# Awesome Deep Vision
A curated list of deep learning resources for computer vision, inspired by [awesome-php](https://github.com/ziadoz/awesome-php) and [awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision).

## Contributing
Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-vision/pulls) or email jiwon@alum.mit.edu to add links.

## Papers

### Image Restoration
#### Super-Resolution
 * SRCNN [[Web]](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html) [[Paper-ECCV14]](http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2014_deepresolution.pdf) [[Paper-arXiv15]](http://arxiv.org/pdf/1501.00092v1.pdf)
    * Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, in ECCV 2014
    * Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Image Super-Resolution Using Deep Convolutional Networks, arXiv:1501.00092 (2015)

#### Compression Artifacts Reduction
  * Compression Artifacts Reduction by a Deep Convolutional Network [[Paper-arXiv15]](http://arxiv.org/pdf/1504.06993v1)
    * Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang, Compression Artifacts Reduction by a Deep Convolutional Network, arXiv:1504.06993

### Image Captioning 
   * Baidu/UCLA: Explain Images with Multimodal Recurrent Neural Networks(http://arxiv.org/abs/1410.1090)
   * Toronto: Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models(http://arxiv.org/abs/1411.2539)
   * Berkeley: Long-term Recurrent Convolutional Networks for Visual Recognition and Description(http://arxiv.org/abs/1411.4389)
   * Google: Show and Tell: A Neural Image Caption Generator(http://arxiv.org/abs/1411.4555)
   * Stanford: Deep Visual-Semantic Alignments for Generating Image  Description(http://cs.stanford.edu/people/karpathy/deepimagesent/)
   * UML/UT:  Translating Videos to Natural Language Using Deep Recurrent Neural Networks(http://arxiv.org/abs/1412.4729)
   * Microsoft/CMU:  Learning a Recurrent Visual Representation for Image Caption Generation(http://arxiv.org/abs/1411.5654)
   * Microsoft:  From Captions to Visual Concepts and Back(http://arxiv.org/abs/1411.4952)