update some CVPR 2015 papers

update some CVPR 2015 papers

README.md

@@ -19,6 +19,10 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
   - [Low-Level Vision](#low-level-vision)
   - [Edge Detection](#edge-detection)
   - [Semantic Segmentation](#semantic-segmentation)
+  - [Visual Attention and Saliency](#visual-attention-and-saliency)
+  - [Object Recognition](#object-recognition)
+  - [Understanding CNN](#understanding-cnn)
+  - [Other Topics](#other-topics)
  - [Courses](#courses)
  - [Books](#books)
  - [Videos](#videos)
@@ -96,6 +100,42 @@ NIPS 2012.
   * BoxSup [[Paper]](http://arxiv.org/pdf/1503.01640v2)
    * Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640
 
+### Visual Attention and Saliency
+  * Mr-CNN [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Liu_Predicting_Eye_Fixations_2015_CVPR_paper.pdf)
+   * Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu, Predicting Eye Fixations using Convolutional Neural Networks, CVPR, 2015.
+  * Learning a Sequential Search for Landmarks [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Singh_Learning_a_Sequential_2015_CVPR_paper.pdf)
+   * Saurabh Singh, Derek Hoiem, David Forsyth, Learning a Sequential Search for Landmarks, CVPR, 2015.
+  * Multiple Object Recognition with Visual Attention [[Paper]](http://arxiv.org/pdf/1412.7755v2.pdf)
+   * Jimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu, Multiple Object Recognition with Visual Attention, ICLR, 2015.
+  * Recurrent Models of Visual Attention[[Paper]](http://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf)
+   * Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu, Recurrent Models of Visual Attention, NIPS, 2014.
+   
+### Object Recognition
+  * Weakly-supervised learning with convolutional neural networks [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Oquab_Is_Object_Localization_2015_CVPR_paper.pdf)
+   * Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic, Is object localization for free? – Weakly-supervised learning with convolutional neural networks, CVPR, 2015.
+  * FV-CNN [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Cimpoi_Deep_Filter_Banks_2015_CVPR_paper.pdf)
+   * Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, Deep Filter Banks for Texture Recognition and Segmentation, CVPR, 2015.
+   
+### Understanding CNN
+  * equivariance and equivalence of representations [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lenc_Understanding_Image_Representations_2015_CVPR_paper.pdf)
+   * Karel Lenc, Andrea Vedaldi, Understanding image representations by measuring their equivariance and equivalence, CVPR, 2015.
+  * Deep Neural Networks Are Easily Fooled [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf)
+   * Anh Nguyen, Jason Yosinski, Jeff Clune, Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images, CVPR, 2015.
+  * Understanding Deep Image Representations by Inverting Them [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf)
+   * Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015.
+   
+### Other Topics
+  * Surface Normal Estimation [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Designing_Deep_Networks_2015_CVPR_paper.pdf)
+   * Xiaolong Wang, David F. Fouhey, Abhinav Gupta, Designing Deep Networks for Surface Normal Estimation, CVPR, 2015.
+  * Action Detection [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Gkioxari_Finding_Action_Tubes_2015_CVPR_paper.pdf)
+   * Georgia Gkioxari, Jitendra Malik, Finding Action Tubes, CVPR, 2015.
+  * Crowd Counting [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhang_Cross-Scene_Crowd_Counting_2015_CVPR_paper.pdf)
+   * Cong Zhang, Hongsheng Li, Xiaogang Wang, Xiaokang Yang, Cross-scene Crowd Counting via Deep Convolutional Neural Networks, CVPR, 2015.
+  * 3D Shape Retrieval [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Sketch-Based_3D_Shape_2015_CVPR_paper.pdf)
+   * Fang Wang, Le Kang, Yi Li, Sketch-based 3D Shape Retrieval using Convolutional Neural Networks, CVPR, 2015.
+  * Generate image [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf)
+   * Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, Learning to Generate Chairs with Convolutional Neural Networks, CVPR, 2015.  
+
 ## Courses
  * Deep Vision
   * [Stanford] [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/)