全球最全计算机视觉资料（4：分割和识别）

目标检测和深度学习

Segmentation

1. Alexander Kolesnikov, Christoph Lampert, Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, ECCV, 2016. [http://pub.ist.ac.at/~akolesnikov/files/ECCV2016/main.pdf] [https://github.com/kolesman/SEC]
2. Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv:1504.01013. [http://arxiv.org/pdf/1504.01013]
3. Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel, Deeply Learning the Messages in Message Passing Inference, arXiv:1508.02108. [http://arxiv.org/pdf/1506.02108]
4. Deep Parsing Network . Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang, Semantic Image Segmentation via Deep Parsing Network, arXiv:1509.02634 / ICCV 2015 [http://arxiv.org/pdf/1509.02634.pdf]
5. CentraleSuperBoundaries, Iasonas Kokkinos, Surpassing Humans in Boundary Detection using Deep Learning INRIA [http://arxiv.org/pdf/1511.07386]
6. BoxSup. Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation [http://arxiv.org/pdf/1503.01640]
7. Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, Learning Deconvolution Network for Semantic Segmentation, arXiv:1505.04366. [http://arxiv.org/pdf/1505.04366]
8. Seunghoon Hong, Hyeonwoo Noh, Bohyung Han, Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation, arXiv:1506.04924. [http://arxiv.org/pdf/1506.04924]
9. Seunghoon Hong, Junhyuk Oh, Bohyung Han, and Honglak Lee, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network. [http://arxiv.org/pdf/1512.07928.pdf] Project Page[http://cvlab.postech.ac.kr/research/transfernet/]
10. Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr, Conditional Random Fields as Recurrent Neural Networks. [http://arxiv.org/pdf/1502.03240]
11. Liang-Chieh Chen, George Papandreou, Kevin Murphy, Alan L. Yuille, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, arXiv:1502.02734. [http://arxiv.org/pdf/1502.02734]
12. Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich, Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015 [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mostajabi_Feedforward_Semantic_Segmentation_2015_CVPR_paper.pdf]
13. Holger Caesar, Jasper Uijlings, Vittorio Ferrari, Joint Calibration for Semantic Segmentation. [http://arxiv.org/pdf/1507.01581]
14. Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015. [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf]
15. Bharath Hariharan, Pablo Arbelaez, Ross Girshick, Jitendra Malik, Hypercolumns for Object Segmentation and Fine-Grained Localization, CVPR, 2015. [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hariharan_Hypercolumns_for_Object_2015_CVPR_paper.pdf]
16. Abhishek Sharma, Oncel Tuzel, David W. Jacobs, Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015. [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Sharma_Deep_Hierarchical_Parsing_2015_CVPR_paper.pdf]
17. Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers, ICML, 2012. [http://yann.lecun.com/exdb/publis/pdf/farabet-icml-12.pdf]
18. Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Learning Hierarchical Features for Scene Labeling, PAMI, 2013. [http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf]
19. Fisher Yu, Vladlen Koltun, "Multi-Scale Context Aggregation by Dilated Convolutions", ICLR 2016, [http://arxiv.org/pdf/1511.07122v2.pdf]
20. Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, "Weakly supervised graph based semantic segmentation by learning communities of image-parts", ICCV, 2015, [http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Pourian_Weakly_Supervised_Graph_ICCV_2015_paper.pdf]

Object Recognition

1. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell [http://arxiv.org/abs/1310.1531]
2. CNN Features off-the-shelf: an Astounding Baseline for Recognition CVPR 2014 [http://arxiv.org/abs/1403.6382]
3. HD-CNN: Hierarchical Deep Convolutional Neural Network for Image Classification intro: ICCV 2015 [https://arxiv.org/abs/1410.0736]
4. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. ImageNet top-5 error: 4.94% [http://arxiv.org/abs/1502.01852]
5. Humans and deep networks largely agree on which kinds of variation make object recognition harder [http://arxiv.org/abs/1604.06486]
6. FusionNet: 3D Object Classification Using Multiple Data Representations [https://arxiv.org/abs/1607.05695]
7. Deep FisherNet for Object Classification [http://arxiv.org/abs/1608.00182]
8. Factorized Bilinear Models for Image Recognition [https://arxiv.org/abs/1611.05709]
9. Hyperspectral CNN Classification with Limited Training Samples [https://arxiv.org/abs/1611.09007]
10. The More You Know: Using Knowledge Graphs for Image Classification [https://arxiv.org/abs/1612.04844]
11. MaxMin Convolutional Neural Networks for Image Classification [http://webia.lip6.fr/~thomen/papers/Blot_ICIP_2016.pdf]
12. Cost-Effective Active Learning for Deep Image Classification. TCSVT 2016. [https://arxiv.org/abs/1701.03551]
13. Deep Collaborative Learning for Visual Recognition [https://www.arxiv.org/abs/1703.01229]
14. Convolutional Low-Resolution Fine-Grained Classification [https://arxiv.org/abs/1703.05393]
15. Deep Mixture of Diverse Experts for Large-Scale Visual Recognition [https://arxiv.org/abs/1706.07901] Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition [https://arxiv.org/abs/1707.06335]
16. Why Do Deep Neural Networks Still Not Recognize These Images?: A Qualitative Analysis on Failure Cases of ImageNet Classification [https://arxiv.org/abs/1709.03439]
17. B-CNN: Branch Convolutional Neural Network for Hierarchical Classification [https://arxiv.org/abs/1709.09890]
18. Multiple Object Recognition with Visual Attention [https://arxiv.org/abs/1412.7755]
19. Multiple Instance Learning Convolutional Neural Networks for Object Recognition [https://arxiv.org/abs/1610.03155]
20. Deep Learning Face Representation from Predicting 10,000 Classes. intro: CVPR 2014 [http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf]
21. Deep Learning Face Representation by Joint Identification-Verification [https://arxiv.org/abs/1406.4773]
22. Deeply learned face representations are sparse, selective, and robust [http://arxiv.org/abs/1412.1265]
23. FaceNet: A Unified Embedding for Face Recognition and Clustering [http://arxiv.org/abs/1503.03832]
24. Bilinear CNN Models for Fine-grained Visual Recognition [http://vis-www.cs.umass.edu/bcnn/]
25. DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment [http://arxiv.org/abs/1606.05675]
26. Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios [http://or.nsfc.gov.cn/bitstream/00001903-5/417802/1/1000014103914.pdf]

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