awesome-deep-vision

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Microsoft (Deep Residual Learning) [ ][ ]

Microsoft (PReLu/Weight Initialization)

Batch Normalization

GoogLeNet

VGG-Net

AlexNet

PVANET

OverFeat, NYU

R-CNN, UC Berkeley

SPP, Microsoft Research

Fast R-CNN, Microsoft Research

Faster R-CNN, Microsoft Research

R-CNN minus R, Oxford

End-to-end people detection in crowded scenes

You Only Look Once: Unified, Real-Time Object Detection , , ,

Inside-Outside Net

Deep Residual Network (Current State-of-the-Art)

Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning [ ]

R-FCN

SSD

Speed/accuracy trade-offs for modern convolutional object detectors

Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, "Delving Deeper into Convolutional Networks for Learning Video Representations", ICLR 2016. [ ]

Michael Mathieu, camille couprie, Yann Lecun, "Deep Multi Scale Video Prediction Beyond Mean Square Error", ICLR 2016. [ ]

Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han, Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network, arXiv:1502.06796

Hanxi Li, Yi Li and Fatih Porikli, DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, BMVC, 2014

N Wang, DY Yeung, Learning a Deep Compact Image Representation for Visual Tracking, NIPS, 2013

Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, Hierarchical Convolutional Features for Visual Tracking, ICCV 2015 [ ] [ ]

Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, Visual Tracking with fully Convolutional Networks, ICCV 2015 [ ] [ ]

Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, [ ] [ ] [ ]

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)

Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, arXiv:1511.04587, 2015

Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015

Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015

Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155, 2016

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

Osendorfer, Christian, Hubert Soyer, and Patrick van der Smagt, Image Super-Resolution with Fast Approximate Convolutional Sparse Coding, ICONIP, 2014

Optical Flow (FlowNet)

Compression Artifacts Reduction

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

Deep Edge-Aware Filter

Computing the Stereo Matching Cost with a Convolutional Neural Network

Colorful Image Colorization Richard Zhang, Phillip Isola, Alexei A. Efros, ECCV, 2016 ,

Ryan Dahl,

Feature Learning by Inpainting

Holistically-Nested Edge Detection

DeepEdge

DeepContour

PASCAL VOC2012 Challenge Leaderboard (01 Sep. 2016) (from PASCAL VOC2012 )

Alexander Kolesnikov, Christoph Lampert, Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, ECCV, 2016

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)

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

BoxSup

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

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

Joint Calibration

Fully Convolutional Networks for Semantic Segmentation

Hypercolumn

Abhishek Sharma, Oncel Tuzel, David W. Jacobs, Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015

Learning Hierarchical Features for Scene Labeling

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, [ ]

Mr-CNN

Learning a Sequential Search for Landmarks

Multiple Object Recognition with Visual Attention

Recurrent Models of Visual Attention

Weakly-supervised learning with convolutional neural networks

FV-CNN

Karel Lenc, Andrea Vedaldi, Understanding image representations by measuring their equivariance and equivalence, CVPR, 2015

Anh Nguyen, Jason Yosinski, Jeff Clune, Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images, CVPR, 2015

Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015

Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, Object Detectors Emerge in Deep Scene CNNs, ICLR, 2015

Alexey Dosovitskiy, Thomas Brox, Inverting Visual Representations with Convolutional Networks, arXiv, 2015

Matthrew Zeiler, Rob Fergus, Visualizing and Understanding Convolutional Networks, ECCV, 2014

UCLA / Baidu

Toronto

Berkeley

Google

Stanford

UML / UT

CMU / Microsoft

Microsoft

Univ. Montreal / Univ. Toronto [ ] [ ]

Idiap / EPFL / Facebook [ ]

UCLA / Baidu [ ]

Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, Exploring Nearest Neighbor Approaches for Image Captioning, arXiv:1505.04467 [ ]

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 [ ]

Adelaide [ ]

Tilburg [ ]

Univ. Montreal [ ]

Cornell [ ]

MS + City Univ. of HongKong [ ]

Berkeley

UT / UML / Berkeley

Microsoft

UT / Berkeley / UML

Univ. Montreal / Univ. Sherbrooke [ ]

MPI / Berkeley [ ]

Univ. Toronto / MIT [ ]

Univ. Montreal [ ]

TAU / USC [ ]

Virginia Tech / MSR

MPI / Berkeley

Toronto

Baidu / UCLA

POSTECH [ ] [ ]

CMU / Microsoft Research [ ]

MetaMind [ ]

SNU + NAVER [ ]

UC Berkeley + Sony [ ]

Postech [ ]

SNU + NAVER [ ]

Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu. "Conditional Image Generation with PixelCNN Decoders"

Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, "Learning to Generate Chairs with Convolutional Neural Networks", CVPR, 2015

Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, "DRAW: A Recurrent Neural Network For Image Generation", ICML, 2015. [ ]

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Generative Adversarial Networks, NIPS, 2014

Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, NIPS, 2015

Lucas Theis, Aäron van den Oord, Matthias Bethge, "A note on the evaluation of generative models", ICLR 2016. [ ]

Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence, "Variationally Auto-Encoded Deep Gaussian Processes", ICLR 2016. [ ]

Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, "Generating Images from Captions with Attention", ICLR 2016, [ ]

Jost Tobias Springenberg, "Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks", ICLR 2016, [ ]

Harrison Edwards, Amos Storkey, "Censoring Representations with an Adversary", ICLR 2016, [ ]

Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii, "Distributional Smoothing with Virtual Adversarial Training", ICLR 2016, [ ]

Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros, "Generative Visual Manipulation on the Natural Image Manifold", ECCV 2016. [ ] [ ] [ ]

Alec Radford, Luke Metz, Soumith Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR 2016. [ ]

Visual Analogy [ ]

Surface Normal Estimation

Action Detection

Crowd Counting

3D Shape Retrieval

Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell, "Auxiliary Image Regularization for Deep CNNs with Noisy Labels", ICLR 2016, [ ]

Artistic Style

Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling, Appearance-Based Gaze Estimation in the Wild, CVPR, 2015

Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR, 2014

Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang, DeepID3: Face Recognition with Very Deep Neural Networks, 2015

Florian Schroff, Dmitry Kalenichenko, James Philbin, FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR, 2015

Yue Wu, Tal Hassner, KangGeon Kim, Gerard Medioni, Prem Natarajan, Facial Landmark Detection with Tweaked Convolutional Neural Networks, 2015

[Stanford]

[CUHK]

[Stanford]

[Oxford]

[NYU]

Tensorflow: An open source software library for numerical computation using data flow graph by Google [ ]

Torch7: Deep learning library in Lua, used by Facebook and Google Deepmind [ ]

torchnet

Torch-based deep learning libraries: [ ],

Caffe: Deep learning framework by the BVLC [ ]

Theano: Mathematical library in Python, maintained by LISA lab [ ]

Theano-based deep learning libraries: [ ], [ ], [ ], [ ]

MatConvNet: CNNs for MATLAB [ ]

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]

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

[CVPR 2014]

Applied Deep Learning for Computer Vision with Torch

[CVPR 2015]