awesome-awesomeness/html/deepvision.html

<h1 id="awesome-deep-vision-awesome">Awesome Deep Vision <a
href="https://github.com/sindresorhus/awesome"><img
src="https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg"
alt="Awesome" /></a></h1>
<p>A curated list of deep learning resources for computer vision,
inspired by <a
href="https://github.com/ziadoz/awesome-php">awesome-php</a> and <a
href="https://github.com/jbhuang0604/awesome-computer-vision">awesome-computer-vision</a>.</p>
<p>Maintainers - <a href="https://github.com/kjw0612">Jiwon Kim</a>, <a
href="https://github.com/hmyeong">Heesoo Myeong</a>, <a
href="https://github.com/myungsub">Myungsub Choi</a>, <a
href="https://github.com/deruci">Jung Kwon Lee</a>, <a
href="https://github.com/jazzsaxmafia">Taeksoo Kim</a></p>
<p>The project is not actively maintained.</p>
<h2 id="contributing">Contributing</h2>
<p>Please feel free to <a
href="https://github.com/kjw0612/awesome-deep-vision/pulls">pull
requests</a> to add papers.</p>
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href="https://gitter.im/kjw0612/awesome-deep-vision?utm_source=badge&amp;utm_medium=badge&amp;utm_campaign=pr-badge&amp;utm_content=badge"><img
src="https://badges.gitter.im/Join%20Chat.svg"
alt="Join the chat at https://gitter.im/kjw0612/awesome-deep-vision" /></a></p>
<h2 id="sharing">Sharing</h2>
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<h2 id="table-of-contents">Table of Contents</h2>
<ul>
<li><a href="#papers">Papers</a>
<ul>
<li><a href="#imagenet-classification">ImageNet Classification</a></li>
<li><a href="#object-detection">Object Detection</a></li>
<li><a href="#object-tracking">Object Tracking</a></li>
<li><a href="#low-level-vision">Low-Level Vision</a>
<ul>
<li><a href="#super-resolution">Super-Resolution</a></li>
<li><a href="#other-applications">Other Applications</a></li>
</ul></li>
<li><a href="#edge-detection">Edge Detection</a></li>
<li><a href="#semantic-segmentation">Semantic Segmentation</a></li>
<li><a href="#visual-attention-and-saliency">Visual Attention and
Saliency</a></li>
<li><a href="#object-recognition">Object Recognition</a></li>
<li><a href="#human-pose-estimation">Human Pose Estimation</a></li>
<li><a href="#understanding-cnn">Understanding CNN</a></li>
<li><a href="#image-and-language">Image and Language</a>
<ul>
<li><a href="#image-captioning">Image Captioning</a></li>
<li><a href="#video-captioning">Video Captioning</a></li>
<li><a href="#question-answering">Question Answering</a></li>
</ul></li>
<li><a href="#image-generation">Image Generation</a></li>
<li><a href="#other-topics">Other Topics</a></li>
</ul></li>
<li><a href="#courses">Courses</a></li>
<li><a href="#books">Books</a></li>
<li><a href="#videos">Videos</a></li>
<li><a href="#software">Software</a>
<ul>
<li><a href="#framework">Framework</a></li>
<li><a href="#applications">Applications</a></li>
</ul></li>
<li><a href="#tutorials">Tutorials</a></li>
<li><a href="#blogs">Blogs</a></li>
</ul>
<h2 id="papers">Papers</h2>
<h3 id="imagenet-classification">ImageNet Classification</h3>
<p><img
src="https://cloud.githubusercontent.com/assets/5226447/8451949/327b9566-2022-11e5-8b34-53b4a64c13ad.PNG"
alt="classification" /> (from Alex Krizhevsky, Ilya Sutskever, Geoffrey
E. Hinton, ImageNet Classification with Deep Convolutional Neural
Networks, NIPS, 2012.) * Microsoft (Deep Residual Learning) [<a
href="http://arxiv.org/pdf/1512.03385v1.pdf">Paper</a>][<a
href="http://image-net.org/challenges/talks/ilsvrc2015_deep_residual_learning_kaiminghe.pdf">Slide</a>]
* Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual
Learning for Image Recognition, arXiv:1512.03385. * Microsoft
(PReLu/Weight Initialization) <a
href="http://arxiv.org/pdf/1502.01852">[Paper]</a> * Kaiming He, Xiangyu
Zhang, Shaoqing Ren, Jian Sun, Delving Deep into Rectifiers: Surpassing
Human-Level Performance on ImageNet Classification, arXiv:1502.01852. *
Batch Normalization <a
href="http://arxiv.org/pdf/1502.03167">[Paper]</a> * Sergey Ioffe,
Christian Szegedy, Batch Normalization: Accelerating Deep Network
Training by Reducing Internal Covariate Shift, arXiv:1502.03167. *
GoogLeNet <a href="http://arxiv.org/pdf/1409.4842">[Paper]</a> *
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich,
CVPR, 2015. * VGG-Net <a
href="http://www.robots.ox.ac.uk/~vgg/research/very_deep/">[Web]</a> <a
href="http://arxiv.org/pdf/1409.1556">[Paper]</a> * Karen Simonyan and
Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale
Visual Recognition, ICLR, 2015. * AlexNet <a
href="http://papers.nips.cc/book/advances-in-neural-information-processing-systems-25-2012">[Paper]</a>
* Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet
Classification with Deep Convolutional Neural Networks, NIPS, 2012.</p>
<h3 id="object-detection">Object Detection</h3>
<p><img
src="https://cloud.githubusercontent.com/assets/5226447/8452063/f76ba500-2022-11e5-8db1-2cd5d490e3b3.PNG"
alt="object_detection" /> (from Shaoqing Ren, Kaiming He, Ross Girshick,
Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region
Proposal Networks, arXiv:1506.01497.)</p>
<ul>
<li>PVANET <a href="https://arxiv.org/pdf/1608.08021">[Paper]</a> <a
href="https://github.com/sanghoon/pva-faster-rcnn">[Code]</a>
<ul>
<li>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</li>
</ul></li>
<li>OverFeat, NYU <a
href="http://arxiv.org/pdf/1312.6229.pdf">[Paper]</a>
<ul>
<li>OverFeat: Integrated Recognition, Localization and Detection using
Convolutional Networks, ICLR, 2014.</li>
</ul></li>
<li>R-CNN, UC Berkeley <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf">[Paper-CVPR14]</a>
<a href="http://arxiv.org/pdf/1311.2524">[Paper-arXiv14]</a>
<ul>
<li>Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, Rich
feature hierarchies for accurate object detection and semantic
segmentation, CVPR, 2014.</li>
</ul></li>
<li>SPP, Microsoft Research <a
href="http://arxiv.org/pdf/1406.4729">[Paper]</a>
<ul>
<li>Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Spatial Pyramid
Pooling in Deep Convolutional Networks for Visual Recognition, ECCV,
2014.</li>
</ul></li>
<li>Fast R-CNN, Microsoft Research <a
href="http://arxiv.org/pdf/1504.08083">[Paper]</a>
<ul>
<li>Ross Girshick, Fast R-CNN, arXiv:1504.08083.</li>
</ul></li>
<li>Faster R-CNN, Microsoft Research <a
href="http://arxiv.org/pdf/1506.01497">[Paper]</a>
<ul>
<li>Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN:
Towards Real-Time Object Detection with Region Proposal Networks,
arXiv:1506.01497.</li>
</ul></li>
<li>R-CNN minus R, Oxford <a
href="http://arxiv.org/pdf/1506.06981">[Paper]</a>
<ul>
<li>Karel Lenc, Andrea Vedaldi, R-CNN minus R, arXiv:1506.06981.</li>
</ul></li>
<li>End-to-end people detection in crowded scenes <a
href="http://arxiv.org/abs/1506.04878">[Paper]</a>
<ul>
<li>Russell Stewart, Mykhaylo Andriluka, End-to-end people detection in
crowded scenes, arXiv:1506.04878.</li>
</ul></li>
<li>You Only Look Once: Unified, Real-Time Object Detection <a
href="http://arxiv.org/abs/1506.02640">[Paper]</a>, <a
href="https://arxiv.org/abs/1612.08242">[Paper Version 2]</a>, <a
href="https://github.com/pjreddie/darknet">[C Code]</a>, <a
href="https://github.com/thtrieu/darkflow">[Tensorflow Code]</a>
<ul>
<li>Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, You Only
Look Once: Unified, Real-Time Object Detection, arXiv:1506.02640</li>
<li>Joseph Redmon, Ali Farhadi (Version 2)</li>
</ul></li>
<li>Inside-Outside Net <a
href="http://arxiv.org/abs/1512.04143">[Paper]</a>
<ul>
<li>Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick,
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and
Recurrent Neural Networks</li>
</ul></li>
<li>Deep Residual Network (Current State-of-the-Art) <a
href="http://arxiv.org/abs/1512.03385">[Paper]</a>
<ul>
<li>Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual
Learning for Image Recognition</li>
</ul></li>
<li>Weakly Supervised Object Localization with Multi-fold Multiple
Instance Learning [<a
href="http://arxiv.org/pdf/1503.00949.pdf">Paper</a>]</li>
<li>R-FCN <a href="https://arxiv.org/abs/1605.06409">[Paper]</a> <a
href="https://github.com/daijifeng001/R-FCN">[Code]</a>
<ul>
<li>Jifeng Dai, Yi Li, Kaiming He, Jian Sun, R-FCN: Object Detection via
Region-based Fully Convolutional Networks</li>
</ul></li>
<li>SSD <a href="https://arxiv.org/pdf/1512.02325v2.pdf">[Paper]</a> <a
href="https://github.com/weiliu89/caffe/tree/ssd">[Code]</a>
<ul>
<li>Wei Liu1, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott
Reed, Cheng-Yang Fu, Alexander C. Berg, SSD: Single Shot MultiBox
Detector, arXiv:1512.02325</li>
</ul></li>
<li>Speed/accuracy trade-offs for modern convolutional object detectors
<a href="https://arxiv.org/pdf/1611.10012v1.pdf">[Paper]</a>
<ul>
<li>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</li>
</ul></li>
</ul>
<h3 id="video-classification">Video Classification</h3>
<ul>
<li>Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, “Delving Deeper
into Convolutional Networks for Learning Video Representations”, ICLR
2016. [<a href="http://arxiv.org/pdf/1511.06432v4.pdf">Paper</a>]</li>
<li>Michael Mathieu, camille couprie, Yann Lecun, “Deep Multi Scale
Video Prediction Beyond Mean Square Error”, ICLR 2016. [<a
href="http://arxiv.org/pdf/1511.05440v6.pdf">Paper</a>]</li>
</ul>
<h3 id="object-tracking">Object Tracking</h3>
<ul>
<li>Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han, Online
Tracking by Learning Discriminative Saliency Map with Convolutional
Neural Network, arXiv:1502.06796. <a
href="http://arxiv.org/pdf/1502.06796">[Paper]</a></li>
<li>Hanxi Li, Yi Li and Fatih Porikli, DeepTrack: Learning
Discriminative Feature Representations by Convolutional Neural Networks
for Visual Tracking, BMVC, 2014. <a
href="http://www.bmva.org/bmvc/2014/files/paper028.pdf">[Paper]</a></li>
<li>N Wang, DY Yeung, Learning a Deep Compact Image Representation for
Visual Tracking, NIPS, 2013. <a
href="http://winsty.net/papers/dlt.pdf">[Paper]</a></li>
<li>Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang,
Hierarchical Convolutional Features for Visual Tracking, ICCV 2015 [<a
href="http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Ma_Hierarchical_Convolutional_Features_ICCV_2015_paper.pdf">Paper</a>]
[<a href="https://github.com/jbhuang0604/CF2">Code</a>]</li>
<li>Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, Visual
Tracking with fully Convolutional Networks, ICCV 2015 [<a
href="http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf">Paper</a>]
[<a href="https://github.com/scott89/FCNT">Code</a>]</li>
<li>Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional
Neural Networks for Visual Tracking, [<a
href="http://arxiv.org/pdf/1510.07945.pdf">Paper</a>] [<a
href="https://github.com/HyeonseobNam/MDNet">Code</a>] [<a
href="http://cvlab.postech.ac.kr/research/mdnet/">Project Page</a>]</li>
</ul>
<h3 id="low-level-vision">Low-Level Vision</h3>
<h4 id="super-resolution">Super-Resolution</h4>
<ul>
<li>Iterative Image Reconstruction
<ul>
<li>Sven Behnke: Learning Iterative Image Reconstruction. IJCAI, 2001.
<a
href="http://www.ais.uni-bonn.de/behnke/papers/ijcai01.pdf">[Paper]</a></li>
<li>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. <a
href="http://www.ais.uni-bonn.de/behnke/papers/ijcia01.pdf">[Paper]</a></li>
</ul></li>
<li>Super-Resolution (SRCNN) <a
href="http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html">[Web]</a> <a
href="http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepresolution.pdf">[Paper-ECCV14]</a>
<a href="http://arxiv.org/pdf/1501.00092.pdf">[Paper-arXiv15]</a>
<ul>
<li>Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep
Convolutional Network for Image Super-Resolution, ECCV, 2014.</li>
<li>Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Image
Super-Resolution Using Deep Convolutional Networks,
arXiv:1501.00092.</li>
</ul></li>
<li>Very Deep Super-Resolution
<ul>
<li>Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image
Super-Resolution Using Very Deep Convolutional Networks,
arXiv:1511.04587, 2015. <a
href="http://arxiv.org/abs/1511.04587">[Paper]</a></li>
</ul></li>
<li>Deeply-Recursive Convolutional Network
<ul>
<li>Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive
Convolutional Network for Image Super-Resolution, arXiv:1511.04491,
2015. <a href="http://arxiv.org/abs/1511.04491">[Paper]</a></li>
</ul></li>
<li>Casade-Sparse-Coding-Network
<ul>
<li>Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang,
Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015.
<a
href="http://www.ifp.illinois.edu/~dingliu2/iccv15/iccv15.pdf">[Paper]</a>
<a href="http://www.ifp.illinois.edu/~dingliu2/iccv15/">[Code]</a></li>
</ul></li>
<li>Perceptual Losses for Super-Resolution
<ul>
<li>Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for
Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155, 2016.
<a href="http://arxiv.org/abs/1603.08155">[Paper]</a> <a
href="http://cs.stanford.edu/people/jcjohns/papers/fast-style/fast-style-supp.pdf">[Supplementary]</a></li>
</ul></li>
<li>SRGAN
<ul>
<li>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. <a
href="https://arxiv.org/pdf/1609.04802v3.pdf">[Paper]</a></li>
</ul></li>
<li>Others
<ul>
<li>Osendorfer, Christian, Hubert Soyer, and Patrick van der Smagt,
Image Super-Resolution with Fast Approximate Convolutional Sparse
Coding, ICONIP, 2014. <a
href="http://brml.org/uploads/tx_sibibtex/281.pdf">[Paper
ICONIP-2014]</a></li>
</ul></li>
</ul>
<h4 id="other-applications">Other Applications</h4>
<ul>
<li>Optical Flow (FlowNet) <a
href="http://arxiv.org/pdf/1504.06852">[Paper]</a>
<ul>
<li>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.</li>
</ul></li>
<li>Compression Artifacts Reduction <a
href="http://arxiv.org/pdf/1504.06993">[Paper-arXiv15]</a>
<ul>
<li>Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang, Compression
Artifacts Reduction by a Deep Convolutional Network,
arXiv:1504.06993.</li>
</ul></li>
<li>Blur Removal
<ul>
<li>Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard
Schölkopf, Learning to Deblur, arXiv:1406.7444 <a
href="http://arxiv.org/pdf/1406.7444.pdf">[Paper]</a></li>
<li>Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a
Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR,
2015 <a href="http://arxiv.org/pdf/1503.00593">[Paper]</a></li>
</ul></li>
<li>Image Deconvolution <a href="http://lxu.me/projects/dcnn/">[Web]</a>
<a href="http://lxu.me/mypapers/dcnn_nips14.pdf">[Paper]</a>
<ul>
<li>Li Xu, Jimmy SJ. Ren, Ce Liu, Jiaya Jia, Deep Convolutional Neural
Network for Image Deconvolution, NIPS, 2014.</li>
</ul></li>
<li>Deep Edge-Aware Filter <a
href="http://jmlr.org/proceedings/papers/v37/xub15.pdf">[Paper]</a>
<ul>
<li>Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia, Deep
Edge-Aware Filters, ICML, 2015.</li>
</ul></li>
<li>Computing the Stereo Matching Cost with a Convolutional Neural
Network <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zbontar_Computing_the_Stereo_2015_CVPR_paper.pdf">[Paper]</a>
<ul>
<li>Jure Žbontar, Yann LeCun, Computing the Stereo Matching Cost with a
Convolutional Neural Network, CVPR, 2015.</li>
</ul></li>
<li>Colorful Image Colorization Richard Zhang, Phillip Isola, Alexei A.
Efros, ECCV, 2016 <a
href="http://arxiv.org/pdf/1603.08511.pdf">[Paper]</a>, <a
href="https://github.com/richzhang/colorization">[Code]</a></li>
<li>Ryan Dahl, <a href="http://tinyclouds.org/colorize/">[Blog]</a></li>
<li>Feature Learning by Inpainting<a
href="https://arxiv.org/pdf/1604.07379v1.pdf">[Paper]</a><a
href="https://github.com/pathak22/context-encoder">[Code]</a>
<ul>
<li>Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell,
Alexei A. Efros, Context Encoders: Feature Learning by Inpainting, CVPR,
2016</li>
</ul></li>
</ul>
<h3 id="edge-detection">Edge Detection</h3>
<p><img
src="https://cloud.githubusercontent.com/assets/5226447/8452371/93ca6f7e-2025-11e5-90f2-d428fd5ff7ac.PNG"
alt="edge_detection" /> (from Gedas Bertasius, Jianbo Shi, Lorenzo
Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down
Contour Detection, CVPR, 2015.)</p>
<ul>
<li>Holistically-Nested Edge Detection <a
href="http://arxiv.org/pdf/1504.06375">[Paper]</a> <a
href="https://github.com/s9xie/hed">[Code]</a>
<ul>
<li>Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection,
arXiv:1504.06375.</li>
</ul></li>
<li>DeepEdge <a href="http://arxiv.org/pdf/1412.1123">[Paper]</a>
<ul>
<li>Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A
Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection,
CVPR, 2015.</li>
</ul></li>
<li>DeepContour <a
href="http://mc.eistar.net/UpLoadFiles/Papers/DeepContour_cvpr15.pdf">[Paper]</a>
<ul>
<li>Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang,
DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing
Loss for Contour Detection, CVPR, 2015.</li>
</ul></li>
</ul>
<h3 id="semantic-segmentation">Semantic Segmentation</h3>
<p><img
src="https://cloud.githubusercontent.com/assets/5226447/8452076/0ba8340c-2023-11e5-88bc-bebf4509b6bb.PNG"
alt="semantic_segmantation" /> (from Jifeng Dai, Kaiming He, Jian Sun,
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks
for Semantic Segmentation, arXiv:1503.01640.) * PASCAL VOC2012 Challenge
Leaderboard (01 Sep. 2016) <img
src="https://cloud.githubusercontent.com/assets/3803777/18164608/c3678488-7038-11e6-9ec1-74a1542dce13.png"
alt="VOC2012_top_rankings" /> (from PASCAL VOC2012 <a
href="http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&amp;compid=6">leaderboards</a>)
* SEC: Seed, Expand and Constrain * Alexander Kolesnikov, Christoph
Lampert, Seed, Expand and Constrain: Three Principles for
Weakly-Supervised Image Segmentation, ECCV, 2016. <a
href="http://pub.ist.ac.at/~akolesnikov/files/ECCV2016/main.pdf">[Paper]</a>
<a href="https://github.com/kolesman/SEC">[Code]</a> * Adelaide *
Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient
piecewise training of deep structured models for semantic segmentation,
arXiv:1504.01013. <a href="http://arxiv.org/pdf/1504.01013">[Paper]</a>
(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. <a
href="http://arxiv.org/pdf/1506.02108">[Paper]</a> (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 <a
href="http://arxiv.org/pdf/1509.02634.pdf">[Paper]</a> (2nd ranked in
VOC 2012) * CentraleSuperBoundaries, INRIA <a
href="http://arxiv.org/pdf/1511.07386">[Paper]</a> * Iasonas Kokkinos,
Surpassing Humans in Boundary Detection using Deep Learning,
arXiv:1411.07386 (4th ranked in VOC 2012) * BoxSup <a
href="http://arxiv.org/pdf/1503.01640">[Paper]</a> * Jifeng Dai, Kaiming
He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise
Convolutional Networks for Semantic Segmentation, arXiv:1503.01640. (6th
ranked in VOC2012) * POSTECH * Hyeonwoo Noh, Seunghoon Hong, Bohyung
Han, Learning Deconvolution Network for Semantic Segmentation,
arXiv:1505.04366. <a href="http://arxiv.org/pdf/1505.04366">[Paper]</a>
(7th ranked in VOC2012) * Seunghoon Hong, Hyeonwoo Noh, Bohyung Han,
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation,
arXiv:1506.04924. <a href="http://arxiv.org/pdf/1506.04924">[Paper]</a>
* Seunghoon Hong,Junhyuk Oh, Bohyung Han, and Honglak Lee, Learning
Transferrable Knowledge for Semantic Segmentation with Deep
Convolutional Neural Network, arXiv:1512.07928 [<a
href="http://arxiv.org/pdf/1512.07928.pdf">Paper</a>] [<a
href="http://cvlab.postech.ac.kr/research/transfernet/">Project
Page</a>] * Conditional Random Fields as Recurrent Neural Networks <a
href="http://arxiv.org/pdf/1502.03240">[Paper]</a> * 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. <a
href="http://arxiv.org/pdf/1502.02734">[Paper]</a> (9th ranked in
VOC2012) * Zoom-out <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mostajabi_Feedforward_Semantic_Segmentation_2015_CVPR_paper.pdf">[Paper]</a>
* Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich,
Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015 *
Joint Calibration <a href="http://arxiv.org/pdf/1507.01581">[Paper]</a>
* Holger Caesar, Jasper Uijlings, Vittorio Ferrari, Joint Calibration
for Semantic Segmentation, arXiv:1507.01581. * Fully Convolutional
Networks for Semantic Segmentation <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf">[Paper-CVPR15]</a>
<a href="http://arxiv.org/pdf/1411.4038">[Paper-arXiv15]</a> * Jonathan
Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for
Semantic Segmentation, CVPR, 2015. * Hypercolumn <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hariharan_Hypercolumns_for_Object_2015_CVPR_paper.pdf">[Paper]</a>
* 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. <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Sharma_Deep_Hierarchical_Parsing_2015_CVPR_paper.pdf">[Paper]</a>
* Learning Hierarchical Features for Scene Labeling <a
href="http://yann.lecun.com/exdb/publis/pdf/farabet-icml-12.pdf">[Paper-ICML12]</a>
<a
href="http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf">[Paper-PAMI13]</a>
* 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 <a
href="http://mi.eng.cam.ac.uk/projects/segnet/">[Web]</a> * Vijay
Badrinarayanan, Alex Kendall and Roberto Cipolla “SegNet: A Deep
Convolutional Encoder-Decoder Architecture for Image Segmentation.”
arXiv preprint arXiv:1511.00561, 2015. <a
href="http://arxiv.org/abs/1511.00561">[Paper]</a> * 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. <a
href="http://arxiv.org/abs/1511.00561">[Paper]</a> * Princeton * Fisher
Yu, Vladlen Koltun, “Multi-Scale Context Aggregation by Dilated
Convolutions”, ICLR 2016, [<a
href="http://arxiv.org/pdf/1511.07122v2.pdf">Paper</a>] * Univ. of
Washington, Allen AI * Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar
Divvala, Yejin Choi, Ali Farhadi, “Segment-Phrase Table for Semantic
Segmentation, Visual Entailment and Paraphrasing”, ICCV, 2015, [<a
href="http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Izadinia_Segment-Phrase_Table_for_ICCV_2015_paper.pdf">Paper</a>]
* INRIA * Iasonas Kokkinos, “Pusing the Boundaries of Boundary Detection
Using deep Learning”, ICLR 2016, [<a
href="http://arxiv.org/pdf/1511.07386v2.pdf">Paper</a>] * UCSB *
Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, “Weakly supervised
graph based semantic segmentation by learning communities of
image-parts”, ICCV, 2015, [<a
href="http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Pourian_Weakly_Supervised_Graph_ICCV_2015_paper.pdf">Paper</a>]</p>
<h3 id="visual-attention-and-saliency">Visual Attention and
Saliency</h3>
<p><img
src="https://cloud.githubusercontent.com/assets/5226447/8492362/7ec65b88-2183-11e5-978f-017e45ddba32.png"
alt="saliency" /> (from Nian Liu, Junwei Han, Dingwen Zhang, Shifeng
Wen, Tianming Liu, Predicting Eye Fixations using Convolutional Neural
Networks, CVPR, 2015.)</p>
<ul>
<li>Mr-CNN <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Liu_Predicting_Eye_Fixations_2015_CVPR_paper.pdf">[Paper]</a>
<ul>
<li>Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu,
Predicting Eye Fixations using Convolutional Neural Networks, CVPR,
2015.</li>
</ul></li>
<li>Learning a Sequential Search for Landmarks <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Singh_Learning_a_Sequential_2015_CVPR_paper.pdf">[Paper]</a>
<ul>
<li>Saurabh Singh, Derek Hoiem, David Forsyth, Learning a Sequential
Search for Landmarks, CVPR, 2015.</li>
</ul></li>
<li>Multiple Object Recognition with Visual Attention <a
href="http://arxiv.org/pdf/1412.7755.pdf">[Paper]</a>
<ul>
<li>Jimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu, Multiple Object
Recognition with Visual Attention, ICLR, 2015.</li>
</ul></li>
<li>Recurrent Models of Visual Attention <a
href="http://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf">[Paper]</a>
<ul>
<li>Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu,
Recurrent Models of Visual Attention, NIPS, 2014.</li>
</ul></li>
</ul>
<h3 id="object-recognition">Object Recognition</h3>
<ul>
<li>Weakly-supervised learning with convolutional neural networks <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Oquab_Is_Object_Localization_2015_CVPR_paper.pdf">[Paper]</a>
<ul>
<li>Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic, Is object
localization for free? – Weakly-supervised learning with convolutional
neural networks, CVPR, 2015.</li>
</ul></li>
<li>FV-CNN <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Cimpoi_Deep_Filter_Banks_2015_CVPR_paper.pdf">[Paper]</a>
<ul>
<li>Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, Deep Filter Banks for
Texture Recognition and Segmentation, CVPR, 2015.</li>
</ul></li>
</ul>
<h3 id="human-pose-estimation">Human Pose Estimation</h3>
<ul>
<li>Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh, Realtime
Multi-Person 2D Pose Estimation using Part Affinity Fields, CVPR,
2017.</li>
<li>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.</li>
<li>Shih-En Wei, Varun Ramakrishna, Takeo Kanade, and Yaser Sheikh,
Convolutional pose machines, CVPR, 2016.</li>
<li>Alejandro Newell, Kaiyu Yang, and Jia Deng, Stacked hourglass
networks for human pose estimation, ECCV, 2016.</li>
<li>Tomas Pfister, James Charles, and Andrew Zisserman, Flowing convnets
for human pose estimation in videos, ICCV, 2015.</li>
<li>Jonathan J. Tompson, Arjun Jain, Yann LeCun, Christoph Bregler,
Joint training of a convolutional network and a graphical model for
human pose estimation, NIPS, 2014.</li>
</ul>
<h3 id="understanding-cnn">Understanding CNN</h3>
<p><img
src="https://cloud.githubusercontent.com/assets/5226447/8452083/1aaa0066-2023-11e5-800b-2248ead51584.PNG"
alt="understanding" /> (from Aravindh Mahendran, Andrea Vedaldi,
Understanding Deep Image Representations by Inverting Them, CVPR,
2015.)</p>
<ul>
<li>Karel Lenc, Andrea Vedaldi, Understanding image representations by
measuring their equivariance and equivalence, CVPR, 2015. <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lenc_Understanding_Image_Representations_2015_CVPR_paper.pdf">[Paper]</a></li>
<li>Anh Nguyen, Jason Yosinski, Jeff Clune, Deep Neural Networks are
Easily Fooled:High Confidence Predictions for Unrecognizable Images,
CVPR, 2015. <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf">[Paper]</a></li>
<li>Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image
Representations by Inverting Them, CVPR, 2015. <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf">[Paper]</a></li>
<li>Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio
Torralba, Object Detectors Emerge in Deep Scene CNNs, ICLR, 2015. <a
href="http://arxiv.org/abs/1412.6856">[arXiv Paper]</a></li>
<li>Alexey Dosovitskiy, Thomas Brox, Inverting Visual Representations
with Convolutional Networks, arXiv, 2015. <a
href="http://arxiv.org/abs/1506.02753">[Paper]</a></li>
<li>Matthrew Zeiler, Rob Fergus, Visualizing and Understanding
Convolutional Networks, ECCV, 2014. <a
href="https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf">[Paper]</a></li>
</ul>
<h3 id="image-and-language">Image and Language</h3>
<h4 id="image-captioning">Image Captioning</h4>
<p><img
src="https://cloud.githubusercontent.com/assets/5226447/8452051/e8f81030-2022-11e5-85db-c68e7d8251ce.PNG"
alt="image_captioning" /> (from Andrej Karpathy, Li Fei-Fei, Deep
Visual-Semantic Alignments for Generating Image Description, CVPR,
2015.)</p>
<ul>
<li>UCLA / Baidu <a href="http://arxiv.org/pdf/1410.1090">[Paper]</a>
<ul>
<li>Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan L. Yuille, Explain
Images with Multimodal Recurrent Neural Networks, arXiv:1410.1090.</li>
</ul></li>
<li>Toronto <a href="http://arxiv.org/pdf/1411.2539">[Paper]</a>
<ul>
<li>Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel, Unifying
Visual-Semantic Embeddings with Multimodal Neural Language Models,
arXiv:1411.2539.</li>
</ul></li>
<li>Berkeley <a href="http://arxiv.org/pdf/1411.4389">[Paper]</a>
<ul>
<li>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.</li>
</ul></li>
<li>Google <a href="http://arxiv.org/pdf/1411.4555">[Paper]</a>
<ul>
<li>Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan, Show
and Tell: A Neural Image Caption Generator, arXiv:1411.4555.</li>
</ul></li>
<li>Stanford <a
href="http://cs.stanford.edu/people/karpathy/deepimagesent/">[Web]</a>
<a
href="http://cs.stanford.edu/people/karpathy/cvpr2015.pdf">[Paper]</a>
<ul>
<li>Andrej Karpathy, Li Fei-Fei, Deep Visual-Semantic Alignments for
Generating Image Description, CVPR, 2015.</li>
</ul></li>
<li>UML / UT <a href="http://arxiv.org/pdf/1412.4729">[Paper]</a>
<ul>
<li>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.</li>
</ul></li>
<li>CMU / Microsoft <a
href="http://arxiv.org/pdf/1411.5654">[Paper-arXiv]</a> <a
href="http://www.cs.cmu.edu/~xinleic/papers/cvpr15_rnn.pdf">[Paper-CVPR]</a>
<ul>
<li>Xinlei Chen, C. Lawrence Zitnick, Learning a Recurrent Visual
Representation for Image Caption Generation, arXiv:1411.5654.</li>
<li>Xinlei Chen, C. Lawrence Zitnick, Mind’s Eye: A Recurrent Visual
Representation for Image Caption Generation, CVPR 2015</li>
</ul></li>
<li>Microsoft <a href="http://arxiv.org/pdf/1411.4952">[Paper]</a>
<ul>
<li>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.</li>
</ul></li>
<li>Univ. Montreal / Univ. Toronto [<a
href="http://kelvinxu.github.io/projects/capgen.html">Web</a>] [<a
href="http://www.cs.toronto.edu/~zemel/documents/captionAttn.pdf">Paper</a>]
<ul>
<li>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</li>
</ul></li>
<li>Idiap / EPFL / Facebook [<a
href="http://arxiv.org/pdf/1502.03671">Paper</a>]
<ul>
<li>Remi Lebret, Pedro O. Pinheiro, Ronan Collobert, Phrase-based Image
Captioning, arXiv:1502.03671 / ICML 2015</li>
</ul></li>
<li>UCLA / Baidu [<a href="http://arxiv.org/pdf/1504.06692">Paper</a>]
<ul>
<li>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</li>
</ul></li>
<li>MS + Berkeley
<ul>
<li>Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C.
Lawrence Zitnick, Exploring Nearest Neighbor Approaches for Image
Captioning, arXiv:1505.04467 [<a
href="http://arxiv.org/pdf/1505.04467.pdf">Paper</a>]</li>
<li>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 [<a
href="http://arxiv.org/pdf/1505.01809.pdf">Paper</a>]</li>
</ul></li>
<li>Adelaide [<a href="http://arxiv.org/pdf/1506.01144.pdf">Paper</a>]
<ul>
<li>Qi Wu, Chunhua Shen, Anton van den Hengel, Lingqiao Liu, Anthony
Dick, Image Captioning with an Intermediate Attributes Layer,
arXiv:1506.01144</li>
</ul></li>
<li>Tilburg [<a href="http://arxiv.org/pdf/1506.03694.pdf">Paper</a>]
<ul>
<li>Grzegorz Chrupala, Akos Kadar, Afra Alishahi, Learning language
through pictures, arXiv:1506.03694</li>
</ul></li>
<li>Univ. Montreal [<a
href="http://arxiv.org/pdf/1507.01053.pdf">Paper</a>]
<ul>
<li>Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia
Content using Attention-based Encoder-Decoder Networks,
arXiv:1507.01053</li>
</ul></li>
<li>Cornell [<a href="http://arxiv.org/pdf/1508.02091.pdf">Paper</a>]
<ul>
<li>Jack Hessel, Nicolas Savva, Michael J. Wilber, Image Representations
and New Domains in Neural Image Captioning, arXiv:1508.02091</li>
</ul></li>
<li>MS + City Univ. of HongKong [<a
href="http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yao_Learning_Query_and_ICCV_2015_paper.pdf">Paper</a>]
<ul>
<li>Ting Yao, Tao Mei, and Chong-Wah Ngo, “Learning Query and Image
Similarities with Ranking Canonical Correlation Analysis”, ICCV,
2015</li>
</ul></li>
</ul>
<h4 id="video-captioning">Video Captioning</h4>
<ul>
<li>Berkeley <a href="http://jeffdonahue.com/lrcn/">[Web]</a> <a
href="http://arxiv.org/pdf/1411.4389.pdf">[Paper]</a>
<ul>
<li>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.</li>
</ul></li>
<li>UT / UML / Berkeley <a
href="http://arxiv.org/pdf/1412.4729">[Paper]</a>
<ul>
<li>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.</li>
</ul></li>
<li>Microsoft <a href="http://arxiv.org/pdf/1505.01861">[Paper]</a>
<ul>
<li>Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui, Joint
Modeling Embedding and Translation to Bridge Video and Language,
arXiv:1505.01861.</li>
</ul></li>
<li>UT / Berkeley / UML <a
href="http://arxiv.org/pdf/1505.00487">[Paper]</a>
<ul>
<li>Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond
Mooney, Trevor Darrell, Kate Saenko, Sequence to Sequence–Video to Text,
arXiv:1505.00487.</li>
</ul></li>
<li>Univ. Montreal / Univ. Sherbrooke [<a
href="http://arxiv.org/pdf/1502.08029.pdf">Paper</a>]
<ul>
<li>Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher
Pal, Hugo Larochelle, Aaron Courville, Describing Videos by Exploiting
Temporal Structure, arXiv:1502.08029</li>
</ul></li>
<li>MPI / Berkeley [<a
href="http://arxiv.org/pdf/1506.01698.pdf">Paper</a>]
<ul>
<li>Anna Rohrbach, Marcus Rohrbach, Bernt Schiele, The Long-Short Story
of Movie Description, arXiv:1506.01698</li>
</ul></li>
<li>Univ. Toronto / MIT [<a
href="http://arxiv.org/pdf/1506.06724.pdf">Paper</a>]
<ul>
<li>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</li>
</ul></li>
<li>Univ. Montreal [<a
href="http://arxiv.org/pdf/1507.01053.pdf">Paper</a>]
<ul>
<li>Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia
Content using Attention-based Encoder-Decoder Networks,
arXiv:1507.01053</li>
</ul></li>
<li>TAU / USC [<a href="https://arxiv.org/pdf/1612.06950.pdf">paper</a>]
<ul>
<li>Dotan Kaufman, Gil Levi, Tal Hassner, Lior Wolf, Temporal
Tessellation for Video Annotation and Summarization,
arXiv:1612.06950.</li>
</ul></li>
</ul>
<h4 id="question-answering">Question Answering</h4>
<p><img
src="https://cloud.githubusercontent.com/assets/5226447/8452068/ffe7b1f6-2022-11e5-87ab-4f6d4696c220.PNG"
alt="question_answering" /> (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)</p>
<ul>
<li>Virginia Tech / MSR <a href="http://www.visualqa.org/">[Web]</a> <a
href="http://arxiv.org/pdf/1505.00468">[Paper]</a>
<ul>
<li>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.</li>
</ul></li>
<li>MPI / Berkeley <a
href="https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/vision-and-language/visual-turing-challenge/">[Web]</a>
<a href="http://arxiv.org/pdf/1505.01121">[Paper]</a>
<ul>
<li>Mateusz Malinowski, Marcus Rohrbach, Mario Fritz, Ask Your Neurons:
A Neural-based Approach to Answering Questions about Images,
arXiv:1505.01121.</li>
</ul></li>
<li>Toronto <a href="http://arxiv.org/pdf/1505.02074">[Paper]</a> <a
href="http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/">[Dataset]</a>
<ul>
<li>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.</li>
</ul></li>
<li>Baidu / UCLA <a href="http://arxiv.org/pdf/1505.05612">[Paper]</a>
<a href="">[Dataset]</a>
<ul>
<li>Hauyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, Wei Xu,
Are You Talking to a Machine? Dataset and Methods for Multilingual Image
Question Answering, arXiv:1505.05612.</li>
</ul></li>
<li>POSTECH [<a href="http://arxiv.org/pdf/1511.05756.pdf">Paper</a>]
[<a href="http://cvlab.postech.ac.kr/research/dppnet/">Project Page</a>]
<ul>
<li>Hyeonwoo Noh, Paul Hongsuck Seo, and Bohyung Han, Image Question
Answering using Convolutional Neural Network with Dynamic Parameter
Prediction, arXiv:1511.05765</li>
</ul></li>
<li>CMU / Microsoft Research [<a
href="http://arxiv.org/pdf/1511.02274v2.pdf">Paper</a>]
<ul>
<li>Yang, Z., He, X., Gao, J., Deng, L., &amp; Smola, A. (2015). Stacked
Attention Networks for Image Question Answering. arXiv:1511.02274.</li>
</ul></li>
<li>MetaMind [<a href="http://arxiv.org/pdf/1603.01417v1.pdf">Paper</a>]
<ul>
<li>Xiong, Caiming, Stephen Merity, and Richard Socher. “Dynamic Memory
Networks for Visual and Textual Question Answering.” arXiv:1603.01417
(2016).</li>
</ul></li>
<li>SNU + NAVER [<a href="http://arxiv.org/abs/1606.01455">Paper</a>]
<ul>
<li>Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim,
Jung-Woo Ha, Byoung-Tak Zhang, <em>Multimodal Residual Learning for
Visual QA</em>, arXiv:1606:01455</li>
</ul></li>
<li>UC Berkeley + Sony [<a
href="https://arxiv.org/pdf/1606.01847">Paper</a>]
<ul>
<li>Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor
Darrell, and Marcus Rohrbach, <em>Multimodal Compact Bilinear Pooling
for Visual Question Answering and Visual Grounding</em>,
arXiv:1606.01847</li>
</ul></li>
<li>Postech [<a href="http://arxiv.org/pdf/1606.03647.pdf">Paper</a>]
<ul>
<li>Hyeonwoo Noh and Bohyung Han, <em>Training Recurrent Answering Units
with Joint Loss Minimization for VQA</em>, arXiv:1606.03647</li>
</ul></li>
<li>SNU + NAVER [<a href="http://arxiv.org/abs/1610.04325">Paper</a>]
<ul>
<li>Jin-Hwa Kim, Kyoung Woon On, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak
Zhang, <em>Hadamard Product for Low-rank Bilinear Pooling</em>,
arXiv:1610.04325.</li>
</ul></li>
</ul>
<h3 id="image-generation">Image Generation</h3>
<ul>
<li>Convolutional / Recurrent Networks
<ul>
<li>Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt,
Alex Graves, Koray Kavukcuoglu. “Conditional Image Generation with
PixelCNN Decoders”<a
href="https://arxiv.org/pdf/1606.05328v2.pdf">[Paper]</a><a
href="https://github.com/kundan2510/pixelCNN">[Code]</a></li>
<li>Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, “Learning
to Generate Chairs with Convolutional Neural Networks”, CVPR, 2015. <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf">[Paper]</a></li>
<li>Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende,
Daan Wierstra, “DRAW: A Recurrent Neural Network For Image Generation”,
ICML, 2015. [<a
href="https://arxiv.org/pdf/1502.04623v2.pdf">Paper</a>]</li>
</ul></li>
<li>Adversarial Networks
<ul>
<li>Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David
Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Generative
Adversarial Networks, NIPS, 2014. <a
href="http://arxiv.org/abs/1406.2661">[Paper]</a></li>
<li>Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, Deep
Generative Image Models using a Laplacian Pyramid of Adversarial
Networks, NIPS, 2015. <a
href="http://arxiv.org/abs/1506.05751">[Paper]</a></li>
<li>Lucas Theis, Aäron van den Oord, Matthias Bethge, “A note on the
evaluation of generative models”, ICLR 2016. [<a
href="http://arxiv.org/abs/1511.01844">Paper</a>]</li>
<li>Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence,
“Variationally Auto-Encoded Deep Gaussian Processes”, ICLR 2016. [<a
href="http://arxiv.org/pdf/1511.06455v2.pdf">Paper</a>]</li>
<li>Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov,
“Generating Images from Captions with Attention”, ICLR 2016, [<a
href="http://arxiv.org/pdf/1511.02793v2.pdf">Paper</a>]</li>
<li>Jost Tobias Springenberg, “Unsupervised and Semi-supervised Learning
with Categorical Generative Adversarial Networks”, ICLR 2016, [<a
href="http://arxiv.org/pdf/1511.06390v1.pdf">Paper</a>]</li>
<li>Harrison Edwards, Amos Storkey, “Censoring Representations with an
Adversary”, ICLR 2016, [<a
href="http://arxiv.org/pdf/1511.05897v3.pdf">Paper</a>]</li>
<li>Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin
Ishii, “Distributional Smoothing with Virtual Adversarial Training”,
ICLR 2016, [<a
href="http://arxiv.org/pdf/1507.00677v8.pdf">Paper</a>]</li>
<li>Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros,
“Generative Visual Manipulation on the Natural Image Manifold”, ECCV
2016. [<a href="https://arxiv.org/pdf/1609.03552v2.pdf">Paper</a>] [<a
href="https://github.com/junyanz/iGAN">Code</a>] [<a
href="https://youtu.be/9c4z6YsBGQ0">Video</a>]</li>
</ul></li>
<li>Mixing Convolutional and Adversarial Networks
<ul>
<li>Alec Radford, Luke Metz, Soumith Chintala, “Unsupervised
Representation Learning with Deep Convolutional Generative Adversarial
Networks”, ICLR 2016. [<a
href="http://arxiv.org/pdf/1511.06434.pdf">Paper</a>]</li>
</ul></li>
</ul>
<h3 id="other-topics">Other Topics</h3>
<ul>
<li>Visual Analogy [<a
href="https://web.eecs.umich.edu/~honglak/nips2015-analogy.pdf">Paper</a>]
<ul>
<li>Scott Reed, Yi Zhang, Yuting Zhang, Honglak Lee, Deep Visual Analogy
Making, NIPS, 2015</li>
</ul></li>
<li>Surface Normal Estimation <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Designing_Deep_Networks_2015_CVPR_paper.pdf">[Paper]</a>
<ul>
<li>Xiaolong Wang, David F. Fouhey, Abhinav Gupta, Designing Deep
Networks for Surface Normal Estimation, CVPR, 2015.</li>
</ul></li>
<li>Action Detection <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Gkioxari_Finding_Action_Tubes_2015_CVPR_paper.pdf">[Paper]</a>
<ul>
<li>Georgia Gkioxari, Jitendra Malik, Finding Action Tubes, CVPR,
2015.</li>
</ul></li>
<li>Crowd Counting <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhang_Cross-Scene_Crowd_Counting_2015_CVPR_paper.pdf">[Paper]</a>
<ul>
<li>Cong Zhang, Hongsheng Li, Xiaogang Wang, Xiaokang Yang, Cross-scene
Crowd Counting via Deep Convolutional Neural Networks, CVPR, 2015.</li>
</ul></li>
<li>3D Shape Retrieval <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Sketch-Based_3D_Shape_2015_CVPR_paper.pdf">[Paper]</a>
<ul>
<li>Fang Wang, Le Kang, Yi Li, Sketch-based 3D Shape Retrieval using
Convolutional Neural Networks, CVPR, 2015.</li>
</ul></li>
<li>Weakly-supervised Classification
<ul>
<li>Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell,
“Auxiliary Image Regularization for Deep CNNs with Noisy Labels”, ICLR
2016, [<a href="http://arxiv.org/pdf/1511.07069v2.pdf">Paper</a>]</li>
</ul></li>
<li>Artistic Style <a href="http://arxiv.org/abs/1508.06576">[Paper]</a>
<a href="https://github.com/jcjohnson/neural-style">[Code]</a>
<ul>
<li>Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, A Neural
Algorithm of Artistic Style.</li>
</ul></li>
<li>Human Gaze Estimation
<ul>
<li>Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling,
Appearance-Based Gaze Estimation in the Wild, CVPR, 2015. <a
href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhang_Appearance-Based_Gaze_Estimation_2015_CVPR_paper.pdf">[Paper]</a>
<a
href="https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild-mpiigaze/">[Website]</a></li>
</ul></li>
<li>Face Recognition
<ul>
<li>Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, Lior Wolf, DeepFace:
Closing the Gap to Human-Level Performance in Face Verification, CVPR,
2014. <a
href="https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf">[Paper]</a></li>
<li>Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang, DeepID3: Face
Recognition with Very Deep Neural Networks, 2015. <a
href="http://arxiv.org/abs/1502.00873">[Paper]</a></li>
<li>Florian Schroff, Dmitry Kalenichenko, James Philbin, FaceNet: A
Unified Embedding for Face Recognition and Clustering, CVPR, 2015. <a
href="http://arxiv.org/abs/1503.03832">[Paper]</a></li>
</ul></li>
<li>Facial Landmark Detection
<ul>
<li>Yue Wu, Tal Hassner, KangGeon Kim, Gerard Medioni, Prem Natarajan,
Facial Landmark Detection with Tweaked Convolutional Neural Networks,
2015. <a href="http://arxiv.org/abs/1511.04031">[Paper]</a> <a
href="http://www.openu.ac.il/home/hassner/projects/tcnn_landmarks/">[Project]</a></li>
</ul></li>
</ul>
<h2 id="courses">Courses</h2>
<ul>
<li>Deep Vision
<ul>
<li>[Stanford] <a href="http://cs231n.stanford.edu/">CS231n:
Convolutional Neural Networks for Visual Recognition</a></li>
<li>[CUHK] <a
href="https://piazza.com/cuhk.edu.hk/spring2015/eleg5040/home">ELEG
5040: Advanced Topics in Signal Processing(Introduction to Deep
Learning)</a></li>
</ul></li>
<li>More Deep Learning
<ul>
<li>[Stanford] <a href="http://cs224d.stanford.edu/">CS224d: Deep
Learning for Natural Language Processing</a></li>
<li>[Oxford] <a
href="https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/">Deep
Learning by Prof. Nando de Freitas</a></li>
<li>[NYU] <a
href="http://cilvr.cs.nyu.edu/doku.php?id=courses:deeplearning2014:start">Deep
Learning by Prof. Yann LeCun</a></li>
</ul></li>
</ul>
<h2 id="books">Books</h2>
<ul>
<li>Free Online Books
<ul>
<li><a href="http://www.iro.umontreal.ca/~bengioy/dlbook/">Deep Learning
by Ian Goodfellow, Yoshua Bengio, and Aaron Courville</a></li>
<li><a href="http://neuralnetworksanddeeplearning.com/">Neural Networks
and Deep Learning by Michael Nielsen</a></li>
<li><a href="http://deeplearning.net/tutorial/deeplearning.pdf">Deep
Learning Tutorial by LISA lab, University of Montreal</a></li>
</ul></li>
</ul>
<h2 id="videos">Videos</h2>
<ul>
<li>Talks
<ul>
<li><a href="https://www.youtube.com/watch?v=n1ViNeWhC24">Deep Learning,
Self-Taught Learning and Unsupervised Feature Learning By Andrew
Ng</a></li>
<li><a href="https://www.youtube.com/watch?v=vShMxxqtDDs">Recent
Developments in Deep Learning By Geoff Hinton</a></li>
<li><a href="https://www.youtube.com/watch?v=sc-KbuZqGkI">The
Unreasonable Effectiveness of Deep Learning by Yann LeCun</a></li>
<li><a href="https://www.youtube.com/watch?v=4xsVFLnHC_0">Deep Learning
of Representations by Yoshua bengio</a></li>
</ul></li>
</ul>
<h2 id="software">Software</h2>
<h3 id="framework">Framework</h3>
<ul>
<li>Tensorflow: An open source software library for numerical
computation using data flow graph by Google [<a
href="https://www.tensorflow.org/">Web</a>]</li>
<li>Torch7: Deep learning library in Lua, used by Facebook and Google
Deepmind [<a href="http://torch.ch/">Web</a>]
<ul>
<li>Torch-based deep learning libraries: [<a
href="https://github.com/torchnet/torchnet">torchnet</a>],</li>
</ul></li>
<li>Caffe: Deep learning framework by the BVLC [<a
href="http://caffe.berkeleyvision.org/">Web</a>]</li>
<li>Theano: Mathematical library in Python, maintained by LISA lab [<a
href="http://deeplearning.net/software/theano/">Web</a>]
<ul>
<li>Theano-based deep learning libraries: [<a
href="http://deeplearning.net/software/pylearn2/">Pylearn2</a>], [<a
href="https://github.com/mila-udem/blocks">Blocks</a>], [<a
href="http://keras.io/">Keras</a>], [<a
href="https://github.com/Lasagne/Lasagne">Lasagne</a>]</li>
</ul></li>
<li>MatConvNet: CNNs for MATLAB [<a
href="http://www.vlfeat.org/matconvnet/">Web</a>]</li>
<li>MXNet: A flexible and efficient deep learning library for
heterogeneous distributed systems with multi-language support [<a
href="http://mxnet.io/">Web</a>]</li>
<li>Deepgaze: A computer vision library for human-computer interaction
based on CNNs [<a
href="https://github.com/mpatacchiola/deepgaze">Web</a>]</li>
</ul>
<h3 id="applications">Applications</h3>
<ul>
<li>Adversarial Training
<ul>
<li>Code and hyperparameters for the paper “Generative Adversarial
Networks” <a
href="https://github.com/goodfeli/adversarial">[Web]</a></li>
</ul></li>
<li>Understanding and Visualizing
<ul>
<li>Source code for “Understanding Deep Image Representations by
Inverting Them,” CVPR, 2015. <a
href="https://github.com/aravindhm/deep-goggle">[Web]</a></li>
</ul></li>
<li>Semantic Segmentation
<ul>
<li>Source code for the paper “Rich feature hierarchies for accurate
object detection and semantic segmentation,” CVPR, 2014. <a
href="https://github.com/rbgirshick/rcnn">[Web]</a></li>
<li>Source code for the paper “Fully Convolutional Networks for Semantic
Segmentation,” CVPR, 2015. <a
href="https://github.com/longjon/caffe/tree/future">[Web]</a></li>
</ul></li>
<li>Super-Resolution
<ul>
<li>Image Super-Resolution for Anime-Style-Art <a
href="https://github.com/nagadomi/waifu2x">[Web]</a></li>
</ul></li>
<li>Edge Detection
<ul>
<li>Source code for the paper “DeepContour: A Deep Convolutional Feature
Learned by Positive-Sharing Loss for Contour Detection,” CVPR, 2015. <a
href="https://github.com/shenwei1231/DeepContour">[Web]</a></li>
<li>Source code for the paper “Holistically-Nested Edge Detection”, ICCV
2015. <a href="https://github.com/s9xie/hed">[Web]</a></li>
</ul></li>
</ul>
<h2 id="tutorials">Tutorials</h2>
<ul>
<li>[CVPR 2014] <a
href="https://sites.google.com/site/deeplearningcvpr2014/">Tutorial on
Deep Learning in Computer Vision</a></li>
<li>[CVPR 2015] <a href="https://github.com/soumith/cvpr2015">Applied
Deep Learning for Computer Vision with Torch</a></li>
</ul>
<h2 id="blogs">Blogs</h2>
<ul>
<li><a
href="http://www.computervisionblog.com/2015/06/deep-down-rabbit-hole-cvpr-2015-and.html">Deep
down the rabbit hole: CVPR 2015 and beyond@Tombone’s Computer Vision
Blog</a></li>
<li><a
href="http://zoyathinks.blogspot.kr/2015/06/cvpr-recap-and-where-were-going.html">CVPR
recap and where we’re going@Zoya Bylinskii (MIT PhD Student)’s
Blog</a></li>
<li><a
href="http://www.wired.com/2015/06/facebook-googles-fake-brains-spawn-new-visual-reality/">Facebook’s
AI Painting@Wired</a></li>
<li><a
href="http://googleresearch.blogspot.kr/2015/06/inceptionism-going-deeper-into-neural.html">Inceptionism:
Going Deeper into Neural Networks@Google Research</a></li>
<li><a href="http://peterroelants.github.io/">Implementing Neural
networks</a></li>
</ul>