面向机器视觉的深度学习资源汇总

jopen 9年前

Awesome Deep Vision

A curated list of deep learning resources for computer vision, inspired by awesome-php and awesome-computer-vision.

Maintainers - Jiwon Kim, Heesoo Myeong, Myungsub Choi, JanghoonChoi, Jung Kwon Lee

Contributing

Please feel free to pull requests or email jiwon@alum.mit.edu to add links.

Sharing

Table of Contents

  • Papers </li>
  • Courses
  • Books
  • Videos
  • Software
    • Framework
    • Applications
    • </ul> </li>
    • Tutorials
    • Blogs
    • </ul>

      Papers

      ImageNet Classification

      classification (from Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS, 2012.)

      • Microsoft (PReLu/Weight Initialization) [Paper]
        • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, arXiv:1502.01852.
        </li>
      • Batch Normalization [Paper]
        • Sergey Ioffe, Christian Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv:1502.03167.
        • </ul> </li>
        • GoogLeNet [Paper]
          • Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, CVPR, 2015.
          • </ul> </li>
          • VGG-Net [Web] [Paper]
            • Karen Simonyan and Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Visual Recognition, ICLR, 2015.
            • </ul> </li>
            • AlexNet [Paper]
              • Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS, 2012.
              • </ul> </li> </ul>

                Object Detection

                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.)

                • OverFeat, NYU [Paper]
                  • Matthrew Zeiler, Rob Fergus, Visualizing and Understanding Convolutional Networks, ECCV, 2014.
                  </li>
                • R-CNN, UC Berkeley [Paper-CVPR14] [Paper-arXiv14]
                  • Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR, 2014.
                  • </ul> </li>
                  • SPP, Microsoft Research [Paper]
                    • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, ECCV, 2014.
                    • </ul> </li>
                    • Fast R-CNN, Microsoft Research [Paper]
                      • Ross Girshick, Fast R-CNN, arXiv:1504.08083.
                      • </ul> </li>
                      • Faster R-CNN, Microsoft Research [Paper]
                        • Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497.
                        • </ul> </li>
                        • R-CNN minus R, Oxford [Paper]
                          • Karel Lenc, Andrea Vedaldi, R-CNN minus R, arXiv:1506.06981.
                          • </ul> </li> </ul>

                            Low-Level Vision

                            • Optical Flow (FlowNet) [Paper]
                              • 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>
                            • Super-Resolution (SRCNN) [Web] [Paper-ECCV14] [Paper-arXiv15][Paper ICONIP-2014]
                              • 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.
                              • Osendorfer, Christian, Hubert Soyer, and Patrick van der Smagt, Image Super-Resolution with Fast Approximate Convolutional Sparse Coding, ICONIP, 2014.
                              • </ul> </li>
                              • Compression Artifacts Reduction [Paper-arXiv15]
                                • Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang, Compression Artifacts Reduction by a Deep Convolutional Network, arXiv:1504.06993.
                                • </ul> </li>
                                • Non-Uniform Motion Blur Removal [Paper]
                                  • Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015.
                                  • </ul> </li>
                                  • Image Deconvolution [Web] [Paper]
                                    • Li Xu, Jimmy SJ. Ren, Ce Liu, Jiaya Jia, Deep Convolutional Neural Network for Image Deconvolution, NIPS, 2014.
                                    • </ul> </li>
                                    • Deep Edge-Aware Filter [Paper]
                                      • Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia, Deep Edge-Aware Filters, ICML, 2015.
                                      • </ul> </li>
                                      • Computing the Stereo Matching Cost with a Convolutional Neural Network [Paper]
                                        • Jure Žbontar, Yann LeCun, Computing the Stereo Matching Cost with a Convolutional Neural Network, CVPR, 2015.
                                        • </ul> </li> </ul>

                                          Edge Detection

                                          edge_detection (from Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.)

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

                                                Semantic Segmentation

                                                semantic_segmantation (from Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640.)

                                                • BoxSup [Paper]
                                                  • Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640.
                                                  </li>
                                                • Conditional Random Fields as Recurrent Neural Networks [Paper]
                                                  • 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.
                                                  • </ul> </li>
                                                  • Fully Convolutional Networks for Semantic Segmentation [Paper-CVPR15] [Paper-arXiv15]
                                                    • Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015.
                                                    • </ul> </li>
                                                    • Learning Hierarchical Features for Scene Labeling [Paper-ICML12] [Paper-PAMI13]
                                                      • 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.
                                                      • </ul> </li> </ul>

                                                        Visual Attention and Saliency

                                                        saliency (from Federico Perazzi, Philipp Krahenbuhl, Yael Pritch, Alexander Hornung, Saliency Filters: Contrast Based Filtering for Salient Region Detection, CVPR, 2012.)

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

                                                                Object Recognition

                                                                • Weakly-supervised learning with convolutional neural networks [Paper]
                                                                  • Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic, Is object localization for free? – Weakly-supervised learning with convolutional neural networks, CVPR, 2015.
                                                                  </li>
                                                                • FV-CNN [Paper]
                                                                  • Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, Deep Filter Banks for Texture Recognition and Segmentation, CVPR, 2015.
                                                                  • </ul> </li> </ul>

                                                                    Understanding CNN

                                                                    understanding (from Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015.)

                                                                    • Equivariance and Equivalence of Representations [Paper]
                                                                      • Karel Lenc, Andrea Vedaldi, Understanding image representations by measuring their equivariance and equivalence, CVPR, 2015.
                                                                      </li>
                                                                    • Deep Neural Networks Are Easily Fooled [Paper]
                                                                      • Anh Nguyen, Jason Yosinski, Jeff Clune, Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images, CVPR, 2015.
                                                                      • </ul> </li>
                                                                      • Understanding Deep Image Representations by Inverting Them [Paper]
                                                                        • Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015.
                                                                        • </ul> </li> </ul>

                                                                          Image Captioning

                                                                          image_captioning (from Andrej Karpathy, Li Fei-Fei, Deep Visual-Semantic Alignments for Generating Image Description, CVPR, 2015.)