Facebook); Cristian CantonFerrer (Facebook)
7.Photographic Text-to-Image SynthesisWith a Hierarchically-Nested Adversarial Network：
Zizhao Zhang (Univ. of Florida); Yuanpu Xie(Univ. of Florida); Lin Yang ()
8.Logo Synthesis and Manipulation WithClustered Generative Adversarial Networks：
Alexander Sage (ETH Zürich); Eirikur Agustsson (ETH Zürich); RaduTimofte (ETH Zürich); Luc Van Gool (ETH Zürich)
9.Cross-View Image Synthesis UsingConditional GANs：
Krishna Regmi (Univ. of Central Florida);Ali Borji (Univ. of Central Florida)
10.AttnGAN: Fine-Grained Text to ImageGeneration 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 WithGenerative Adversarial Network：
Yancheng Bai (KAUST/Iscas); Yongqiang Zhang(Harbin Inst. of Technology/KAUST); Mingli Ding (); Bernard Ghanem ()
2.Learning Face Age Progression: APyramid Architecture of GANs：
Hongyu Yang (Beihang Univ.); Di Huang ();Yunhong Wang (); Anil K. Jain (MSU)
3.Super-FAN: Integrated Facial LandmarkLocalization and Super-Resolution
of Real-World Low Resolution Faces inArbitrary Poses With GANs：
Adrian Bulat (); Georgios Tzimiropoulos ()
4.Face Aging With Identity-PreservedConditional Generative Adversarial Networks：
Zongwei Wang (); Xu Tang (Baidu); WeixinLuo (ShanghaiTech Univ.); Shenghua Gao (ShanghaiTech Univ.)
5.Towards Open-Set Identity PreservingFace Synthesis：
Jianmin Bao (Univ. of Science andTechnology of China); Dong Chen (Microsoft Research Asia); Fang Wen ();Houqiang Li (); Gang Hua
(Microsoft Research)
6.Weakly Supervised Facial Action UnitRecognition Through Adversarial Training：
Guozhu Peng (Univ. of Science andTechnology of China); Shangfei Wang ()
7.FaceID-GAN: Learning a SymmetryThree-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 (ChineseUniv. of Hong Kong); Xiaoou Tang (Chinese Univ. of Hong Kong)
人体姿态相关：
1.GANerated Hands for Real-Time 3D HandTracking 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 forPose-Based Human Image Synthesis：
Chenyang Si (Inst. of Automation, ChineseAcademy of Sciences); Wei Wang (); Liang Wang (); Tieniu Tan (NLPR)
3.Deformable GANs for Pose-Based HumanImage Generation：
Aliaksandr Siarohin (DISI, Univ. ofTrento); Enver Sangineto (Univ. of Trento); StéphaneLathuilière (INRIA); Nicu Sebe (Univ. of Trento)
4.Social GAN: Socially AcceptableTrajectories With Generative Adversarial Networks：
Agrim Gupta (Stanford Univ.); JustinJohnson (Stanford Univ.); Li Fei-Fei (Stanford Univ.); Silvio Savarese ();Alexandre Alahi (EPFL)
行人重识别：
1.Person Transfer GAN to Bridge DomainGap 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 (MPIInformatics); Stamatios Georgoulis (KU Leuven); Luc Van Gool (KU Leuven); BerntSchiele (MPI Informatics); Mario Fritz (MPI Informatics)
3.Image-Image Domain Adaptation WithPreserved 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 TechnologySydney); Yi Yang (); Jianbin Jiao ()
目标跟踪：
1.VITAL: VIsual Tracking via AdversarialLearning：
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 viaAdversarial Positive Instance Generation：
Xiao Wang (Anhui Univ.); Chenglong Li(Anhui Univ.); Bin Luo (); Jin Tang ()
目标检测：
1.Generative Adversarial LearningTowards 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 UsingWasserstein GANs：
Christian F. Baumgartner (ETH Zürich); Lisa M. Koch (ETH Zürich); Kerem CanTezcan (ETH Zürich); Jia Xi Ang (ETH Zürich); Ender Konukoglu (ETH Zürich)
图像检索：
1.HashGAN: Deep Learning to Hash WithPair Conditional Wasserstein GAN：
Yue Cao (Tsinghua Univ.); Bin Liu (TsinghuaUniv.); Mingsheng Long (Tsinghua Univ.); Jianmin Wang ()
视频合成：
1.Learning to Generate Time-Lapse VideosUsing 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. ofRochester)
2.MoCoGAN: Decomposing Motion andContent for Video Generation：
Sergey Tulyakov (); Ming-Yu Liu (NVIDIA);Xiaodong Yang (NVIDIA); Jan Kautz (NVIDIA)
一、一些视觉会议论文集链接