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 Awesome Deep Vision !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
 Awesome Deep Vision !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
A curated list of deep learning resources for computer vision, inspired by awesome-php (https://github.com/ziadoz/awesome-php) and awesome-computer-vision 
(https://github.com/jbhuang0604/awesome-computer-vision).
Maintainers - Jiwon Kim (https://github.com/kjw0612), Heesoo Myeong (https://github.com/hmyeong), Myungsub Choi (https://github.com/myungsub), Jung Kwon Lee (https://github.com/deruci), Taeksoo Kim 
(https://github.com/jazzsaxmafia)
Maintainers - Jiwon Kim (https://github.com/kjw0612), Heesoo Myeong (https://github.com/hmyeong), Myungsub Choi (https://github.com/myungsub), Jung Kwon Lee (https://github.com/deruci), 
Taeksoo Kim (https://github.com/jazzsaxmafia)
The project is not actively maintained. 
@@ -75,7 +75,8 @@
  ⟡ Kye-Hyeon Kim, Sanghoon Hong, Byungseok Roh, Yeongjae Cheon, Minje Park, PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection, arXiv:1608.08021
⟡ OverFeat, NYU Paper  (http://arxiv.org/pdf/1312.6229.pdf)
  ⟡ OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks, ICLR, 2014.
⟡ R-CNN, UC Berkeley Paper-CVPR14  (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf) Paper-arXiv14  (http://arxiv.org/pdf/1311.2524)
⟡ R-CNN, UC Berkeley Paper-CVPR14  (http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf) Paper-arXiv14  
(http://arxiv.org/pdf/1311.2524)
  ⟡ Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR, 2014.
⟡ SPP, Microsoft Research Paper  (http://arxiv.org/pdf/1406.4729)
  ⟡ Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, ECCV, 2014.
@@ -87,8 +88,8 @@
  ⟡ Karel Lenc, Andrea Vedaldi, R-CNN minus R, arXiv:1506.06981.
⟡ End-to-end people detection in crowded scenes Paper  (http://arxiv.org/abs/1506.04878)
  ⟡ Russell Stewart, Mykhaylo Andriluka, End-to-end people detection in crowded scenes, arXiv:1506.04878.
⟡ You Only Look Once: Unified, Real-Time Object Detection Paper  (http://arxiv.org/abs/1506.02640), Paper Version 2  (https://arxiv.org/abs/1612.08242), C Code  (https://github.com/pjreddie/darknet), Tensorflow 
Code  (https://github.com/thtrieu/darkflow)
⟡ You Only Look Once: Unified, Real-Time Object Detection Paper  (http://arxiv.org/abs/1506.02640), Paper Version 2  (https://arxiv.org/abs/1612.08242), C Code  
(https://github.com/pjreddie/darknet), Tensorflow Code  (https://github.com/thtrieu/darkflow)
  ⟡ Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, You Only Look Once: Unified, Real-Time Object Detection, arXiv:1506.02640
  ⟡ Joseph Redmon, Ali Farhadi (Version 2)
⟡ Inside-Outside Net Paper  (http://arxiv.org/abs/1512.04143)
@@ -101,14 +102,16 @@
⟡ SSD Paper  (https://arxiv.org/pdf/1512.02325v2.pdf) Code  (https://github.com/weiliu89/caffe/tree/ssd)
  ⟡ Wei Liu1, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg, SSD: Single Shot MultiBox Detector, arXiv:1512.02325
⟡ Speed/accuracy trade-offs for modern convolutional object detectors Paper  (https://arxiv.org/pdf/1611.10012v1.pdf)
  ⟡ Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Kevin Murphy, Google Research, arXiv:1611.10012
  ⟡ Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Kevin Murphy, Google Research, 
arXiv:1611.10012
Video Classification
⟡ Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, "Delving Deeper into Convolutional Networks for Learning Video Representations", ICLR 2016. Paper (http://arxiv.org/pdf/1511.06432v4.pdf) 
⟡ Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, "Delving Deeper into Convolutional Networks for Learning Video Representations", ICLR 2016. Paper (http://arxiv.org/pdf/1511.06432v4.pdf)
⟡ Michael Mathieu, camille couprie, Yann Lecun, "Deep Multi Scale Video Prediction Beyond Mean Square Error", ICLR 2016. Paper (http://arxiv.org/pdf/1511.05440v6.pdf) 
Object Tracking
⟡ Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han, Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network, arXiv:1502.06796. Paper  (http://arxiv.org/pdf/1502.06796)
⟡ Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han, Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network, arXiv:1502.06796. Paper  
(http://arxiv.org/pdf/1502.06796)
⟡ Hanxi Li, Yi Li and Fatih Porikli, DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, BMVC, 2014. Paper  
(http://www.bmva.org/bmvc/2014/files/paper028.pdf)
⟡ N Wang, DY Yeung, Learning a Deep Compact Image Representation for Visual Tracking, NIPS, 2013. Paper  (http://winsty.net/papers/dlt.pdf)
@@ -116,16 +119,16 @@
(http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Ma_Hierarchical_Convolutional_Features_ICCV_2015_paper.pdf) Code (https://github.com/jbhuang0604/CF2) 
⟡ Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, Visual Tracking with fully Convolutional Networks, ICCV 2015 Paper (http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf) Code 
(https://github.com/scott89/FCNT) 
⟡ Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, Paper (http://arxiv.org/pdf/1510.07945.pdf) Code (https://github.com/HyeonseobNam/MDNet) Project Page 
(http://cvlab.postech.ac.kr/research/mdnet/) 
⟡ Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, Paper (http://arxiv.org/pdf/1510.07945.pdf) Code 
(https://github.com/HyeonseobNam/MDNet) Project Page (http://cvlab.postech.ac.kr/research/mdnet/) 
Low-Level Vision
Super-Resolution
⟡ Iterative Image Reconstruction
  ⟡ Sven Behnke: Learning Iterative Image Reconstruction. IJCAI, 2001. Paper  (http://www.ais.uni-bonn.de/behnke/papers/ijcai01.pdf)
  ⟡ Sven Behnke: Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid. International Journal of Computational Intelligence and Applications, vol. 1, no. 4, pp. 427-438, 2001. Paper  
(http://www.ais.uni-bonn.de/behnke/papers/ijcia01.pdf)
  ⟡ Sven Behnke: Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid. International Journal of Computational Intelligence and Applications, vol. 1, no. 4, pp. 427-438, 
2001. Paper  (http://www.ais.uni-bonn.de/behnke/papers/ijcia01.pdf)
⟡ Super-Resolution (SRCNN) Web  (http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html) Paper-ECCV14  (http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepresolution.pdf) Paper-arXiv15  
(http://arxiv.org/pdf/1501.00092.pdf)
  ⟡ Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, ECCV, 2014.
@@ -135,22 +138,22 @@
⟡ Deeply-Recursive Convolutional Network
  ⟡ Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015. Paper  (http://arxiv.org/abs/1511.04491)
⟡ Casade-Sparse-Coding-Network
  ⟡ Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015. Paper  (http://www.ifp.illinois.edu/~dingliu2/iccv15/iccv15.pdf) 
Code  (http://www.ifp.illinois.edu/~dingliu2/iccv15/)
  ⟡ Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015. Paper  
(http://www.ifp.illinois.edu/~dingliu2/iccv15/iccv15.pdf) Code  (http://www.ifp.illinois.edu/~dingliu2/iccv15/)
⟡ Perceptual Losses for Super-Resolution
  ⟡ Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155, 2016. Paper  (http://arxiv.org/abs/1603.08155) Supplementary  
(http://cs.stanford.edu/people/jcjohns/papers/fast-style/fast-style-supp.pdf)
  ⟡ Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155, 2016. Paper  (http://arxiv.org/abs/1603.08155) 
Supplementary  (http://cs.stanford.edu/people/jcjohns/papers/fast-style/fast-style-supp.pdf)
⟡ SRGAN
  ⟡ Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi, Photo-Realistic Single Image 
Super-Resolution Using a Generative Adversarial Network, arXiv:1609.04802v3, 2016. Paper  (https://arxiv.org/pdf/1609.04802v3.pdf)
  ⟡ Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi, Photo-Realistic 
Single Image Super-Resolution Using a Generative Adversarial Network, arXiv:1609.04802v3, 2016. Paper  (https://arxiv.org/pdf/1609.04802v3.pdf)
⟡ Others
  ⟡ Osendorfer, Christian, Hubert Soyer, and Patrick van der Smagt, Image Super-Resolution with Fast Approximate Convolutional Sparse Coding, ICONIP, 2014. Paper ICONIP-2014  
(http://brml.org/uploads/tx_sibibtex/281.pdf)
Other Applications
⟡ Optical Flow (FlowNet) Paper  (http://arxiv.org/pdf/1504.06852)
  ⟡ Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox, FlowNet: Learning Optical Flow with Convolutional Networks,
arXiv:1504.06852.
  ⟡ Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox, FlowNet: Learning Optical Flow with 
Convolutional Networks, arXiv:1504.06852.
⟡ Compression Artifacts Reduction Paper-arXiv15  (http://arxiv.org/pdf/1504.06993)
  ⟡ Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang, Compression Artifacts Reduction by a Deep Convolutional Network, arXiv:1504.06993.
⟡ Blur Removal
@@ -160,7 +163,8 @@
  ⟡ Li Xu, Jimmy SJ. Ren, Ce Liu, Jiaya Jia, Deep Convolutional Neural Network for Image Deconvolution, NIPS, 2014.
⟡ Deep Edge-Aware Filter Paper  (http://jmlr.org/proceedings/papers/v37/xub15.pdf)
  ⟡ Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia, Deep Edge-Aware Filters, ICML, 2015.
⟡ Computing the Stereo Matching Cost with a Convolutional Neural Network Paper  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zbontar_Computing_the_Stereo_2015_CVPR_paper.pdf)
⟡ Computing the Stereo Matching Cost with a Convolutional Neural Network Paper  
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zbontar_Computing_the_Stereo_2015_CVPR_paper.pdf)
  ⟡ Jure Žbontar, Yann LeCun, Computing the Stereo Matching Cost with a Convolutional Neural Network, CVPR, 2015.
⟡ Colorful Image Colorization Richard Zhang, Phillip Isola, Alexei A. Efros, ECCV, 2016 Paper  (http://arxiv.org/pdf/1603.08511.pdf), Code  (https://github.com/richzhang/colorization)
⟡ Ryan Dahl, Blog  (http://tinyclouds.org/colorize/)
@@ -185,15 +189,16 @@
 !VOC2012_top_rankings (https://cloud.githubusercontent.com/assets/3803777/18164608/c3678488-7038-11e6-9ec1-74a1542dce13.png)
 (from PASCAL VOC2012 leaderboards (http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6))
⟡ SEC: Seed, Expand and Constrain
  ⟡  Alexander Kolesnikov, Christoph Lampert, Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, ECCV, 2016. Paper  (http://pub.ist.ac.at/~akolesnikov/files/ECCV2016/main.pdf)
Code  (https://github.com/kolesman/SEC)
  ⟡  Alexander Kolesnikov, Christoph Lampert, Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, ECCV, 2016. Paper  
(http://pub.ist.ac.at/~akolesnikov/files/ECCV2016/main.pdf) Code  (https://github.com/kolesman/SEC)
⟡ Adelaide
  ⟡ Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv:1504.01013. Paper  (http://arxiv.org/pdf/1504.01013) (1st 
  ⟡ Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv:1504.01013. Paper  
(http://arxiv.org/pdf/1504.01013) (1st ranked in VOC2012)
  ⟡ Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel, Deeply Learning the Messages in Message Passing Inference, arXiv:1508.02108. Paper  (http://arxiv.org/pdf/1506.02108) (4th 
ranked in VOC2012)
  ⟡ Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel, Deeply Learning the Messages in Message Passing Inference, arXiv:1508.02108. Paper  (http://arxiv.org/pdf/1506.02108) (4th ranked in VOC2012)
⟡ Deep Parsing Network (DPN)
  ⟡ Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang, Semantic Image Segmentation via Deep Parsing Network, arXiv:1509.02634 / ICCV 2015 Paper  (http://arxiv.org/pdf/1509.02634.pdf) (2nd ranked in 
VOC 2012)
  ⟡ Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang, Semantic Image Segmentation via Deep Parsing Network, arXiv:1509.02634 / ICCV 2015 Paper  
(http://arxiv.org/pdf/1509.02634.pdf) (2nd ranked in VOC 2012)
⟡ CentraleSuperBoundaries, INRIA Paper  (http://arxiv.org/pdf/1511.07386)
  ⟡ Iasonas Kokkinos, Surpassing Humans in Boundary Detection using Deep Learning, arXiv:1411.07386 (4th ranked in VOC 2012)
⟡ BoxSup Paper  (http://arxiv.org/pdf/1503.01640)
@@ -204,31 +209,32 @@
  ⟡ Seunghoon Hong,Junhyuk Oh, Bohyung Han, and Honglak Lee, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, arXiv:1512.07928 Paper 
(http://arxiv.org/pdf/1512.07928.pdf) Project Page (http://cvlab.postech.ac.kr/research/transfernet/) 
⟡ Conditional Random Fields as Recurrent Neural Networks Paper  (http://arxiv.org/pdf/1502.03240)
  ⟡ Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr, Conditional Random Fields as Recurrent Neural Networks, arXiv:1502.03240. 
(8th ranked in VOC2012)
  ⟡ Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr, Conditional Random Fields as Recurrent Neural Networks, 
arXiv:1502.03240. (8th ranked in VOC2012)
⟡ DeepLab
  ⟡ Liang-Chieh Chen, George Papandreou, Kevin Murphy, Alan L. Yuille, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, arXiv:1502.02734. Paper  (http://arxiv.org/pdf/1502.02734) 
(9th ranked in VOC2012)
  ⟡ Liang-Chieh Chen, George Papandreou, Kevin Murphy, Alan L. Yuille, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, arXiv:1502.02734. Paper  
(http://arxiv.org/pdf/1502.02734) (9th ranked in VOC2012)
⟡ Zoom-out Paper  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mostajabi_Feedforward_Semantic_Segmentation_2015_CVPR_paper.pdf)
  ⟡ Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich, Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015
⟡ Joint Calibration Paper  (http://arxiv.org/pdf/1507.01581)
  ⟡ Holger Caesar, Jasper Uijlings, Vittorio Ferrari, Joint Calibration for Semantic Segmentation, arXiv:1507.01581.
⟡ Fully Convolutional Networks for Semantic Segmentation Paper-CVPR15  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf) Paper-arXiv15  
(http://arxiv.org/pdf/1411.4038)
⟡ Fully Convolutional Networks for Semantic Segmentation Paper-CVPR15  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf)
Paper-arXiv15  (http://arxiv.org/pdf/1411.4038)
  ⟡ Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015.
⟡ Hypercolumn Paper  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hariharan_Hypercolumns_for_Object_2015_CVPR_paper.pdf)
  ⟡ Bharath Hariharan, Pablo Arbelaez, Ross Girshick, Jitendra Malik, Hypercolumns for Object Segmentation and Fine-Grained Localization, CVPR, 2015.
⟡ Deep Hierarchical Parsing
  ⟡ Abhishek Sharma, Oncel Tuzel, David W. Jacobs, Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015. Paper  
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Sharma_Deep_Hierarchical_Parsing_2015_CVPR_paper.pdf)
⟡ Learning Hierarchical Features for Scene Labeling Paper-ICML12  (http://yann.lecun.com/exdb/publis/pdf/farabet-icml-12.pdf) Paper-PAMI13  (http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf)
⟡ Learning Hierarchical Features for Scene Labeling Paper-ICML12  (http://yann.lecun.com/exdb/publis/pdf/farabet-icml-12.pdf) Paper-PAMI13  
(http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf)
  ⟡ Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers, ICML, 2012.
  ⟡ Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Learning Hierarchical Features for Scene Labeling, PAMI, 2013.
⟡ University of Cambridge Web  (http://mi.eng.cam.ac.uk/projects/segnet/)
  ⟡ Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." arXiv preprint arXiv:1511.00561, 2015. Paper  
(http://arxiv.org/abs/1511.00561)
⟡ Alex Kendall, Vijay Badrinarayanan and Roberto Cipolla "Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding." arXiv preprint arXiv:1511.02680, 2015. 
Paper  (http://arxiv.org/abs/1511.00561)
⟡ Alex Kendall, Vijay Badrinarayanan and Roberto Cipolla "Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding." arXiv preprint 
arXiv:1511.02680, 2015. Paper  (http://arxiv.org/abs/1511.00561)
⟡ Princeton
  ⟡ Fisher Yu, Vladlen Koltun, "Multi-Scale Context Aggregation by Dilated Convolutions", ICLR 2016, Paper (http://arxiv.org/pdf/1511.07122v2.pdf) 
⟡ Univ. of Washington, Allen AI
@@ -261,7 +267,8 @@
Human Pose Estimation
⟡ Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh, Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields, CVPR, 2017.
⟡ Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter Gehler, and Bernt Schiele, Deepcut: Joint subset partition and labeling for multi person pose estimation, CVPR, 2016.
⟡ Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter Gehler, and Bernt Schiele, Deepcut: Joint subset partition and labeling for multi person pose 
estimation, CVPR, 2016.
⟡ Shih-En Wei, Varun Ramakrishna, Takeo Kanade, and Yaser Sheikh, Convolutional pose machines, CVPR, 2016.
⟡ Alejandro Newell, Kaiyu Yang, and Jia Deng, Stacked hourglass networks for human pose estimation, ECCV, 2016.
⟡ Tomas Pfister, James Charles, and Andrew Zisserman, Flowing convnets for human pose estimation in videos, ICCV, 2015.
@@ -293,31 +300,33 @@
⟡ Toronto Paper  (http://arxiv.org/pdf/1411.2539)
  ⟡ Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel, Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models, arXiv:1411.2539.
⟡ Berkeley Paper  (http://arxiv.org/pdf/1411.4389)
  ⟡ Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual Recognition and Description, 
arXiv:1411.4389.
  ⟡ Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual 
Recognition and Description, arXiv:1411.4389.
⟡ Google Paper  (http://arxiv.org/pdf/1411.4555)
  ⟡ Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan, Show and Tell: A Neural Image Caption Generator, arXiv:1411.4555.
⟡ Stanford Web  (http://cs.stanford.edu/people/karpathy/deepimagesent/) Paper  (http://cs.stanford.edu/people/karpathy/cvpr2015.pdf)
  ⟡ Andrej Karpathy, Li Fei-Fei, Deep Visual-Semantic Alignments for Generating Image Description, CVPR, 2015.
⟡ UML / UT Paper  (http://arxiv.org/pdf/1412.4729)
  ⟡ Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, NAACL-HLT, 2015.
  ⟡ Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, NAACL-HLT, 
2015.
⟡ CMU / Microsoft Paper-arXiv  (http://arxiv.org/pdf/1411.5654) Paper-CVPR  (http://www.cs.cmu.edu/~xinleic/papers/cvpr15_rnn.pdf)
  ⟡ Xinlei Chen, C. Lawrence Zitnick, Learning a Recurrent Visual Representation for Image Caption Generation, arXiv:1411.5654.
  ⟡ Xinlei Chen, C. Lawrence Zitnick, Mind’s Eye: A Recurrent Visual Representation for Image Caption Generation, CVPR 2015
⟡ Microsoft Paper  (http://arxiv.org/pdf/1411.4952)
  ⟡ Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C. Platt, C. Lawrence Zitnick, Geoffrey Zweig, From Captions to Visual 
Concepts and Back, CVPR, 2015.
  ⟡ Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C. Platt, C. Lawrence Zitnick, Geoffrey Zweig, From 
Captions to Visual Concepts and Back, CVPR, 2015.
⟡ Univ. Montreal / Univ. Toronto Web (http://kelvinxu.github.io/projects/capgen.html) Paper (http://www.cs.toronto.edu/~zemel/documents/captionAttn.pdf) 
  ⟡ Kelvin Xu, Jimmy Lei Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio, Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention, 
arXiv:1502.03044 / ICML 2015
  ⟡ Kelvin Xu, Jimmy Lei Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio, Show, Attend, and Tell: Neural Image Caption Generation with 
Visual Attention, arXiv:1502.03044 / ICML 2015
⟡ Idiap / EPFL / Facebook Paper (http://arxiv.org/pdf/1502.03671) 
  ⟡ Remi Lebret, Pedro O. Pinheiro, Ronan Collobert, Phrase-based Image Captioning, arXiv:1502.03671 / ICML 2015
⟡ UCLA / Baidu Paper (http://arxiv.org/pdf/1504.06692) 
  ⟡ Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan L. Yuille, Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images, arXiv:1504.06692
⟡ MS + Berkeley
  ⟡ Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, Exploring Nearest Neighbor Approaches for Image Captioning, arXiv:1505.04467 Paper (http://arxiv.org/pdf/1505.04467.pdf) 
  ⟡ Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell, Language Models for Image Captioning: The Quirks and What Works, arXiv:1505.01809 Paper 
(http://arxiv.org/pdf/1505.01809.pdf) 
  ⟡ Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, Exploring Nearest Neighbor Approaches for Image Captioning, arXiv:1505.04467 Paper 
(http://arxiv.org/pdf/1505.04467.pdf) 
  ⟡ Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell, Language Models for Image Captioning: The Quirks and What Works, 
arXiv:1505.01809 Paper (http://arxiv.org/pdf/1505.01809.pdf) 
⟡ Adelaide Paper (http://arxiv.org/pdf/1506.01144.pdf) 
  ⟡ Qi Wu, Chunhua Shen, Anton van den Hengel, Lingqiao Liu, Anthony Dick, Image Captioning with an Intermediate Attributes Layer, arXiv:1506.01144
⟡ Tilburg Paper (http://arxiv.org/pdf/1506.03694.pdf) 
@@ -332,10 +341,11 @@
Video Captioning
⟡ Berkeley Web  (http://jeffdonahue.com/lrcn/) Paper  (http://arxiv.org/pdf/1411.4389.pdf)
  ⟡ Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual Recognition and Description, 
CVPR, 2015.
  ⟡ Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual 
Recognition and Description, CVPR, 2015.
⟡ UT / UML / Berkeley Paper  (http://arxiv.org/pdf/1412.4729)
  ⟡ Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, arXiv:1412.4729.
  ⟡ Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, 
arXiv:1412.4729.
⟡ Microsoft Paper  (http://arxiv.org/pdf/1505.01861)
  ⟡ Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui, Joint Modeling Embedding and Translation to Bridge Video and Language, arXiv:1505.01861.
⟡ UT / Berkeley / UML Paper  (http://arxiv.org/pdf/1505.00487)
@@ -345,8 +355,8 @@
⟡ MPI / Berkeley Paper (http://arxiv.org/pdf/1506.01698.pdf) 
  ⟡ Anna Rohrbach, Marcus Rohrbach, Bernt Schiele, The Long-Short Story of Movie Description, arXiv:1506.01698
⟡ Univ. Toronto / MIT Paper (http://arxiv.org/pdf/1506.06724.pdf) 
  ⟡ Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler, Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading 
Books, arXiv:1506.06724
  ⟡ Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler, Aligning Books and Movies: Towards Story-like Visual Explanations by Watching 
Movies and Reading Books, arXiv:1506.06724
⟡ Univ. Montreal Paper (http://arxiv.org/pdf/1507.01053.pdf) 
  ⟡ Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia Content using Attention-based Encoder-Decoder Networks, arXiv:1507.01053
⟡ TAU / USC paper (https://arxiv.org/pdf/1612.06950.pdf) 
@@ -354,11 +364,14 @@
Question Answering
!question_answering (https://cloud.githubusercontent.com/assets/5226447/8452068/ffe7b1f6-2022-11e5-87ab-4f6d4696c220.PNG)
(from Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, CVPR, 2015 SUNw:Scene Understanding workshop)
(from Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, CVPR, 2015 SUNw:Scene Understanding 
workshop)
⟡ Virginia Tech / MSR Web  (http://www.visualqa.org/) Paper  (http://arxiv.org/pdf/1505.00468)
  ⟡ Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, CVPR, 2015 SUNw:Scene Understanding workshop.
⟡ MPI / Berkeley Web  (https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/vision-and-language/visual-turing-challenge/) Paper  (http://arxiv.org/pdf/1505.01121)
  ⟡ Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, CVPR, 2015 SUNw:Scene Understanding 
workshop.
⟡ MPI / Berkeley Web  (https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/vision-and-language/visual-turing-challenge/) Paper  
(http://arxiv.org/pdf/1505.01121)
  ⟡ Mateusz Malinowski, Marcus Rohrbach, Mario Fritz, Ask Your Neurons: A Neural-based Approach to Answering Questions about Images, arXiv:1505.01121.
⟡ Toronto Paper  (http://arxiv.org/pdf/1505.02074) Dataset  (http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/)
  ⟡ Mengye Ren, Ryan Kiros, Richard Zemel, Image Question Answering: A Visual Semantic Embedding Model and a New Dataset, arXiv:1505.02074 / ICML 2015 deep learning workshop.
@@ -373,7 +386,8 @@
⟡ SNU + NAVER Paper (http://arxiv.org/abs/1606.01455) 
  ⟡ Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang, Multimodal Residual Learning for Visual QA, arXiv:1606:01455
⟡ UC Berkeley + Sony Paper (https://arxiv.org/pdf/1606.01847) 
  ⟡ Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, and Marcus Rohrbach, Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding, arXiv:1606.01847
  ⟡ Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, and Marcus Rohrbach, Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding, 
arXiv:1606.01847
⟡ Postech Paper (http://arxiv.org/pdf/1606.03647.pdf) 
  ⟡ Hyeonwoo Noh and Bohyung Han, Training Recurrent Answering Units with Joint Loss Minimization for VQA, arXiv:1606.03647
⟡ SNU + NAVER Paper (http://arxiv.org/abs/1610.04325) 
@@ -381,25 +395,29 @@
Image Generation
⟡ Convolutional / Recurrent Networks
  ⟡ Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu. "Conditional Image Generation with PixelCNN Decoders"Paper  (https://arxiv.org/pdf/1606.05328v2.pdf)Code  
(https://github.com/kundan2510/pixelCNN)
  ⟡ Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu. "Conditional Image Generation with PixelCNN Decoders"Paper  
(https://arxiv.org/pdf/1606.05328v2.pdf)Code  (https://github.com/kundan2510/pixelCNN)
  ⟡ Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, "Learning to Generate Chairs with Convolutional Neural Networks", CVPR, 2015. Paper  
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf)
  ⟡ Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, "DRAW: A Recurrent Neural Network For Image Generation", ICML, 2015. Paper (https://arxiv.org/pdf/1502.04623v2.pdf) 
  ⟡ Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, "DRAW: A Recurrent Neural Network For Image Generation", ICML, 2015. Paper 
(https://arxiv.org/pdf/1502.04623v2.pdf) 
⟡ Adversarial Networks
  ⟡ Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Generative Adversarial Networks, NIPS, 2014. Paper  
(http://arxiv.org/abs/1406.2661)
  ⟡ Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, NIPS, 2015. Paper  (http://arxiv.org/abs/1506.05751)
  ⟡ Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, NIPS, 2015. Paper  
(http://arxiv.org/abs/1506.05751)
  ⟡ Lucas Theis, Aäron van den Oord, Matthias Bethge, "A note on the evaluation of generative models", ICLR 2016. Paper (http://arxiv.org/abs/1511.01844) 
  ⟡ Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence, "Variationally Auto-Encoded Deep Gaussian Processes", ICLR 2016. Paper (http://arxiv.org/pdf/1511.06455v2.pdf) 
  ⟡ Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, "Generating Images from Captions with Attention", ICLR 2016, Paper (http://arxiv.org/pdf/1511.02793v2.pdf) 
  ⟡ Jost Tobias Springenberg, "Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks", ICLR 2016, Paper (http://arxiv.org/pdf/1511.06390v1.pdf) 
  ⟡ Harrison Edwards, Amos Storkey, "Censoring Representations with an Adversary", ICLR 2016, Paper (http://arxiv.org/pdf/1511.05897v3.pdf) 
  ⟡ Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii, "Distributional Smoothing with Virtual Adversarial Training", ICLR 2016, Paper (http://arxiv.org/pdf/1507.00677v8.pdf) 
  ⟡ Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros, "Generative Visual Manipulation on the Natural Image Manifold", ECCV 2016. Paper (https://arxiv.org/pdf/1609.03552v2.pdf) Code 
(https://github.com/junyanz/iGAN) Video (https://youtu.be/9c4z6YsBGQ0) 
  ⟡ Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii, "Distributional Smoothing with Virtual Adversarial Training", ICLR 2016, Paper 
(http://arxiv.org/pdf/1507.00677v8.pdf) 
  ⟡ Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros, "Generative Visual Manipulation on the Natural Image Manifold", ECCV 2016. Paper 
(https://arxiv.org/pdf/1609.03552v2.pdf) Code (https://github.com/junyanz/iGAN) Video (https://youtu.be/9c4z6YsBGQ0) 
⟡ Mixing Convolutional and Adversarial Networks
  ⟡ Alec Radford, Luke Metz, Soumith Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR 2016. Paper (http://arxiv.org/pdf/1511.06434.pdf) 
  ⟡ Alec Radford, Luke Metz, Soumith Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR 2016. Paper 
(http://arxiv.org/pdf/1511.06434.pdf) 
Other Topics
⟡ Visual Analogy Paper (https://web.eecs.umich.edu/~honglak/nips2015-analogy.pdf) 
@@ -426,8 +444,8 @@
  ⟡ Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang, DeepID3: Face Recognition with Very Deep Neural Networks, 2015. Paper  (http://arxiv.org/abs/1502.00873)
  ⟡ Florian Schroff, Dmitry Kalenichenko, James Philbin, FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR, 2015. Paper  (http://arxiv.org/abs/1503.03832)
⟡ Facial Landmark Detection
  ⟡ Yue Wu, Tal Hassner, KangGeon Kim, Gerard Medioni, Prem Natarajan, Facial Landmark Detection with Tweaked Convolutional Neural Networks, 2015. Paper  (http://arxiv.org/abs/1511.04031) Project  
(http://www.openu.ac.il/home/hassner/projects/tcnn_landmarks/)
  ⟡ Yue Wu, Tal Hassner, KangGeon Kim, Gerard Medioni, Prem Natarajan, Facial Landmark Detection with Tweaked Convolutional Neural Networks, 2015. Paper  (http://arxiv.org/abs/1511.04031) 
Project  (http://www.openu.ac.il/home/hassner/projects/tcnn_landmarks/)
Courses
⟡ Deep Vision