awesome-deep-vision

Computer Vision Resources

A curated list of deep learning resources and papers for computer vision

A curated list of deep learning resources for computer vision

GitHub
11k stars
1k watching
3k forks
last commit: over 1 year ago
Linked from 11 awesome lists


Awesome Deep Vision / Sharing
http://twitter.com/home?status=http://jiwonkim.org/awesome-deep-vision%0ADeep [Share on Twitter]( Learning Resources for Computer Vision)
Share on Facebook
Share on Google Plus
Share on LinkedIn

Awesome Deep Vision / Papers / ImageNet Classification
Paper Microsoft (Deep Residual Learning) [ ][ ]
[Paper] Microsoft (PReLu/Weight Initialization)
[Paper] Batch Normalization
[Paper] GoogLeNet
[Web] VGG-Net
[Paper] AlexNet

Awesome Deep Vision / Papers / Object Detection
[Paper] PVANET
[Paper] OverFeat, NYU
[Paper-CVPR14] R-CNN, UC Berkeley
[Paper] SPP, Microsoft Research
[Paper] Fast R-CNN, Microsoft Research
[Paper] Faster R-CNN, Microsoft Research
[Paper] R-CNN minus R, Oxford
[Paper] End-to-end people detection in crowded scenes
[Paper] You Only Look Once: Unified, Real-Time Object Detection , , ,
[Paper] Inside-Outside Net
[Paper] Deep Residual Network (Current State-of-the-Art)
Paper Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning [ ]
[Paper] R-FCN
[Paper] SSD
[Paper] Speed/accuracy trade-offs for modern convolutional object detectors

Awesome Deep Vision / Papers / Video Classification
Paper Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, "Delving Deeper into Convolutional Networks for Learning Video Representations", ICLR 2016. [ ]
Paper Michael Mathieu, camille couprie, Yann Lecun, "Deep Multi Scale Video Prediction Beyond Mean Square Error", ICLR 2016. [ ]

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

Awesome Deep Vision / Papers / Low-Level Vision / Iterative Image Reconstruction
[Paper] Sven Behnke: Learning Iterative Image Reconstruction. IJCAI, 2001
[Paper] 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

Awesome Deep Vision / Papers / Low-Level Vision
[Web] Super-Resolution (SRCNN)

Awesome Deep Vision / Papers / Low-Level Vision / Very Deep Super-Resolution
[Paper] Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, arXiv:1511.04587, 2015

Awesome Deep Vision / Papers / Low-Level Vision / Deeply-Recursive Convolutional Network
[Paper] Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015

Awesome Deep Vision / Papers / Low-Level Vision / Casade-Sparse-Coding-Network
[Paper] Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015

Awesome Deep Vision / Papers / Low-Level Vision / Perceptual Losses for Super-Resolution
[Paper] Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155, 2016

Awesome Deep Vision / Papers / Low-Level Vision / SRGAN
[Paper] 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

Awesome Deep Vision / Papers / Low-Level Vision / Others
[Paper ICONIP-2014] Osendorfer, Christian, Hubert Soyer, and Patrick van der Smagt, Image Super-Resolution with Fast Approximate Convolutional Sparse Coding, ICONIP, 2014

Awesome Deep Vision / Papers / Low-Level Vision
[Paper] Optical Flow (FlowNet)
[Paper-arXiv15] Compression Artifacts Reduction

Awesome Deep Vision / Papers / Low-Level Vision / Blur Removal
[Paper] Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf, Learning to Deblur, arXiv:1406.7444
[Paper] Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015

Awesome Deep Vision / Papers / Low-Level Vision
[Web] Image Deconvolution
[Paper] Deep Edge-Aware Filter
[Paper] Computing the Stereo Matching Cost with a Convolutional Neural Network
[Paper] Colorful Image Colorization Richard Zhang, Phillip Isola, Alexei A. Efros, ECCV, 2016 ,
[Blog] Ryan Dahl,
[Paper] Feature Learning by Inpainting

Awesome Deep Vision / Papers / Edge Detection
[Paper] Holistically-Nested Edge Detection
[Paper] DeepEdge
[Paper] DeepContour

Awesome Deep Vision / Papers / Semantic Segmentation
leaderboards PASCAL VOC2012 Challenge Leaderboard (01 Sep. 2016) (from PASCAL VOC2012 )

Awesome Deep Vision / Papers / Semantic Segmentation / SEC: Seed, Expand and Constrain
[Paper] Alexander Kolesnikov, Christoph Lampert, Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, ECCV, 2016

Awesome Deep Vision / Papers / Semantic Segmentation / Adelaide
[Paper] 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)
[Paper] 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)

Awesome Deep Vision / Papers / Semantic Segmentation / Deep Parsing Network (DPN)
[Paper] 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)

Awesome Deep Vision / Papers / Semantic Segmentation
[Paper] CentraleSuperBoundaries, INRIA
[Paper] BoxSup

Awesome Deep Vision / Papers / Semantic Segmentation / POSTECH
[Paper] Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, Learning Deconvolution Network for Semantic Segmentation, arXiv:1505.04366. (7th ranked in VOC2012)
[Paper] Seunghoon Hong, Hyeonwoo Noh, Bohyung Han, Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation, arXiv:1506.04924
Paper Seunghoon Hong,Junhyuk Oh, Bohyung Han, and Honglak Lee, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, arXiv:1512.07928 [ ] [ ]

Awesome Deep Vision / Papers / Semantic Segmentation
[Paper] Conditional Random Fields as Recurrent Neural Networks

Awesome Deep Vision / Papers / Semantic Segmentation / DeepLab
[Paper] 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)

Awesome Deep Vision / Papers / Semantic Segmentation
[Paper] Zoom-out
[Paper] Joint Calibration
[Paper-CVPR15] Fully Convolutional Networks for Semantic Segmentation
[Paper] Hypercolumn

Awesome Deep Vision / Papers / Semantic Segmentation / Deep Hierarchical Parsing
[Paper] Abhishek Sharma, Oncel Tuzel, David W. Jacobs, Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015

Awesome Deep Vision / Papers / Semantic Segmentation
[Paper-ICML12] Learning Hierarchical Features for Scene Labeling
[Web] University of Cambridge

Awesome Deep Vision / Papers / Semantic Segmentation / [Web]
[Paper] Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." arXiv preprint arXiv:1511.00561, 2015

Awesome Deep Vision / Papers / Semantic Segmentation
[Paper] 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

Awesome Deep Vision / Papers / Semantic Segmentation / Princeton
Paper Fisher Yu, Vladlen Koltun, "Multi-Scale Context Aggregation by Dilated Convolutions", ICLR 2016, [ ]

Awesome Deep Vision / Papers / Semantic Segmentation / Univ. of Washington, Allen AI
Paper Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi, "Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing", ICCV, 2015, [ ]

Awesome Deep Vision / Papers / Semantic Segmentation / INRIA
Paper Iasonas Kokkinos, "Pusing the Boundaries of Boundary Detection Using deep Learning", ICLR 2016, [ ]

Awesome Deep Vision / Papers / Semantic Segmentation / UCSB
Paper Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, "Weakly supervised graph based semantic segmentation by learning communities of image-parts", ICCV, 2015, [ ]

Awesome Deep Vision / Papers / Visual Attention and Saliency
[Paper] Mr-CNN
[Paper] Learning a Sequential Search for Landmarks
[Paper] Multiple Object Recognition with Visual Attention
[Paper] Recurrent Models of Visual Attention

Awesome Deep Vision / Papers / Object Recognition
[Paper] Weakly-supervised learning with convolutional neural networks
[Paper] FV-CNN

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

Awesome Deep Vision / Papers / Image and Language
[Paper] UCLA / Baidu
[Paper] Toronto
[Paper] Berkeley
[Paper] Google
[Web] Stanford
[Paper] UML / UT
[Paper-arXiv] CMU / Microsoft
[Paper] Microsoft
Web Univ. Montreal / Univ. Toronto [ ] [ ]
Paper Idiap / EPFL / Facebook [ ]
Paper UCLA / Baidu [ ]

Awesome Deep Vision / Papers / Image and Language / MS + Berkeley
Paper Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, Exploring Nearest Neighbor Approaches for Image Captioning, arXiv:1505.04467 [ ]
Paper 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 [ ]

Awesome Deep Vision / Papers / Image and Language
Paper Adelaide [ ]
Paper Tilburg [ ]
Paper Univ. Montreal [ ]
Paper Cornell [ ]
Paper MS + City Univ. of HongKong [ ]
[Web] Berkeley
[Paper] UT / UML / Berkeley
[Paper] Microsoft
[Paper] UT / Berkeley / UML
Paper Univ. Montreal / Univ. Sherbrooke [ ]
Paper MPI / Berkeley [ ]
Paper Univ. Toronto / MIT [ ]
Paper Univ. Montreal [ ]
paper TAU / USC [ ]
[Web] Virginia Tech / MSR
[Web] MPI / Berkeley
[Paper] Toronto
[Paper] Baidu / UCLA
Paper POSTECH [ ] [ ]
Paper CMU / Microsoft Research [ ]
Paper MetaMind [ ]
Paper SNU + NAVER [ ]
Paper UC Berkeley + Sony [ ]
Paper Postech [ ]
Paper SNU + NAVER [ ]

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

Awesome Deep Vision / Papers / Image Generation / Adversarial Networks
[Paper] 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] Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, NIPS, 2015
Paper Lucas Theis, Aäron van den Oord, Matthias Bethge, "A note on the evaluation of generative models", ICLR 2016. [ ]
Paper Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence, "Variationally Auto-Encoded Deep Gaussian Processes", ICLR 2016. [ ]
Paper Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, "Generating Images from Captions with Attention", ICLR 2016, [ ]
Paper Jost Tobias Springenberg, "Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks", ICLR 2016, [ ]
Paper Harrison Edwards, Amos Storkey, "Censoring Representations with an Adversary", ICLR 2016, [ ]
Paper Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii, "Distributional Smoothing with Virtual Adversarial Training", ICLR 2016, [ ]
Paper Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros, "Generative Visual Manipulation on the Natural Image Manifold", ECCV 2016. [ ] [ ] [ ]

Awesome Deep Vision / Papers / Image Generation / Mixing Convolutional and Adversarial Networks
Paper Alec Radford, Luke Metz, Soumith Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR 2016. [ ]

Awesome Deep Vision / Papers / Other Topics
Paper Visual Analogy [ ]
[Paper] Surface Normal Estimation
[Paper] Action Detection
[Paper] Crowd Counting
[Paper] 3D Shape Retrieval

Awesome Deep Vision / Papers / Other Topics / Weakly-supervised Classification
Paper Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell, "Auxiliary Image Regularization for Deep CNNs with Noisy Labels", ICLR 2016, [ ]

Awesome Deep Vision / Papers / Other Topics
[Paper] Artistic Style

Awesome Deep Vision / Papers / Other Topics / Human Gaze Estimation
[Paper] Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling, Appearance-Based Gaze Estimation in the Wild, CVPR, 2015

Awesome Deep Vision / Papers / Other Topics / Face Recognition
[Paper] Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR, 2014
[Paper] Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang, DeepID3: Face Recognition with Very Deep Neural Networks, 2015
[Paper] Florian Schroff, Dmitry Kalenichenko, James Philbin, FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR, 2015

Awesome Deep Vision / Papers / Other Topics / Facial Landmark Detection
[Paper] Yue Wu, Tal Hassner, KangGeon Kim, Gerard Medioni, Prem Natarajan, Facial Landmark Detection with Tweaked Convolutional Neural Networks, 2015

Awesome Deep Vision / Courses / Deep Vision
CS231n: Convolutional Neural Networks for Visual Recognition [Stanford]
ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning) [CUHK]

Awesome Deep Vision / Courses / More Deep Learning
CS224d: Deep Learning for Natural Language Processing [Stanford]
Deep Learning by Prof. Nando de Freitas [Oxford]
Deep Learning by Prof. Yann LeCun [NYU]

Awesome Deep Vision / Books / Free Online Books
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

Awesome Deep Vision / Videos / Talks
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

Awesome Deep Vision / Software / Framework
Web 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 [ ]

Awesome Deep Vision / Software / Framework / Web
torchnet 998 over 5 years ago Torch-based deep learning libraries: [ ],

Awesome Deep Vision / Software / Framework
Web Caffe: Deep learning framework by the BVLC [ ]
Web Theano: Mathematical library in Python, maintained by LISA lab [ ]

Awesome Deep Vision / Software / Framework / Web
Pylearn2 Theano-based deep learning libraries: [ ], [ ], [ ], [ ]

Awesome Deep Vision / Software / Framework
Web MatConvNet: CNNs for MATLAB [ ]
Web MXNet: A flexible and efficient deep learning library for heterogeneous distributed systems with multi-language support [ ]
Web 1,796 7 months ago Deepgaze: A computer vision library for human-computer interaction based on CNNs [ ]

Awesome Deep Vision / Software / Applications / Adversarial Training
[Web] 3,898 over 4 years ago Code and hyperparameters for the paper "Generative Adversarial Networks"

Awesome Deep Vision / Software / Applications / Understanding and Visualizing
[Web] 168 about 7 years ago Source code for "Understanding Deep Image Representations by Inverting Them," CVPR, 2015

Awesome Deep Vision / Software / Applications / Semantic Segmentation
[Web] 2,380 over 7 years ago Source code for the paper "Rich feature hierarchies for accurate object detection and semantic segmentation," CVPR, 2014
[Web] 81 about 7 years ago Source code for the paper "Fully Convolutional Networks for Semantic Segmentation," CVPR, 2015

Awesome Deep Vision / Software / Applications / Super-Resolution
[Web] 27,605 over 1 year ago Image Super-Resolution for Anime-Style-Art

Awesome Deep Vision / Software / Applications / Edge Detection
[Web] 94 over 2 years ago Source code for the paper "DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection," CVPR, 2015
[Web] 1,803 8 months ago Source code for the paper "Holistically-Nested Edge Detection", ICCV 2015

Awesome Deep Vision / Tutorials
Tutorial on Deep Learning in Computer Vision [CVPR 2014]
Applied Deep Learning for Computer Vision with Torch 869 almost 8 years ago [CVPR 2015]

Awesome Deep Vision / Blogs
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

Backlinks from these awesome lists:

More related projects: