国内外从事计算机视觉和图像处理相关领域的著名学者都以在三大顶级会议（ICCV。CVPR和ECCV）上发表论文为荣，其影响力远胜于一般SCI期刊论文。这三大顶级学术会议论文也引领着未来的研究趋势。CVPR是基本的计算机视觉会议。能够把它看作是计算机视觉研究的奥林匹克。
博主今天先来整理CVPR2015年的精彩文章（这个就够非常长一段时间消化的了）
顶级会议CVPR2015參会paper网址：
http://www.cv-foundation.org/openaccess/CVPR2015.py
来吧，一项项的開始整理。总有你须要的文章在等你！
CNN Architectures
CNN网络结构：
1.Hypercolumns for Object Segmentation and Fine-Grained Localization
Authors: Bharath Hariharan, Pablo Arbeláez, Ross Girshick, Jitendra Malik
2.Modeling Local and Global Deformations in Deep Learning: Epitomic Convolution, Multiple Instance Learning, and Sliding Window Detection
Authors: George Papandreou, Iasonas Kokkinos, Pierre-André Savalle
3.Going Deeper With Convolutions
Authors: Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
这篇文章推荐一下。使用了《network in network》中的用 global averaging pooling layer 替代 fully-connected layer的思想。有看过的能够私信博主，一起讨论文章心得。
4.Improving Object Detection With Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
Authors: Yuting Zhang, Kihyuk Sohn, Ruben Villegas, Gang Pan, Honglak Lee
5.Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Authors: Anh Nguyen, Jason Yosinski, Jeff Clune
Action and Event Recognition
1.Deeply Learned Attributes for Crowded Scene Understanding
Authors: Jing Shao, Kai Kang, Chen Change Loy, Xiaogang Wang
2.Modeling Video Evolution for Action Recognition
Authors: Basura Fernando, Efstratios Gavves, José Oramas M., Amir Ghodrati, Tinne Tuytelaars
3.Joint Inference of Groups, Events and Human Roles in Aerial Videos
Authors: Tianmin Shu, Dan Xie, Brandon Rothrock, Sinisa Todorovic, Song Chun Zhu
Segmentation in Images and Video
1.Causal Video Object Segmentation From Persistence of Occlusions
Authors: Brian Taylor, Vasiliy Karasev, Stefano Soatto
2.Fully Convolutional Networks for Semantic Segmentation
Authors: Jonathan Long, Evan Shelhamer, Trevor Darrell
——文章把全连接层当做卷积层，也用来输出featuremap。
这样相比Hypercolumns/HED 这种模型，可迁移的模型层数（指VGG16/Alexnet等）就很多其他了。可是从文章来看，由于纯卷积嘛，所以featuremap的每个点之间没有位置信息的区分。相较于Hypercolumns的claim。鼻子的点出如今图像的上半部分能够划分为pedestrian类的像素，可是假设出如今下方就应该划分为背景。所以位置信息应该是挺重要须要考虑的。
这或许是速度与性能的trade-off?
3.Is object localization for free - Weakly-supervised learning with convolutional neural networks
——弱监督做object detection的文章。首先fc layer当做conv layer与上面这篇文章思想一致。同一时候把最后max pooling之前的feature map看做包括class localization的信息，仅仅只是从第五章“Does adding object-level supervision help classification”的结果看。效果虽好，可是这一物理解释可能不够完好。
4.Shape-Tailored Local Descriptors and Their Application to Segmentation and Tracking
Authors: Naeemullah Khan, Marei Algarni, Anthony Yezzi, Ganesh Sundaramoorthi
5.Deep Filter Banks for Texture Recognition and Segmentation
Authors: Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi
6.Deeply learned face representations are sparse, selective, and robust, Yi Sun, Xiaogang Wang, Xiaoou Tang
——DeepID系列之DeepID2+。在DeepID2之上的改进是添加了网络的规模(feature map数目)。另外每一层都接入一个全连通层加supervision。
最精彩的地方应该是后面对神经元性能的分析，发现了三个特点：1.中度稀疏最大化了区分性。并适合二值化；2.身份和attribute选择性。3.对遮挡的鲁棒性。这三个特点在模型训练时都没有显示或隐含地强加了约束。都是CNN自己学的。
Image and Video Processing and Restoration
1.Fast and Flexible Convolutional Sparse Coding
Authors: Felix Heide, Wolfgang Heidrich, Gordon Wetzstein
2.What do 15,000 Object Categories Tell Us About Classifying and Localizing Actions?
Authors: Mihir Jain, Jan C. van Gemert, Cees G. M. Snoek
——物品的分类对行为检測有帮助作用。这篇文章是第一篇关于这个话题进行探讨的。是个深坑，大家能够关注一下，考虑占坑。
3.Hypercolumns for Object Segmentation and Fine-Grained Localization
Authors:Bharath Hariharan, Pablo Arbeláez, Ross Girshick, Jitendra Malik
——一个非常好的思路！曾经的CNN或者R-CNN，我们总是用最后一层作为class label。倒数第二层作为feature。这篇文章的作者想到利用每一层的信息。
由于对于每个pixel来讲，在全部层数上它都有被激发和不被激发两种态。作者利用了每一层的激发态作为一个feature vector来帮助自己做精细的物体检測。
3D Models and Images
1.The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose
Authors: Silvia Zuffi, Michael J. Black
2.3D Shape Estimation From 2D Landmarks: A Convex Relaxation Approach
Authors: Xiaowei Zhou, Spyridon Leonardos, Xiaoyan Hu, Kostas Daniilidis
Images and Language
这个类别的文章须要好好看看，对思路的发散非常有帮助
1.Show and Tell: A Neural Image Caption Generator
Authors: Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan
2.Deep Visual-Semantic Alignments for Generating Image Descriptions
Authors: Andrej Karpathy, Li Fei-Fei
3.Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Authors: Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell
4.Becoming the Expert - Interactive Multi-Class Machine Teaching
Authors: Edward Johns, Oisin Mac Aodha, Gabriel J. Brostow
其他
參考文献一：CNN卷积神经网络的改进（15年最新paper）：
http://blog.csdn.net/u010402786/article/details/50499864
文章中的四篇文章也值得一读，当中一篇在上面出现过。
一定要自己下载下来看一看。
參考文献二：这是另外一个博主的博客，也是对CVPR的文章进行了整理：
http://blog.csdn.net/jwh_bupt/article/details/46916653
基本很多文章里面没有凝视核心思想，接下来慢慢补充。2016-01-20