A curated list of deep learning resources for computer vision, inspired by awesome-computer-vision and awesome-deep-vision. Many thanks to Jiwon Kim' great work!
Please feel free to pull requests to add papers.
- Matching Networks for One Shot Learning [Paper]
- Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra
- Active Convolution: Learning the Shape of Convolution for Image Classification [Paper]
- Yunho Jeon, Junmo Kim, CVPR, 2017
- Aggregated Residual Transformations for Deep Neural Networks [Paper] [Code]
- Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He
- Convolutional Neural Fabrics [Paper] [Code]
- Shreyas Saxena, Jakob Verbeek
- Locally Scale-Invariant Convolutional Neural Networks [Paper] [Code]
- Angjoo Kanazawa, Abhishek Sharma, David Jacobs
- Mask R-CNN [Paper]
- Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick, Mask R-CNN, CVPR, 2017
- Deformable Convolutional Networks [Paper] [Code]
- Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, Yichen Wei, Arxiv tech report, 2017
- A-Fast-RCNN: Hard positive generation via adversary for object detection [Paper] [Code]
- Xiaolong Wang, Abhinav Shrivastava, Abhinav Gupta, CVPR, 2017
- Spatial Memory for Context Reasoning in Object Detection [Paper]
- Learning Features by Watching Objects Move [Paper] [Code]
- Deepak Pathak, Ross Girshick, Piotr Dollár, Trevor Darrell, Bharath Hariharan
- Improving Object Detection With One Line of Code [Paper] [Code]
- Navaneeth Bodla, Bharat Singh, Rama Chellappa, Larry S. Davis
- Deep Feature Flow for Video Recognition [Paper] [Code]
- Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, Yichen Wei
- Fully Convolutional Instance-aware Semantic Segmentation [Paper] [Code]
- Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, Yichen Wei, CVPR, 2017 (Spotlight)
- PixelNet: Representation of the pixels, by the pixels, and for the pixels [Paper] [Code]
- Aayush Bansal, Xinlei Chen, Bryan Russell, Abhinav Gupta, Deva Ramanan
- Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform [Paper]
- Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, Alan L. Yuille
- Discovering objects and their relations from entangled scene representations [Paper]
- David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, Peter Battaglia, ICLR, 2017
- Learning Spatiotemporal Features with 3D Convolutional Networks [Paper] [Code]
- Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri
- Structured Receptive Fields in CNNs [Paper]
- Jörn-Henrik Jacobsen, Jan van Gemert, Zhongyu Lou, Arnold W. M. Smeulders, CVPR, 2016
- Understanding image representations by measuring their equivariance and equivalence [Paper]
- Karel Lenc, Andrea Vedaldi, CVPR, 2015
- Dynamic Filter Networks [Paper]
- Bert De Brabandere, Xu Jia, Tinne Tuytelaars, Luc Van Gool, NIPS, 2016
- Network Dissection: Quantifying Interpretability of Deep Visual Representations [Paper]
- David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, Antonio Torralba, CVPR, 2017 (Oral)
- On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima [Paper] [Code]
- Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang, ICLR, 2017
- Auto-Encoding Variational Bayes [Paper]
- Diederik P Kingma, Max Welling
- Intriguing properties of neural networks [Paper]
- Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus
- Convolutional Sequence to Sequence Learning [Paper]
- Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin
- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks [Paper] [Code]
- Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros
- Adversarial Feature Learning [Paper] [Code]
- Jeff Donahue, Philipp Krähenbühl, Trevor Darrell
- Playing Atari with Deep Reinforcement Learning [Paper]
- Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
- Inferring and Executing Programs for Visual Reasoning [Paper] [Code]
- Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick
- Newly published papers (< 6 months) which are worth reading
- Recent Submissions: Computer Vision and Pattern Recognition
- DeepMind
- DeepMind's publications
- Free Online Books
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Neural Networks and Deep Learning by Michael Nielsen
- Deep Natural Language Processing by Hilary Term 2017 at the University of Oxford