Top 100 Most Cited Computer Vision Papers

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ImageNet Image Classification

• ResNet Deep Residual Learning for Image Recognition, Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
• PRelu Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
• Batch Normalization Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Sergey Ioffe, Christian Szegedy
• GoogLeNet Going Deeper with Convolutions, Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
• VGG-Net Very Deep Convolutional Networks for Large-Scale Image Recognition, Karen Simonyan, Andrew Zisserman
• AlexNet ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

Object Detection

• PVANET PVANET：Deep but Lightweight Neural Networks for Real-time Object Detection, Kye-Hyeon Kim, Sanghoon Hong, Byungseok Roh, Yeongjae Cheon, Minje Park
• OverFeat OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks，ICLR 2014, Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann LeCun
• R-CNN Rich feature hierarchies for accurate object detection and semantic segmentation，CVPR 2014, Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
• SPP Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, ECCV 2014, Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
• Fast R-CNN Fast R-CNN, Ross Girshick
• Faster R-CNN Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
• R-CNN minus R R-CNN minus R, Karel Lenc, Andrea Vedaldi
• End-to-end People Detection in Crowded Scenes, Russell Stewart, Mykhaylo Andriluka
• You Only Look Once: Unified, Real-Time Object Detection, Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi](https://arxiv.org/pdf/1506.02640.pdf)
• Inside-Outside Net Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks, Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick
• R-FCN: Object Detection via Region-based Fully Convolutional Networks, Jifeng Dai, Yi Li, Kaiming He, Jian Sun
• SSD: Single Shot MultiBox Detector, Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg
• Speed/accuracy trade-offs for modern convolutional object detector, Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Kevin Murphy

Object Tracking

• Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network, Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han , arXiv:1502.06796.
• DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, Hanxi Li, Yi Li and Fatih Porikli, BMVC, 2014.
• Learning a Deep Compact Image Representation for Visual Tracking, N Wang, DY Yeung, NIPS, 2013.
• Hierarchical Convolutional Features for Visual Tracking, Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, ICCV 2015
• Visual Tracking with fully Convolutional Networks, Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, ICCV 2015
• Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, Hyeonseob Namand Bohyung Han

Object Recognition

• Weakly-supervised learning with convolutional neural networks, Maxime Oquab，Leon Bottou，Ivan Laptev，Josef Sivic，CVPR，2015
• FV-CNN Deep Filter Banks for Texture Recognition and Segmentation, Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, CVPR, 2015.

Human Pose Estimation

• Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields, Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh, CVPR, 2017.
• Deepcut: Joint subset partition and labeling for multi person pose estimation, Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter Gehler, and Bernt Schiele, CVPR, 2016.
• Convolutional pose machines, Shih-En Wei, Varun Ramakrishna, Takeo Kanade, and Yaser Sheikh, CVPR, 2016.
• Stacked hourglass networks（Stacked hourglass networks for human pose estimation, Alejandro Newell, Kaiyu Yang, and Jia Deng, ECCV, 2016.
• Flowing convnets for human pose estimation in videos, Tomas Pfister, James Charles, and Andrew Zisserman, ICCV, 2015.
• Joint training of a convolutional network and a graphical model for human pose estimation, Jonathan J. Tompson, Arjun Jain, Yann LeCun, Christoph Bregler, NIPS, 2014.

Understanding CNN

• Understanding image representations by measuring their equivariance and equivalence, Karel Lenc, Andrea Vedaldi, CVPR, 2015.
• Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images, Anh Nguyen, Jason Yosinski, Jeff Clune, CVPR, 2015.
• Understanding Deep Image Representations by Inverting Them, Aravindh Mahendran, Andrea Vedaldi, CVPR, 2015
• Object Detectors Emerge in Deep Scene CNNs, Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, ICLR, 2015.
• Inverting Visual Representations with Convolutional Networks, Alexey Dosovitskiy, Thomas Brox, arXiv, 2015.
• Visualizing and Understanding Convolutional Networks, Matthrew Zeiler, Rob Fergus, ECCV, 2014.

Image Captioning

• Explain Images with Multimodal Recurrent Neural Networks, Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan L. Yuille, arXiv:1410.1090
• [Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models, Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel, arXiv:1411.2539.[(http://arxiv.org/pdf/1411.2539)
• Long-term Recurrent Convolutional Networks for Visual Recognition and Description, Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, arXiv:1411.4389.
• Show and Tell: A Neural Image Caption Generator, Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan, arXiv:1411.4555.
• Deep Visual-Semantic Alignments for Generating Image Description, Andrej Karpathy, Li Fei-Fei, CVPR, 2015.
• Translating Videos to Natural Language Using Deep Recurrent Neural Networks, Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, NAACL-HLT, 2015.
• Learning a Recurrent Visual Representation for Image Caption Generation, Xinlei Chen, C. Lawrence Zitnick
• From Captions to Visual Concepts and Back, 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, CVPR, 2015.
• Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention, Kelvin Xu, Jimmy Lei Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio, arXiv:1502.03044 / ICML 2015
• Phrase-based Image Captioning, Remi Lebret, Pedro O. Pinheiro, Ronan Collobert, arXiv:1502.03671 / ICML 2015
• Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images, Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan L. Yuille, arXiv:1504.06692
• Exploring Nearest Neighbor Approaches for Image Captioning, Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, arXiv:1505.04467
• Language Models for Image Captioning: The Quirks and What Works, Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell, arXiv:1505.01809
• Image Captioning with an Intermediate Attributes Layer, Qi Wu, Chunhua Shen, Anton van den Hengel, Lingqiao Liu, Anthony Dick, arXiv:1506.01144
• Learning language through pictures, Grzegorz Chrupala, Akos Kadar, Afra Alishahi, arXiv:1506.03694
• Describing Multimedia Content using Attention-based Encoder-Decoder Networks, Kyunghyun Cho, Aaron Courville, Yoshua Bengio, arXiv:1507.01053
• Image Representations and New Domains in Neural Image Captioning, Jack Hessel, Nicolas Savva, Michael J. Wilber, arXiv:1508.02091
• Learning Query and Image Similarities with Ranking Canonical Correlation Analysis, Ting Yao, Tao Mei, and Chong-Wah Ngo, ICCV, 2015

Video Captioning

• 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.
• 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.
• Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui, Joint Modeling Embedding and Translation to Bridge Video and Language, arXiv:1505.01861.
• Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko, Sequence to Sequence–Video to Text, arXiv:1505.00487.
• Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville, Describing Videos by Exploiting Temporal Structure, arXiv:1502.08029
• Anna Rohrbach, Marcus Rohrbach, Bernt Schiele, The Long-Short Story of Movie Description, arXiv:1506.01698
• 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
• Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia Content using Attention-based Encoder-Decoder Networks, arXiv:1507.01053
• Dotan Kaufman, Gil Levi, Tal Hassner, Lior Wolf, Temporal Tessellation for Video Annotation and Summarization, arXiv:1612.06950.

Image Generation

• Conditional Image Generation with PixelCNN Decoders”, Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu
• Learning to Generate Chairs with Convolutional Neural Networks, Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, CVPR, 2015.
• DRAW: A Recurrent Neural Network For Image Generation, Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, ICML, 2015.

Generative Adversarial Networks

• Generative Adversarial Network, Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, NIPS, 2014.
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, NIPS, 2015.
• A note on the evaluation of generative models, Lucas Theis, Aäron van den Oord, Matthias Bethge, ICLR 2016.
• Variationally Auto-Encoded Deep Gaussian Processes, Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence, ICLR 2016.
• Generating Images from Captions with Attention, Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, ICLR 2016
• Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks, Jost Tobias Springenberg, ICLR 2016
• Censoring Representations with an Adversary, Harrison Edwards, Amos Storkey, ICLR 2016
• Distributional Smoothing with Virtual Adversarial Training, Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii, ICLR 2016
• Generative Visual Manipulation on the Natural Image Manifold, 朱俊彦, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros, ECCV 2016.
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Alec Radford, Luke Metz, Soumith Chintala, ICLR 2016

Question and Answer

• VQA: Visual Question Answering, CVPR, 2015 SUNw:Scene Understanding workshop. Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh
• Ask Your Neurons: A Neural-based Approach to Answering Questions about Images, Mateusz Malinowski, Marcus Rohrbach, Mario Fritz, arXiv:1505.01121.
• Image Question Answering: A Visual Semantic Embedding Model and a New Dataset, Mengye Ren, Ryan Kiros, Richard Zemel, arXiv:1505.02074 / ICML 2015 deep learning workshop.
• Hauyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, 徐伟, Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering, arXiv:1505.05612.
• Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction, Hyeonwoo Noh, Paul Hongsuck Seo, and Bohyung Han, arXiv:1511.05765
• Stacked Attention Networks for Image Question Answering, Yang, Z., He, X., Gao, J., Deng, L., & Smola, A. (2015), arXiv:1511.02274.
• Dynamic Memory Networks for Visual and Textual Question Answering, Xiong, Caiming, Stephen Merity, and Richard Socher, arXiv:1603.01417 (2016).
• Multimodal Residual Learning for Visual QA, Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang, arXiv:1606:01455
• Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding, Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, and Marcus Rohrbach, arXiv:1606.01847
• Training Recurrent Answering Units with Joint Loss Minimization for VQA, Hyeonwoo Noh and Bohyung Han, arXiv:1606.03647
• Hadamard Product for Low-rank Bilinear Pooling, Jin-Hwa Kim, Kyoung Woon On, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhan 发表：arXiv:1610.04325.

Visual Attention and Saliency

• Predicting Eye Fixations using Convolutional Neural Networks, Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu, CVPR, 2015.
• Learning a Sequential Search for Landmarks, Saurabh Singh, Derek Hoiem, David Forsyth, CVPR, 2015.
• Multiple Object Recognition with Visual Attention, Jimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu, , ICLR, 2015.
• Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu, Recurrent Models of Visual Attention, NIPS, 2014.

Super Resolution

• Iterative Image Reconstruction
  • Sven Behnke: Learning Iterative Image Reconstruction. IJCAI, 2001.
  • 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.
• Super-Resolution (SRCNN)
  • Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, ECCV, 2014.
• Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Image Super-Resolution Using Deep Convolutional Networks, arXiv:1501.00092.
• Very Deep Super-Resolution, Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, arXiv:1511.04587, 2015.
• Deeply-Recursive Convolutional Network, Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015.
• 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.
• 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.
• 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.

Other Applications

• Optical Flow (FlowNet), 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, Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang, Compression Artifacts Reduction by a Deep Convolutional Network, arXiv:1504.06993.
• Blur Removal
  • Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf, Learning to Deblur, arXiv:1406.7444
  • Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015
• Image Deconvolution, Li Xu, Jimmy SJ. Ren, Ce Liu, Jiaya Jia, Deep Convolutional Neural Network for Image Deconvolution, NIPS, 2014.
• Deep Edge-Aware Filter, 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, 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
• Feature Learning by Inpainting, Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros, Context Encoders: Feature Learning by Inpainting, CVPR, 2016

Contour/Edge Detection

• Holistically-Nested Edge Detection, Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection, arXiv:1504.06375.
• DeepEdge, Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.
• DeepContour, 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.

Semantic Image Segmentation

• SEC: Seed, Expand and Constrain, Alexander Kolesnikov, Christoph Lampert, Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, ECCV, 2016.
• Adelaide, Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv: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. (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 (2nd ranked in VOC 2012)
• CentraleSuperBoundaries, INRIA, Iasonas Kokkinos, Surpassing Humans in Boundary Detection using Deep Learning, arXiv:1411.07386 (4th ranked in VOC 2012)
• BoxSup, 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. (7th ranked in VOC2012)
  • Seunghoon Hong, Hyeonwoo Noh, Bohyung Han, Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation, arXiv:1506.04924.
  • Seunghoon Hong,Junhyuk Oh, Bohyung Han, and Honglak Lee, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, arXiv:1512.07928
• Conditional Random Fields as Recurrent Neural Networks , 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. (9th ranked in VOC2012)
• Zoom-out, Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich, Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015
• Joint Calibration, Holger Caesar, Jasper Uijlings, Vittorio Ferrari, Joint Calibration for Semantic Segmentation, arXiv:1507.01581.
• Fully Convolutional Networks for Semantic Segmentation , Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015.
• Hypercolumn, 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.
• Learning Hierarchical Features for Scene Labeling
  • 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
  • Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” arXiv preprint arXiv:1511.00561, 2015.
  • 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.
• Fisher Yu, Vladlen Koltun, “Multi-Scale Context Aggregation by Dilated Convolutions”, ICLR 2016
• Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi, “Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing”, ICCV, 2015
• Iasonas Kokkinos, “Pusing the Boundaries of Boundary Detection Using deep Learning”, ICLR 2016
• Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, “Weakly supervised graph based semantic segmentation by learning communities of image-parts”, ICCV, 2015

Course

• Stanford CS231n: Convolutional Neural Networks for Visual Recognition
• 香港中文大学 ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning)
• Stanford CS224d: Deep Learning for Natural Language Processing
• Oxford Deep Learning by Prof. Nando de Freitas
• NYU Deep Learning by Prof. Yann LeCun

Book

• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
• Neural Networks and Deep Learning by Michael Nielsen
• Deep Learning Tutorial by LISA lab, University of Montreal

Video

• Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
• Recent Developments in Deep Learning By Geoff Hinton
• The Unreasonable Effectiveness of Deep Learning by Yann LeCun
• Deep Learning of Representations by Yoshua bengio

Library

• Tensorflow: An open source software library for numerical computation using data flow graph by Google [Web]
• Torch7: Deep learning library in Lua, used by Facebook and Google Deepmind [Web]
• Torch-based deep learning libraries: [torchnet],
• Caffe: Deep learning framework by the BVLC [Web]
• Theano: Mathematical library in Python, maintained by LISA lab [Web]
• Theano-based deep learning libraries: [Pylearn2], [Blocks], [Keras], [Lasagne]
• MatConvNet: CNNs for MATLAB [Web]
• MXNet: A flexible and efficient deep learning library for heterogeneous distributed systems with multi-language support [Web]
• Deepgaze: A computer vision library for human-computer interaction based on CNNs [Web]

Application

• Code and hyperparameters for the paper “Generative Adversarial Networks” [Web]
  • Source code for “Understanding Deep Image Representations by Inverting Them,” CVPR, 2015. [Web]
  • Source code for the paper “Rich feature hierarchies for accurate object detection and semantic segmentation,” CVPR, 2014. [Web] ； Source code for the paper “Fully Convolutional Networks for Semantic Segmentation,” CVPR, 2015. [Web]
  • Image Super-Resolution for Anime-Style-Art [Web]
  • Source code for the paper “DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection,” CVPR, 2015. [Web]
  • Source code for the paper “Holistically-Nested Edge Detection”, ICCV 2015. [Web]

Tutorial

• Tutorial on Deep Learning in Computer Vision
• Applied Deep Learning for Computer Vision with Torch

Blog

• Deep down the rabbit hole: CVPR 2015 and beyond@Tombone’s Computer Vision Blog
• CVPR recap and where we’re going@Zoya Bylinskii (MIT PhD Student)’s Blog
• Facebook’s AI Painting@Wired
• Inceptionism: Going Deeper into Neural Networks@Google Research
• Implementing Neural networks

Written on August 22, 2017