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@@ -66,6 +66,10 @@
 <li><a href="#low-level-vision">Low-Level Vision</a></li>
 <li><a href="#edge-detection">Edge Detection</a></li>
 <li><a href="#semantic-segmentation">Semantic Segmentation</a></li>
+<li><a href="#visual-attention-and-saliency">Visual Attention and Saliency</a></li>
+<li><a href="#object-recognition">Object Recognition</a></li>
+<li><a href="#understanding-cnn">Understanding CNN</a></li>
+<li><a href="#other-topics">Other Topics</a></li>
 </ul>
 </li>
 <li><a href="#courses">Courses</a></li>
@@ -276,6 +280,115 @@ NIPS 2012.</li>
 </li>
 </ul>
 
+<h3>
+<a id="visual-attention-and-saliency" class="anchor" href="#visual-attention-and-saliency" aria-hidden="true"><span class="octicon octicon-link"></span></a>Visual Attention and Saliency</h3>
+
+<ul>
+<li>Mr-CNN <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Liu_Predicting_Eye_Fixations_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu, Predicting Eye Fixations using Convolutional Neural Networks, CVPR, 2015.</li>
+</ul>
+</li>
+<li>Learning a Sequential Search for Landmarks <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Singh_Learning_a_Sequential_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Saurabh Singh, Derek Hoiem, David Forsyth, Learning a Sequential Search for Landmarks, CVPR, 2015.</li>
+</ul>
+</li>
+<li>Multiple Object Recognition with Visual Attention <a href="http://arxiv.org/pdf/1412.7755v2.pdf">[Paper]</a>
+
+<ul>
+<li>Jimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu, Multiple Object Recognition with Visual Attention, ICLR, 2015.</li>
+</ul>
+</li>
+<li>Recurrent Models of Visual Attention<a href="http://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf">[Paper]</a>
+
+<ul>
+<li>Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu, Recurrent Models of Visual Attention, NIPS, 2014.</li>
+</ul>
+</li>
+</ul>
+
+<h3>
+<a id="object-recognition" class="anchor" href="#object-recognition" aria-hidden="true"><span class="octicon octicon-link"></span></a>Object Recognition</h3>
+
+<ul>
+<li>Weakly-supervised learning with convolutional neural networks <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Oquab_Is_Object_Localization_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic, Is object localization for free? – Weakly-supervised learning with convolutional neural networks, CVPR, 2015.</li>
+</ul>
+</li>
+<li>FV-CNN <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Cimpoi_Deep_Filter_Banks_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, Deep Filter Banks for Texture Recognition and Segmentation, CVPR, 2015.</li>
+</ul>
+</li>
+</ul>
+
+<h3>
+<a id="understanding-cnn" class="anchor" href="#understanding-cnn" aria-hidden="true"><span class="octicon octicon-link"></span></a>Understanding CNN</h3>
+
+<ul>
+<li>Equivariance and Equivalence of Representations <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lenc_Understanding_Image_Representations_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Karel Lenc, Andrea Vedaldi, Understanding image representations by measuring their equivariance and equivalence, CVPR, 2015.</li>
+</ul>
+</li>
+<li>Deep Neural Networks Are Easily Fooled <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Anh Nguyen, Jason Yosinski, Jeff Clune, Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images, CVPR, 2015.</li>
+</ul>
+</li>
+<li>Understanding Deep Image Representations by Inverting Them <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015.</li>
+</ul>
+</li>
+</ul>
+
+<h3>
+<a id="other-topics" class="anchor" href="#other-topics" aria-hidden="true"><span class="octicon octicon-link"></span></a>Other Topics</h3>
+
+<ul>
+<li>Surface Normal Estimation <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Designing_Deep_Networks_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Xiaolong Wang, David F. Fouhey, Abhinav Gupta, Designing Deep Networks for Surface Normal Estimation, CVPR, 2015.</li>
+</ul>
+</li>
+<li>Action Detection <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Gkioxari_Finding_Action_Tubes_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Georgia Gkioxari, Jitendra Malik, Finding Action Tubes, CVPR, 2015.</li>
+</ul>
+</li>
+<li>Crowd Counting <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhang_Cross-Scene_Crowd_Counting_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Cong Zhang, Hongsheng Li, Xiaogang Wang, Xiaokang Yang, Cross-scene Crowd Counting via Deep Convolutional Neural Networks, CVPR, 2015.</li>
+</ul>
+</li>
+<li>3D Shape Retrieval <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Sketch-Based_3D_Shape_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Fang Wang, Le Kang, Yi Li, Sketch-based 3D Shape Retrieval using Convolutional Neural Networks, CVPR, 2015.</li>
+</ul>
+</li>
+<li>Generate image <a href="http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf">[Paper]</a>
+
+<ul>
+<li>Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, Learning to Generate Chairs with Convolutional Neural Networks, CVPR, 2015.<br>
+</li>
+</ul>
+</li>
+</ul>
+
 <h2>
 <a id="courses" class="anchor" href="#courses" aria-hidden="true"><span class="octicon octicon-link"></span></a>Courses</h2>