computer vision

入門學習

1. 計算機視覺：讓冰冷的機器看懂這個多彩的世界 by

   • [http://www.msra.cn/zh-cn/news/features/computer-vision-20150210]

2. 深度學習與視覺計算 by 王亮 中科院自動化所

   • [http://www.caai.cn/index.php?s=/Home/Article/qikandetail/year/2017/month/04.html]

3. 如何做好計算機視覺的研究？ by 華剛博士

   • 微軟亞洲研究院資深研究他將擔任2019國際模式識別和計算機視覺大會 （CVPR 2019）的程序主席，以及CVPR 2017和ACM MM 2017的領域主席。
   • [http://www.msra.cn/zh-cn/news/features/do-research-in-computer-vision-20161205]

4. 計算機視覺 微軟亞洲研究院係列文章

   • 通俗介紹計算機視覺在生活中的各種應用。
   • [http://www.msra.cn/zh-cn/research/computer-vision]

5. 卷積神經網絡如何進行圖像識別

   • [http://www.infoq.com/cn/articles/convolutional-neural-networks-image-recognition]

6. 相似圖片搜索的原理 阮一峰

   • [http://www.ruanyifeng.com/blog/2011/07/principle_of_similar_image_search.html]

7. 如何識別圖像邊緣？ 阮一峰

   • [http://www.ruanyifeng.com/blog/2016/07/edge-recognition.html]

8. 圖像目標檢測（Object Detection）原理與實現 （1-6）

   • [http://www.voidcn.com/article/p-xnjyqlkj-ua.html]

9. 運動目標跟蹤係列（1-17） - [http://blog.csdn.net/App_12062011/article/category/6269524/1]

10. 看圖說話的AI小朋友——圖像標注趣談(上，下)

    • [https://zhuanlan.zhihu.com/p/22408033]
    • [https://zhuanlan.zhihu.com/p/22520434]

11. Video Analysis 相關領域介紹之Video Captioning(視頻to文字描述)

    • [https://zhuanlan.zhihu.com/p/26730181]

12. 從特斯拉到計算機視覺之「圖像語義分割」

    • [https://zhuanlan.zhihu.com/p/21824299]

13. 視覺求索公眾號相關文章係列，

    • 淺談人工智能：現狀、任務、構架與統一 | 正本清源 [http://mp.weixin.qq.com/s/-wSYLu-XvOrsST8_KEUa-Q]
    • 人生若隻如初見 | 學術人生 [https://mp.weixin.qq.com/s/kFA7bI_FFjZQkBNDvcn01g]
    • 初探計算機視覺的三個源頭、兼談人工智能｜正本清源 [https://mp.weixin.qq.com/s/2ytV5Bt50yhYOFYXYQe6ZQ]

綜述

1. Annotated Computer Vision Bibliography: Table of Contents. Since 1994 Keith Price從1994年開始做了這個索引，涵蓋了所有計算機視覺裏麵所有topic，所有subtopic的著作，包括論文，教材，還對各類主題的關鍵詞。這個網站頻繁更新（最近一次是2017年8月28號），收錄每個方向重要期刊，會議文獻和書籍，並且保證了所有鏈接不失效。
2. What Sparked Video Research in 1877? The Overlooked Role of the Siemens Artificial Eye by Mark Schubin 2017http://ieeexplore.ieee.org/document/7857854/
3. Giving machines humanlike eyes. by Posch, C., Benosman, R., Etienne-Cummings, R. 2015http://ieeexplore.ieee.org/document/7335800/
4. Seeing is not enough by Tom GellerOberlin, OHhttps://dl.acm.org/citation.cfm?id=2001276
5. Visual Tracking: An Experimental Surveyhttps://dl.acm.org/citation.cfm?id=2693387
6. A survey on object recognition and segmentation techniqueshttp://ieeexplore.ieee.org/document/7724975/
7. A Review of Image Recognition with Deep Convolutional Neural Networkhttps://link.springer.com/chapter/10.1007/978-3-319-63309-1_7
8. Recent Advance in Content-based Image Retrieval: A Literature Survey. Wengang Zhou, Houqiang Li, and Qi Tian 2017https://arxiv.org/pdf/1706.06064.pdf
9. Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures 2016https://www.jair.org/media/4900/live-4900-9139-jair.pdf

進階論文

Image Classification

1. Microsoft Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition [http://arxiv.org/pdf/1512.03385v1.pdf] [[http://image-net.org/challenges/talks/ilsvrc2015_deep_residual_learning_kaiminghe.pdf]]
2. Microsoft Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, [http://arxiv.org/pdf/1502.01852]
3. Batch Normalization Sergey Ioffe, Christian Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift[http://arxiv.org/pdf/1502.03167]
4. GoogLeNet Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, CVPR, 2015. [http://arxiv.org/pdf/1409.4842]
5. VGG-Net Karen Simonyan and Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Visual Recognition, ICLR, 2015. [http://www.robots.ox.ac.uk/vgg/research/verydeep/] [http://arxiv.org/pdf/1409.1556]
6. AlexNet Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS, 2012. [http://papers.nips.cc/book/advances-in-neural-information-processing-systems-25-2012]

Object Detection

進階文章

1. Deep Neural Networks for Object Detection （基於DNN的對象檢測）NIPS2013:
   • [https://cis.temple.edu/yuhong/teach/2014spring/papers/NIPS2013DNNOD.pdf]
2. R-CNNRich feature hierarchies for accurate object detection and semantic segmentation:
   • [https://arxiv.org/abs/1311.2524]
3. Fast R-CNN:
   • [http://arxiv.org/abs/1504.08083]
4. Faster R-CNNFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks:
   • [http://arxiv.org/abs/1506.01497]
5. Scalable Object Detection using Deep Neural Networks
   • [http://arxiv.org/abs/1312.2249]
6. Scalable, High-Quality Object Detection
   • [http://arxiv.org/abs/1412.1441]
7. SPP-NetSpatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
   • [http://arxiv.org/abs/1406.4729]
8. DeepID-NetDeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
   • [http://www.ee.cuhk.edu.hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html]
9. Object Detectors Emerge in Deep Scene CNNs
   • [http://arxiv.org/abs/1412.6856]
10. segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
    • [https://arxiv.org/abs/1502.04275]
11. Object Detection Networks on Convolutional Feature Maps
    • [http://arxiv.org/abs/1504.06066]
12. Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
    • [http://arxiv.org/abs/1504.03293]
13. DeepBox: Learning Objectness with Convolutional Networks
    • [http://arxiv.org/abs/1504.03293]
14. Object detection via a multi-region & semantic segmentation-aware CNN model
    • [http://arxiv.org/abs/1505.01749]
15. You Only Look Once: Unified, Real-Time Object Detection
    • [http://arxiv.org/abs/1506.02640]
16. YOLOv2 YOLO9000: Better, Faster, Stronger
    • [https://arxiv.org/abs/1612.08242]
17. AttentionNet: Aggregating Weak Directions for Accurate Object Detection
    • [http://arxiv.org/abs/1506.07704]
18. DenseBox: Unifying Landmark Localization with End to End Object Detection
    • [http://arxiv.org/abs/1509.04874]
19. SSD: Single Shot MultiBox Detector
    • [http://arxiv.org/abs/1512.02325]
20. DSSD : Deconvolutional Single Shot Detector
    • [https://arxiv.org/abs/1701.06659]
21. G-CNN: an Iterative Grid Based Object Detector
    • [http://arxiv.org/abs/1512.07729]
22. HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
    • [http://arxiv.org/abs/1604.00600]
23. A MultiPath Network for Object Detection
    • [http://arxiv.org/abs/1604.02135]
24. R-FCN: Object Detection via Region-based Fully Convolutional Networks
    • [http://arxiv.org/abs/1605.06409]
25. A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
    • [http://arxiv.org/abs/1607.07155]
26. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
    • [http://arxiv.org/abs/1608.08021]
27. Feature Pyramid Networks for Object Detection
    • [https://arxiv.org/abs/1612.03144]
28. Learning Chained Deep Features and Classifiers for Cascade in Object Detection
    • [https://arxiv.org/abs/1702.07054]
29. DSOD: Learning Deeply Supervised Object Detectors from Scratch
    • [https://arxiv.org/abs/1708.01241]
30. Focal Loss for Dense Object Detection ICCV 2017 Best student paper award. Facebook AI Research
    • [https://arxiv.org/abs/1708.02002]

Video Classification

1. Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, "Delving Deeper into Convolutional Networks for Learning Video Representations", ICLR 2016. [http://arxiv.org/pdf/1511.06432v4.pdf]
2. Michael Mathieu, camille couprie, Yann Lecun, "Deep Multi Scale Video Prediction Beyond Mean Square Error", ICLR 2016. Paper [http://arxiv.org/pdf/1511.05440v6.pdf]
3. Donahue, Jeffrey, et al. Long-term recurrent convolutional networks for visual recognition and description CVPR 2015https://arxiv.org/abs/1411.4389
4. Karpathy, Andrej, et al. Large-scale Video Classification with Convolutional Neural Networks. CVPR 2014http://cs.stanford.edu/people/karpathy/deepvideo/
5. Yue-Hei Ng, Joe, et al. Beyond short snippets: Deep networks for video classification. CVPR 2015https://arxiv.org/abs/1503.08909
6. Tran, Du, et al. Learning Spatiotemporal Features with 3D Convolutional Networks. ICCV 2015https://arxiv.org/abs/1412.0767

Object Tracking

1. Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han, Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network, arXiv:1502.06796. [http://arxiv.org/pdf/1502.06796]
2. Hanxi Li, Yi Li and Fatih Porikli, DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, BMVC, 2014. [http://www.bmva.org/bmvc/2014/files/paper028.pdf]
3. N Wang, DY Yeung, Learning a Deep Compact Image Representation for Visual Tracking, NIPS, 2013. [http://winsty.net/papers/dlt.pdf]
4. Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, Hierarchical Convolutional Features for Visual Tracking, ICCV 2015 Paper [http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Ma_Hierarchical_Convolutional_Features_ICCV_2015_paper.pdf] [https://github.com/jbhuang0604/CF2]
5. Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, Visual Tracking with fully Convolutional Networks, ICCV 2015 [http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf] [https://github.com/scott89/FCNT]
6. Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking. [http://arxiv.org/pdf/1510.07945.pdf] [https://github.com/HyeonseobNam/MDNet] [http://cvlab.postech.ac.kr/research/mdnet/]

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/BlotICIP2016.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 [http://papers.nips.cc/paper/5416-analog-memories-in-a-balanced-rate-based-network-of-e-i-neurons]
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 Recognitionhttp://vis-www.cs.umass.edu/bcnn/
25. DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessmenthttp://arxiv.org/abs/1606.05675
26. Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarioshttp://or.nsfc.gov.cn/bitstream/00001903-5/417802/1/1000014103914.pdf

Image Captioning

1. m-RNN模型《 Explain Images with Multimodal Recurrent Neural Networks》 2014https://arxiv.org/pdf/1410.1090.pdf
2. NIC模型 《Show and Tell: A Neural Image Caption Generator》2014
3. MS Captivator From captions to visual concepts and back 2014
4. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention 2015
5. What Value Do Explicit High Level Concepts Have in Vision to Language Problems? 2016https://arxiv.org/pdf/1506.01144.pdf
6. Guiding Long-Short Term Memory for Image Caption Generation 2015https://arxiv.org/pdf/1509.04942.pdf
7. Watch What You Just Said: Image Captioning with Text-Conditional Attention 2016https://arxiv.org/pdf/1606.04621.pdfhttps://github.com/LuoweiZhou/e2e-gLSTM-sc
8. Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning 2017https://arxiv.org/pdf/1612.01887.pdf
9. Self-critical Sequence Training for Image Captioning 2017 CVPRhttps://arxiv.org/pdf/1612.00563.pdf
10. Deep Reinforcement Learning-based Image Captioning with Embedding Reward 2017 cvprhttps://arxiv.org/abs/1704.03899
11. Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning 2017 cvprhttps://arxiv.org/pdf/1612.01887.pdfhttps://github.com/jiasenlu/AdaptiveAttention
12. Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel, Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models, arXiv:1411.2539.https://arxiv.org/abs/1411.2539
13. Berkeley Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual Recognition and Descriptionhttps://arxiv.org/abs/1411.4389
14. UML / UT Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, NAACL-HLT, 2015.https://arxiv.org/abs/1412.4729
15. CMU / Microsoft Xinlei Chen, C. Lawrence Zitnick, Learning a Recurrent Visual Representation for Image Caption Generationhttps://arxiv.org/abs/1411.5654
16. Xinlei Chen, C. Lawrence Zitnick, Mind’s Eye: A Recurrent Visual Representation for Image Caption Generation, CVPR 2015https://www.cs.cmu.edu/~xinleic/papers/cvpr15_rnn.pdf
17. Facebook Remi Lebret, Pedro O. Pinheiro, Ronan Collobert, Phrase-based Image Captioning, arXiv:1502.03671 / ICML 2015https://arxiv.org/abs/1502.03671
18. UCLA / Baidu Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan L. Yuille, Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Imageshttps://arxiv.org/abs/1504.06692
19. MS + Berkeley Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, Exploring Nearest Neighbor Approaches for Image Captioninghttps://arxiv.org/abs/1505.04467
20. Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell, Language Models for Image Captioning: The Quirks and What Workshttps://arxiv.org/abs/1505.01809
21. Adelaide Qi Wu, Chunhua Shen, Anton van den Hengel, Lingqiao Liu, Anthony Dick, Image Captioning with an Intermediate Attributes Layerhttps://arxiv.org/abs/1506.01144v1
22. Tilburg Grzegorz Chrupala, Akos Kadar, Afra Alishahi, Learning language through pictureshttps://arxiv.org/abs/1506.03694
23. Univ. Montreal Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia Content using Attention-based Encoder-Decoder Networkshttps://arxiv.org/abs/1507.01053
24. Cornell Jack Hessel, Nicolas Savva, Michael J. Wilber, Image Representations and New Domains in Neural Image Captioninghttps://arxiv.org/abs/1508.02091
25. MS + City Univ. of HongKong Ting Yao, Tao Mei, and Chong-Wah Ngo, "Learning Query and Image Similarities with Ranking Canonical Correlation Analysis", ICCV, 2015https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yao_Learning_Query_and_ICCV_2015_paper.pdf
26. Mao J, Xu W, Yang Y, et al. Deep Captioning with Multimodal Recurrent Neural Networks (m- RNN) 2015.https://arxiv.org/abs/1412.6632
27. Pan Y, Yao T, Li H, et al. Video Captioning with Transferred Semantic Attributes 2016.https://arxiv.org/abs/1611.07675
28. Johnson J, Karpathy A, Li F F. DenseCap: Fully Convolutional Localization Networks for Dense Captioninghttps://arxiv.org/abs/1511.07571
29. I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. NIPS 2014.https://arxiv.org/abs/1409.3215
30. Karpathy A, Li F F. Deep Visual-Semantic Alignments for Generating Image Descriptions TPAMI 2015https://arxiv.org/abs/1412.2306
31. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei. Large-scale video classification with convolutional neural networks. CVPR, 2014.http://cs.stanford.edu/people/karpathy/deepvideo/
32. Yao T, Pan Y, Li Y, et al. Boosting Image Captioning with Attributes 2016.https://arxiv.org/abs/1611.01646
33. Venugopalan S, Rohrbach M, Donahue J, et al. Sequence to Sequence -- Video to Text. 2015.https://arxiv.org/abs/1505.00487

Video Captioning

. Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual Recognition and Description, CVPR, 2015. [http://jeffdonahue.com/lrcn/] [http://arxiv.org/pdf/1411.4389.pdf] 2. Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, arXiv:1412.4729. UT / UML / Berkeley [http://arxiv.org/pdf/1412.4729] 3. Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui, Joint Modeling Embedding and Translation to Bridge Video and Language, arXiv:1505.01861. Microsoft [http://arxiv.org/pdf/1505.01861] 4. Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko, Sequence to Sequence--Video to Text, arXiv:1505.00487. UT / Berkeley / UML [http://arxiv.org/pdf/1505.00487] 5. Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville, Describing Videos by Exploiting Temporal Structure, arXiv:1502.08029 Univ. Montreal / Univ. Sherbrooke [http://arxiv.org/pdf/1502.08029.pdf]] 6. Anna Rohrbach, Marcus Rohrbach, Bernt Schiele, The Long-Short Story of Movie Description, arXiv:1506.01698 MPI / Berkeley [http://arxiv.org/pdf/1506.01698.pdf]] 7. Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler, Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books, arXiv:1506.06724 Univ. Toronto / MIT [[http://arxiv.org/pdf/1506.06724.pdf]] 8. Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia Content using Attention-based Encoder-Decoder Networks, arXiv:1507.01053 Univ. Montreal [http://arxiv.org/pdf/1507.01053.pdf] 9. Dotan Kaufman, Gil Levi, Tal Hassner, Lior Wolf, Temporal Tessellation for Video Annotation and Summarization, arXiv:1612.06950. TAU / USC [[https://arxiv.org/pdf/1612.06950.pdf]] 10. Chiori Hori, Takaaki Hori, Teng-Yok Lee, Kazuhiro Sumi, John R. Hershey, Tim K. Marks Attention-Based Multimodal Fusion for Video Descriptionhttps://arxiv.org/abs/1701.0312611. Describing Videos using Multi-modal Fusionhttps://dl.acm.org/citation.cfm?id=298406512. Andrew Shin , Katsunori Ohnishi , Tatsuya Harada Beyond caption to narrative: Video captioning with multiple sentenceshttp://ieeexplore.ieee.org/abstract/document/7532983/13. Jianfeng Dong, Xirong Li, Cees G. M. Snoek Word2VisualVec: Image and Video to Sentence Matching by Visual Feature Predictionhttps://pdfs.semanticscholar.org/de22/8875bc33e9db85123469ef80fc0071a92386.pdf14. Multimodal Video Descriptionhttps://dl.acm.org/citation.cfm?id=298406615. Xiaodan Liang, Zhiting Hu, Hao Zhang, Chuang Gan, Eric P. Xing Recurrent Topic-Transition GAN for Visual Paragraph Generationhttps://arxiv.org/abs/1703.0702216. Weakly Supervised Dense Video Captioning（CVPR2017） 17. Multi-Task Video Captioning with Video and Entailment Generation（ACL2017）

Visual Question Answering

1. Kushal Kafle, and Christopher Kanan.An Analysis of Visual Question Answering Algorithms.arXiv:1703.09684, 2017. [https://arxiv.org/abs/1703.09684]
2. Hyeonseob Nam, Jung-Woo Ha, Jeonghee Kim, Dual Attention Networks for Multimodal Reasoning and Matching, arXiv:1611.00471, 2016. [https://arxiv.org/abs/1611.00471]
3. Jin-Hwa Kim, Kyoung Woon On, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang, Hadamard Product for Low-rank Bilinear Pooling, arXiv:1610.04325, 2016. [https://arxiv.org/abs/1610.04325]
4. Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, Marcus Rohrbach, Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding, arXiv:1606.01847, 2016. [https://arxiv.org/abs/1606.01847] [[code]][https://github.com/akirafukui/vqa-mcb]
5. Kuniaki Saito, Andrew Shin, Yoshitaka Ushiku, Tatsuya Harada, DualNet: Domain-Invariant Network for Visual Question Answering. arXiv:1606.06108v1, 2016. [https://arxiv.org/pdf/1606.06108.pdf]
6. Arijit Ray, Gordon Christie, Mohit Bansal, Dhruv Batra, Devi Parikh, Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions, arXiv:1606.06622, 2016. [https://arxiv.org/pdf/1606.06622v1.pdf]
7. Hyeonwoo Noh, Bohyung Han, Training Recurrent Answering Units with Joint Loss Minimization for VQA, arXiv:1606.03647, 2016. [http://arxiv.org/abs/1606.03647v1]
8. Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh, Hierarchical Question-Image Co-Attention for Visual Question Answering, arXiv:1606.00061, 2016. [https://arxiv.org/pdf/1606.00061v2.pdf] [[code]][https://github.com/jiasenlu/HieCoAttenVQA]
9. Jin-Hwa Kim, Sang-Woo Lee, Dong-Hyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang, Multimodal Residual Learning for Visual QA, arXiv:1606.01455, 2016. [https://arxiv.org/pdf/1606.01455v1.pdf]
10. Peng Wang, Qi Wu, Chunhua Shen, Anton van den Hengel, Anthony Dick, FVQA: Fact-based Visual Question Answering, arXiv:1606.05433, 2016. [https://arxiv.org/pdf/1606.05433.pdf]
11. Ilija Ilievski, Shuicheng Yan, Jiashi Feng, A Focused Dynamic Attention Model for Visual Question Answering, arXiv:1604.01485. [https://arxiv.org/pdf/1604.01485v1.pdf]
12. Yuke Zhu, Oliver Groth, Michael Bernstein, Li Fei-Fei, Visual7W: Grounded Question Answering in Images, CVPR 2016. [http://arxiv.org/abs/1511.03416]
13. Hyeonwoo Noh, Paul Hongsuck Seo, and Bohyung Han, Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction, CVPR, 2016.[http://arxiv.org/pdf/1511.05756.pdf]
14. Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, Learning to Compose Neural Networks for Question Answering, NAACL 2016. [http://arxiv.org/pdf/1601.01705.pdf]
15. Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein, Deep compositional question answering with neural module networks, CVPR 2016. [https://arxiv.org/abs/1511.02799]
16. Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola, Stacked Attention Networks for Image Question Answering, CVPR 2016. [http://arxiv.org/abs/1511.02274] [[code]][https://github.com/JamesChuanggg/san-torch]
17. Kevin J. Shih, Saurabh Singh, Derek Hoiem, Where To Look: Focus Regions for Visual Question Answering, CVPR, 2015. [http://arxiv.org/pdf/1511.07394v2.pdf]
18. Kan Chen, Jiang Wang, Liang-Chieh Chen, Haoyuan Gao, Wei Xu, Ram Nevatia, ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering, arXiv:1511.05960v1, Nov 2015. [http://arxiv.org/pdf/1511.05960v1.pdf]
19. Huijuan Xu, Kate Saenko, Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering, arXiv:1511.05234v1, Nov 2015. [http://arxiv.org/abs/1511.05234]
20. Kushal Kafle and Christopher Kanan, Answer-Type Prediction for Visual Question Answering, CVPR 2016. [http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Kafle_Answer-Type_Prediction_for_CVPR_2016_paper.html]
21. Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, ICCV, 2015. [http://arxiv.org/pdf/1505.00468]
22. Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, VQA: Visual Question Answering, ICCV, 2015. [http://arxiv.org/pdf/1505.00468] [[code]][https://github.com/JamesChuanggg/VQA-tensorflow]
23. Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus, Simple Baseline for Visual Question Answering, arXiv:1512.02167v2, Dec 2015. [http://arxiv.org/abs/1512.02167]
24. Hauyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, Wei Xu, Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering, NIPS 2015. [http://arxiv.org/pdf/1505.05612.pdf]
25. Mateusz Malinowski, Marcus Rohrbach, Mario Fritz, Ask Your Neurons: A Neural-based Approach to Answering Questions about Images, ICCV 2015. [http://arxiv.org/pdf/1505.01121v3.pdf]
26. Mengye Ren, Ryan Kiros, Richard Zemel, Exploring Models and Data for Image Question Answering, ICML 2015. [http://arxiv.org/pdf/1505.02074.pdf]
27. Mateusz Malinowski, Mario Fritz, Towards a Visual Turing Challe, NIPS Workshop 2015. [http://arxiv.org/abs/1410.8027]
28. Mateusz Malinowski, Mario Fritz, A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input, NIPS 2014. [http://arxiv.org/pdf/1410.0210v4.pdf]
29. Hedi Ben-younes, Remi Cadene, Matthieu Cord, Nicolas Thome: MUTAN: Multimodal Tucker Fusion for Visual Question Answering [https://arxiv.org/pdf/1705.06676.pdf] [[Code]][https://github.com/Cadene/vqa.pytorch]
30. Jin-Hwa Kim, Kyoung Woon On, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang, Hadamard Product for Low-rank Bilinear Pooling, arXiv:1610.04325, 2016. [https://arxiv.org/abs/1610.04325]
31. Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, Marcus Rohrbach, Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding, arXiv:1606.01847, 2016. [https://arxiv.org/abs/1606.01847]

Edge Detection

1. Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection Holistically-Nested Edge Detection [http://arxiv.org/pdf/1504.06375] [https://github.com/s9xie/hed]
2. Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015. [http://arxiv.org/pdf/1412.1123]
3. Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang, DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection, CVPR, 2015. [http://mc.eistar.net/UpLoadFiles/Papers/DeepContour_cvpr15.pdf]

Human Pose Estimation

1. Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh, Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields, CVPR, 2017.
2. Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Peter Gehler, and Bernt Schiele, Deepcut: Joint subset partition and labeling for multi person pose estimation, CVPR, 2016.
3. Shih-En Wei, Varun Ramakrishna, Takeo Kanade, and Yaser Sheikh, Convolutional pose machines, CVPR, 2016.
4. Alejandro Newell, Kaiyu Yang, and Jia Deng, Stacked hourglass networks for human pose estimation, ECCV, 2016.
5. Tomas Pfister, James Charles, and Andrew Zisserman, Flowing convnets for human pose estimation in videos, ICCV, 2015.
6. Jonathan J. Tompson, Arjun Jain, Yann LeCun, Christoph Bregler, Joint training of a convolutional network and a graphical model for human pose estimation, NIPS, 2014.

Image Generation

1. Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu. "Conditional Image Generation with PixelCNN Decoders"[https://arxiv.org/pdf/1606.05328v2.pdf][https://github.com/kundan2510/pixelCNN]
2. Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, "Learning to Generate Chairs with Convolutional Neural Networks", CVPR, 2015. [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf]
3. Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, "DRAW: A Recurrent Neural Network For Image Generation", ICML, 2015. [https://arxiv.org/pdf/1502.04623v2.pdf]
4. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Generative Adversarial Networks, NIPS, 2014. [http://arxiv.org/abs/1406.2661]
5. Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, NIPS, 2015. [http://arxiv.org/abs/1506.05751]
6. Lucas Theis, Aäron van den Oord, Matthias Bethge, "A note on the evaluation of generative models", ICLR 2016. [http://arxiv.org/abs/1511.01844]
7. Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence, "Variationally Auto-Encoded Deep Gaussian Processes", ICLR 2016. [http://arxiv.org/pdf/1511.06455v2.pdf]
8. Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, "Generating Images from Captions with Attention", ICLR 2016, [http://arxiv.org/pdf/1511.02793v2.pdf]
9. Jost Tobias Springenberg, "Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks", ICLR 2016, [http://arxiv.org/pdf/1511.06390v1.pdf]
10. Harrison Edwards, Amos Storkey, "Censoring Representations with an Adversary", ICLR 2016, [http://arxiv.org/pdf/1511.05897v3.pdf]
11. Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii, "Distributional Smoothing with Virtual Adversarial Training", ICLR 2016, [http://arxiv.org/pdf/1507.00677v8.pdf]
12. Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros, "Generative Visual Manipulation on the Natural Image Manifold", ECCV 2016. [https://arxiv.org/pdf/1609.03552v2.pdf] [https://github.com/junyanz/iGAN] [https://youtu.be/9c4z6YsBGQ0]
13. Alec Radford, Luke Metz, Soumith Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR 2016. [http://arxiv.org/pdf/1511.06434.pdf]

課程

0. 斯坦福視覺實驗室主頁：http://vision.stanford.edu/李飛飛組CS131, CS231A, CS231n 三個課程，可是說是最好的計算機視覺課程。
1. CS 131 Computer Vision: Foundations and Applications： 基礎知識：主要講傳統的邊緣檢測，特征點描述，相機標定，全景圖拚接等知識 [http://vision.stanford.edu/teaching/cs131_fall1415/schedule.html]
2. CS231A Computer Vision: from 3D reconstruction to recognition: [http://cvgl.stanford.edu/teaching/cs231a_winter1415/schedule.html]
3. CS231n 2017: Convolutional Neural Networks for Visual Recognition 主要講卷積神經網絡的具體結構，各組成部分的原理優化以及各種應用。 [http://vision.stanford.edu/teaching/cs231n/] 國內地址：[http://www.bilibili.com/video/av13260183/]
4. Stanford CS231n 2016 : Convolutional Neural Networks for Visual Recognition
   • homepage: [http://cs231n.stanford.edu/]
   • homepage: [http://vision.stanford.edu/teaching/cs231n/index.html]
   • syllabus: [http://vision.stanford.edu/teaching/cs231n/syllabus.html]
   • course notes: [http://cs231n.github.io/]
   • youtube: [https://www.youtube.com/watch?v=NfnWJUyUJYU&feature=youtu.be]
   • mirror: [http://pan.baidu.com/s/1pKsTivp]
   • mirror: [http://pan.baidu.com/s/1c2wR8dy]
   • 網易中文字幕：http://study.163.com/course/introduction/1003223001.htm
   • assignment 1: [http://cs231n.github.io/assignments2016/assignment1/]
   • assignment 2: [http://cs231n.github.io/assignments2016/assignment2/]
   • assignment 3: [http://cs231n.github.io/assignments2016/assignment3/]
5. 1st Summer School on Deep Learning for Computer Vision Barcelona: (July 4-8, 2016)
   • 深度學習計算機視覺夏季學校課程， 包含基礎知識以及許多深度學習在計算機視覺中的應用，比如分類，檢測，captioning等等
   • homepage(slides+videos): [http://imatge-upc.github.io/telecombcn-2016-dlcv/]
   • homepage: [https://imatge.upc.edu/web/teaching/deep-learning-computer-vision]
   • youtube: [https://www.youtube.com/user/imatgeupc/videos?shelf_id=0&sort=dd&view=0]
6. 2nd Summer School on Deep Learning for Computer VisionBarcelona (June 21-27, 2017)https://telecombcn-dl.github.io/2017-dlcv/

Turorial

1. Intro to Deep Learning for Computer Vision 2016http://chaosmail.github.io/deeplearning/2016/10/22/intro-to-deep-learning-for-computer-vision/
2. CVPR 2014 Tutorial on Deep Learning in Computer Visionhttps://sites.google.com/site/deeplearningcvpr2014/
3. CVPR 2015 Applied Deep Learning for Computer Vision with Torchhttps://github.com/soumith/cvpr2015
4. Deep Learning for Computer Vision – Introduction to Convolution Neural Networkshttp://www.analyticsvidhya.com/blog/2016/04/deep-learning-computer-vision-introduction-convolution-neural-networks/
5. A Beginner's Guide To Understanding Convolutional Neural Networks [https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/]
6. CVPR'17 Tutorial Deep Learning for Objects and Scenes by Kaiming He Ross Girshickhttp://deeplearning.csail.mit.edu/
7. CVPR tutorial : Large-Scale Visual Recognitionhttp://www.europe.naverlabs.com/Research/Computer-Vision/Highlights/CVPR-tutorial-Large-Scale-Visual-Recognition
8. CVPR’16 Tutorial on Image Tag Assignment, Refinement and Retrievalhttp://www.lambertoballan.net/2016/06/cvpr16-tutorial-image-tag-assignment-refinement-and-retrieval/
9. Tutorial on Answering Questions about Images with Deep Learning The tutorial was presented at '2nd Summer School on Integrating Vision and Language: Deep Learning' in Malta, 2016 [https://arxiv.org/abs/1610.01076]
10. “Semantic Segmentation for Scene Understanding: Algorithms and Implementations" tutorial [https://www.youtube.com/watch?v=pQ318oCGJGY]
11. A tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the "echo state network" approach [http://minds.jacobs-university.de/sites/default/files/uploads/papers/ESNTutorialRev.pdf] [http://deeplearning.cs.cmu.edu/notes/shaoweiwang.pdf]
12. Towards Good Practices for Recognition & Detection by Hikvision Research Institute. Supervised Data Augmentation (SDA) [http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf]
13. Generative Adversarial Networks by Ian Goodfellow, NIPS 2016 tutorial [https://arxiv.org/abs/1701.00160] [http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf]
14. Deep Learning for Computer Vision – Introduction to Convolution Neural Networks [http://www.analyticsvidhya.com/blog/2016/04/deep-learning-computer-vision-introduction-convolution-neural-networks/]

圖書

0. 兩本經典教材《Computer Vision: A Modern Approach》和《Computer Vision: Algorithms and Applications》，可以先讀完第一本再讀第二本。
1. Computer Vision: A Modern Approach by David A. Forsyth, Jean Ponce 英文：http://cmuems.com/excap/readings/forsyth-ponce-computer-vision-a-modern-approach.pdf中文：https://pan.baidu.com/s/1min99eK
2. Computer Vision: Algorithms and Applications by Richard Szeliski 英文：http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf中文：https://pan.baidu.com/s/1mhYGtio
3. Computer Vision: Models, Learning, and Inference by Simon J.D. Prince 書的主頁上還有配套的Slider, 代碼，tutorial，演示等各種資源。http://www.computervisionmodels.com/

相關期刊與會議

國際會議

1. CVPR, Computer Vision and Pattern Recognition
   CVPR 2017：http://cvpr2017.thecvf.com/
2. ICCV, International Conference on Computer Vision
   ICCV2017：http://iccv2017.thecvf.com/
3. ECCV, European Conference on Computer Vision
4. SIGGRAPH, Special Interest Group on Computer Graphics and Interactive techniques
   SIGGRAPH2017http://s2017.siggraph.org/
5. ACM International Conference on Multimedia
   ACMMM2017：http://www.acmmm.org/2017/
6. ICIP, International Conference on Image Processinghttp://2017.ieeeicip.org/

期刊

1. ACM Transactions on Graphics
2. IEEE Communications Surveys and Tutorials
3. IEEE Signal Processing Magazine
4. IEEE Transactions on EVOLUTIONARY COMPUTATION
5. IEEE Transactions on GEOSCIENCE and REMOTE SENSING 2區
6. IEEE Transactions on Pattern Analysis and Machine Intelligence
7. NEUROCOMPUTING 2區
8. Pattern Recognition Letters 2區
9. Proceedings of the IEEE
10. Signal image and Video Processing 4區
11. IEEE journal on Selected areas in Communications 2區
12. IEEE Transactions on image Processing 2區
13. journal of Visual Communication and image Representation 3區
14. Machine Vision and Application 3區
15. Pattern Recognition 2區
16. Signal Processing-image Communication 3區
17. COMPUTER Vision and image UNDERSTANDING 3區
18. IEEE Communications Surveys and Tutorials
19. IET image Processing 4區
20. Artificial Intelligence 2區
21. Machine Learning 3區
22. Medical image Analysis 2區

領域專家

北美

http://www.ics.uci.edu/~yyang8/extra/cv_giants.html

Other schools in no particular order:

• UCB (Malik, Darrel, Efros)
• UMD (Davis, Chellappa, Jacobs, Aloimonos, Doermann)
• UIUC (Forsyth, Hoiem, Ahuja, Lazebnik)
• UCSD (Kriegman)
• UT-Austin (Aggarwal, Grauman)
• Stanford (Fei-Fei Li, Savarese)
• USC (Nevatia, Medioni)
• Brown (Felzenszwalb, Hays, Sudderth)
• NYU (Rob Fergus)
• UC-Irvine (Fowlkes)
• UNC (Tamara Berg, Alex Berg, Jan-Michael Frahm)
• Columbia (Belhumeur, Shree Nayar, Shih-Fu Chang)
• Washington (Seitz, Farhadi)
• UMass, Amherst (Learned-Miller, Maji)
• Cornell Tech (Belongie)
• Virgina Tech (Batra, Parikh)
• Princeton (Xiao)
• Caltech (Perona)

http://www.it610.com/article/1770772.htm

組織機構與科研院所 THE Computer Vision Foundation 計算視覺基金會 →http://www.cv-foundation.org/
(a non-profit organization that fosters and supports research in all aspects of computer vision) Stanford Vision Lab 斯坦福大學視覺實驗室 →http://vision.stanford.edu/
It focuses on two intimately connected branches of vision research: computer vision and human vision UC Berkeley Computer Vision Group →https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/Face Recognition Homepage →http://www.face-rec.org/USC Computer Vision 南加州大學計算機視覺實驗室 →http://iris.usc.edu/USC-Computer-Vision.html
The Computer Vision Laboratory at the University of Southern California ibug Intelligent behaviour understanding group → https://ibug.doc.ic.ac.uk/ The core expertise of the iBUG group is the machine analysis of human behaviour in space and time including face analysis, body gesture analysis, visual, audio, and multimodal analysis of human behaviour, and biometrics analysis.

datasets

Detection

1. PASCAL VOC 2009 dataset Classification/Detection Competitions, Segmentation Competition, Person Layout Taster Competition datasets
2. LabelMe dataset LabelMe is a web-based image annotation tool that allows researchers to label images and share the annotations with the rest of the community. If you use the database, we only ask that you contribute to it, from time to time, by using the labeling tool.
3. BioID Face Detection Database
4. 1521 images with human faces, recorded under natural conditions, i.e. varying illumination and complex background. The eye positions have been set manually.
5. CMU/VASC & PIE Face dataset
6. Yale Face dataset
7. Caltech Cars, Motorcycles, Airplanes, Faces, Leaves, Backgrounds
8. Caltech 101 Pictures of objects belonging to 101 categories
9. Caltech 256 Pictures of objects belonging to 256 categories
10. Daimler Pedestrian Detection Benchmark 15,560 pedestrian and non-pedestrian samples (image cut-outs) and 6744 additional full images not containing pedestrians for bootstrapping. The test set contains more than 21,790 images with 56,492 pedestrian labels (fully visible or partially occluded), captured from a vehicle in urban traffic.
11. MIT Pedestrian dataset CVC Pedestrian Datasets
12. CVC Pedestrian Datasets CBCL Pedestrian Database
13. MIT Face dataset CBCL Face Database
14. MIT Car dataset CBCL Car Database
15. MIT Street dataset CBCL Street Database
16. INRIA Person Data Set A large set of marked up images of standing or walking people
17. INRIA car dataset A set of car and non-car images taken in a parking lot nearby INRIA
18. INRIA horse dataset A set of horse and non-horse images
19. H3D Dataset 3D skeletons and segmented regions for 1000 people in images
20. HRI RoadTraffic dataset A large-scale vehicle detection dataset
21. BelgaLogos 10000 images of natural scenes, with 37 different logos, and 2695 logos instances, annotated with a bounding box.
22. FlickrBelgaLogos 10000 images of natural scenes grabbed on Flickr, with 2695 logos instances cut and pasted from the BelgaLogos dataset.
23. FlickrLogos-32 The dataset FlickrLogos-32 contains photos depicting logos and is meant for the evaluation of multi-class logo detection/recognition as well as logo retrieval methods on real-world images. It consists of 8240 images downloaded from Flickr.
24. TME Motorway Dataset 30000+ frames with vehicle rear annotation and classification (car and trucks) on motorway/highway sequences. Annotation semi-automatically generated using laser-scanner data. Distance estimation and consistent target ID over time available.
25. PHOS (Color Image Database for illumination invariant feature selection) Phos is a color image database of 15 scenes captured under different illumination conditions. More particularly, every scene of the database contains 15 different images: 9 images captured under various strengths of uniform illumination, and 6 images under different degrees of non-uniform illumination. The images contain objects of different shape, color and texture and can be used for illumination invariant feature detection and selection.
26. CaliforniaND: An Annotated Dataset For Near-Duplicate Detection In Personal Photo Collections California-ND contains 701 photos taken directly from a real user's personal photo collection, including many challenging non-identical near-duplicate cases, without the use of artificial image transformations. The dataset is annotated by 10 different subjects, including the photographer, regarding near duplicates.
27. USPTO Algorithm Challenge, Detecting Figures and Part Labels in Patents Contains drawing pages from US patents with manually labeled figure and part labels.
28. Abnormal Objects Dataset Contains 6 object categories similar to object categories in Pascal VOC that are suitable for studying the abnormalities stemming from objects.
29. Human detection and tracking using RGB-D camera Collected in a clothing store. Captured with Kinect (640*480, about 30fps)
30. Multi-Task Facial Landmark (MTFL) dataset This dataset contains 12,995 face images collected from the Internet. The images are annotated with (1) five facial landmarks, (2) attributes of gender, smiling, wearing glasses, and head pose.
31. WIDER FACE: A Face Detection Benchmark WIDER FACE dataset is a face detection benchmark dataset with images selected from the publicly available WIDER dataset. It contains 32,203 images and 393,703 face annotations.
32. PIROPO Database: People in Indoor ROoms with Perspective and Omnidirectional cameras Multiple sequences recorded in two different indoor rooms, using both omnidirectional and perspective cameras, containing people in a variety of situations (people walking, standing, and sitting). Both annotated and non-annotated sequences are provided, where ground truth is point-based. In total, more than 100,000 annotated frames are available.

Classification

1. PASCAL VOC 2009 dataset Classification/Detection Competitions, Segmentation Competition, Person Layout Taster Competition datasets
2. Caltech Cars, Motorcycles, Airplanes, Faces, Leaves, Backgrounds
3. Caltech 101 Pictures of objects belonging to 101 categories
4. Caltech 256 Pictures of objects belonging to 256 categories
5. ETHZ Shape Classes A dataset for testing object class detection algorithms. It contains 255 test images and features five diverse shape-based classes (apple logos, bottles, giraffes, mugs, and swans).
6. Flower classification data sets 17 Flower Category Dataset
7. Animals with attributes A dataset for Attribute Based Classification. It consists of 30475 images of 50 animals classes with six pre-extracted feature representations for each image.
8. Stanford Dogs Dataset Dataset of 20,580 images of 120 dog breeds with bounding-box annotation, for fine-grained image categorization.
9. Video classification USAA dataset The USAA dataset includes 8 different semantic class videos which are home videos of social occassions which feature activities of group of people. It contains around 100 videos for training and testing respectively. Each video is labeled by 69 attributes. The 69 attributes can be broken down into five broad classes: actions, objects, scenes, sounds, and camera movement.
10. McGill Real-World Face Video Database This database contains 18000 video frames of 640x480 resolution from 60 video sequences, each of which recorded from a different subject (31 female and 29 male).
11. e-Lab Video Data Set Video data sets to train machines to recognise objects in our environment. e-VDS35 has 35 classes and a total of 2050 videos of roughly 10 seconds each.

Recognition

1. Face and Gesture Recognition Working Group FGnet Face and Gesture Recognition Working Group FGnet
2. Feret Face and Gesture Recognition Working Group FGnet
3. PUT face 9971 images of 100 people
4. Labeled Faces in the Wild A database of face photographs designed for studying the problem of unconstrained face recognition
5. Urban scene recognition Traffic Lights Recognition, Lara's public benchmarks.
6. PubFig: Public Figures Face Database The PubFig database is a large, real-world face dataset consisting of 58,797 images of 200 people collected from the internet. Unlike most other existing face datasets, these images are taken in completely uncontrolled situations with non-cooperative subjects.
7. YouTube Faces The data set contains 3,425 videos of 1,595 different people. The shortest clip duration is 48 frames, the longest clip is 6,070 frames, and the average length of a video clip is 181.3 frames.
8. MSRC-12: Kinect gesture data set The Microsoft Research Cambridge-12 Kinect gesture data set consists of sequences of human movements, represented as body-part locations, and the associated gesture to be recognized by the system.
9. QMUL underGround Re-IDentification (GRID) Dataset This dataset contains 250 pedestrian image pairs + 775 additional images captured in a busy underground station for the research on person re-identification.
10. Person identification in TV series Face tracks, features and shot boundaries from our latest CVPR 2013 paper. It is obtained from 6 episodes of Buffy the Vampire Slayer and 6 episodes of Big Bang Theory.
11. ChokePoint Dataset ChokePoint is a video dataset designed for experiments in person identification/verification under real-world surveillance conditions. The dataset consists of 25 subjects (19 male and 6 female) in portal 1 and 29 subjects (23 male and 6 female) in portal 2.
12. Hieroglyph Dataset Ancient Egyptian Hieroglyph Dataset.
13. Rijksmuseum Challenge Dataset: Visual Recognition for Art Dataset Over 110,000 photographic reproductions of the artworks exhibited in the Rijksmuseum (Amsterdam, the Netherlands). Offers four automatic visual recognition challenges consisting of predicting the artist, type, material and creation year. Includes a set of baseline features, and offer a baseline based on state-of-the-art image features encoded with the Fisher vector.
14. The OU-ISIR Gait Database, Treadmill Dataset Treadmill gait datasets composed of 34 subjects with 9 speed variations, 68 subjects with 68 subjects, and 185 subjects with various degrees of gait fluctuations.
15. The OU-ISIR Gait Database, Large Population Dataset Large population gait datasets composed of 4,016 subjects.
16. Pedestrian Attribute Recognition At Far Distance Large-scale PEdesTrian Attribute (PETA) dataset, covering more than 60 attributes (e.g. gender, age range, hair style, casual/formal) on 19000 images.
17. FaceScrub Face Dataset The FaceScrub dataset is a real-world face dataset comprising 107,818 face images of 530 male and female celebrities detected in images retrieved from the Internet. The images are taken under real-world situations (uncontrolled conditions). Name and gender annotations of the faces are included.
18. Depth-Based Person Identification Depth-Based Person Identification from Top View Dataset.

Tracking

• Dataset-AMP:Luka Čehovin Zajc; Alan Lukežič; Aleš Leonardis; Matej Kristan. "Beyond Standard Benchmarks: Parameterizing Performance Evaluation in Visual Object Tracking." ICCV (2017). [paper]

• Dataset-Nfs:Hamed Kiani Galoogahi, Ashton Fagg, Chen Huang, Deva Ramanan and Simon Lucey. "Need for Speed: A Benchmark for Higher Frame Rate Object Tracking." ICCV (2017) [paper] [supp] [project]

• Dataset-DTB70:Siyi Li, Dit-Yan Yeung. "Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models." AAAI (2017) [paper] [project] [dataset]

• Dataset-UAV123:Matthias Mueller, Neil Smith and Bernard Ghanem. "A Benchmark and Simulator for UAV Tracking." ECCV (2016) [paper] [project] [dataset]

• Dataset-TColor-128:Pengpeng Liang, Erik Blasch, Haibin Ling. "Encoding color information for visual tracking: Algorithms and benchmark." TIP (2015) [paper] [project] [dataset]

• Dataset-NUS-PRO:Annan Li, Min Lin, Yi Wu, Ming-Hsuan Yang, and Shuicheng Yan. "NUS-PRO: A New Visual Tracking Challenge." PAMI (2015) [paper] [project] [Data_360(code:bf28)][Data_baidu]] [View_360(code:515a)][View_baidu]]

• Dataset-PTB:Shuran Song and Jianxiong Xiao. "Tracking Revisited using RGBD Camera: Unified Benchmark and Baselines." ICCV (2013) [paper] [project] [5 validation] [95 evaluation]

• Dataset-ALOV300+:Arnold W. M. Smeulders, Dung M. Chu, Rita Cucchiara, Simone Calderara, Afshin Dehghan, Mubarak Shah. "Visual Tracking: An Experimental Survey." PAMI (2014) [paper] [project]Mirror Link:ALOV300Mirror Link:ALOV300

• OTB2013:Wu, Yi, Jongwoo Lim, and Minghsuan Yang. "Online Object Tracking: A Benchmark." CVPR (2013). [paper]

• OTB2015:Wu, Yi, Jongwoo Lim, and Minghsuan Yang. "Object Tracking Benchmark." TPAMI (2015). [paper] [project]

• Dataset-VOT:[project]

[VOT13_paper_ICCV]The Visual Object Tracking VOT2013 challenge results

[VOT14_paper_ECCV]The Visual Object Tracking VOT2014 challenge results

[VOT15_paper_ICCV]The Visual Object Tracking VOT2015 challenge results

[VOT16_paper_ECCV]The Visual Object Tracking VOT2016 challenge results

[VOT17_paper_ECCV]The Visual Object Tracking VOT2017 challenge results

Segmentation

1. Image Segmentation with A Bounding Box Prior dataset Ground truth database of 50 images with: Data, Segmentation, Labelling - Lasso, Labelling - Rectangle
2. PASCAL VOC 2009 dataset Classification/Detection Competitions, Segmentation Competition, Person Layout Taster Competition datasets
3. Motion Segmentation and OBJCUT data Cows for object segmentation, Five video sequences for motion segmentation
4. Geometric Context Dataset Geometric Context Dataset: pixel labels for seven geometric classes for 300 images
5. Crowd Segmentation Dataset This dataset contains videos of crowds and other high density moving objects. The videos are collected mainly from the BBC Motion Gallery and Getty Images website. The videos are shared only for the research purposes. Please consult the terms and conditions of use of these videos from the respective websites.
6. CMU-Cornell iCoseg Dataset Contains hand-labelled pixel annotations for 38 groups of images, each group containing a common foreground. Approximately 17 images per group, 643 images total.
7. Segmentation evaluation database 200 gray level images along with ground truth segmentations
8. The Berkeley Segmentation Dataset and Benchmark Image segmentation and boundary detection. Grayscale and color segmentations for 300 images, the images are divided into a training set of 200 images, and a test set of 100 images.
9. Weizmann horses 328 side-view color images of horses that were manually segmented. The images were randomly collected from the WWW.
10. Saliency-based video segmentation with sequentially updated priors 10 videos as inputs, and segmented image sequences as ground-truth
11. Daimler Urban Segmentation Dataset The dataset consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs. 500 frames come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference.
12. DAVIS: Densely Annotated VIdeo Segmentation A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation.

Foreground/Background

1. Wallflower Dataset For evaluating background modelling algorithms
2. Foreground/Background Microsoft Cambridge Dataset Foreground/Background segmentation and Stereo dataset from Microsoft Cambridge
3. Stuttgart Artificial Background Subtraction Dataset The SABS (Stuttgart Artificial Background Subtraction) dataset is an artificial dataset for pixel-wise evaluation of background models.
4. Image Alpha Matting Dataset Image Alpha Matting Dataset.
5. LASIESTA: Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms LASIESTA is composed by many real indoor and outdoor sequences organized in diferent categories, each of one covering a specific challenge in moving object detection strategies.

Saliency Detection (source)

1. AIM 120 Images / 20 Observers (Neil D. B. Bruce and John K. Tsotsos 2005).
2. LeMeur 27 Images / 40 Observers (O. Le Meur, P. Le Callet, D. Barba and D. Thoreau 2006).
3. Kootstra 100 Images / 31 Observers (Kootstra, G., Nederveen, A. and de Boer, B. 2008).
4. DOVES 101 Images / 29 Observers (van der Linde, I., Rajashekar, U., Bovik, A.C., Cormack, L.K. 2009).
5. Ehinger 912 Images / 14 Observers (Krista A. Ehinger, Barbara Hidalgo-Sotelo, Antonio Torralba and Aude Oliva 2009).
6. NUSEF 758 Images / 75 Observers (R. Subramanian, H. Katti, N. Sebe1, M. Kankanhalli and T-S. Chua 2010).
7. JianLi 235 Images / 19 Observers (Jian Li, Martin D. Levine, Xiangjing An and Hangen He 2011).
8. Extended Complex Scene Saliency Dataset (ECSSD) ECSSD contains 1000 natural images with complex foreground or background. For each image, the ground truth mask of salient object(s) is provided.

Video Surveillance

1. CAVIAR For the CAVIAR project a number of video clips were recorded acting out the different scenarios of interest. These include people walking alone, meeting with others, window shopping, entering and exitting shops, fighting and passing out and last, but not least, leaving a package in a public place.
2. ViSOR ViSOR contains a large set of multimedia data and the corresponding annotations.
3. CUHK Crowd Dataset 474 video clips from 215 crowded scenes, with ground truth on group detection and video classes.?
4. TImes Square Intersection (TISI) Dataset A busy outdoor dataset for research on visual surveillance.
5. Educational Resource Centre (ERCe) Dataset An indoor dataset collected from a university campus for physical event understanding of long video streams.
6. PIROPO Database: People in Indoor ROoms with Perspective and Omnidirectional cameras Multiple sequences recorded in two different indoor rooms, using both omnidirectional and perspective cameras, containing people in a variety of situations (people walking, standing, and sitting). Both annotated and non-annotated sequences are provided, where ground truth is point-based. In total, more than 100,000 annotated frames are available.

Multiview

1. 3D Photography Dataset Multiview stereo data sets: a set of images
2. Multi-view Visual Geometry group's data set Dinosaur, Model House, Corridor, Aerial views, Valbonne Church, Raglan Castle, Kapel sequence
3. Oxford reconstruction data set (building reconstruction) Oxford colleges
4. Multi-View Stereo dataset (Vision Middlebury) Temple, Dino
5. Multi-View Stereo for Community Photo Collections Venus de Milo, Duomo in Pisa, Notre Dame de Paris
6. IS-3D Data Dataset provided by Center for Machine Perception
7. CVLab dataset CVLab dense multi-view stereo image database
8. 3D Objects on Turntable Objects viewed from 144 calibrated viewpoints under 3 different lighting conditions
9. Object Recognition in Probabilistic 3D Scenes Images from 19 sites collected from a helicopter flying around Providence, RI. USA. The imagery contains approximately a full circle around each site.
10. Multiple cameras fall dataset 24 scenarios recorded with 8 IP video cameras. The first 22 first scenarios contain a fall and confounding events, the last 2 ones contain only confounding events.
11. CMP Extreme View Dataset 15 wide baseline stereo image pairs with large viewpoint change, provided ground truth homographies.
12. KTH Multiview Football Dataset II This dataset consists of 8000+ images of professional footballers during a match of the Allsvenskan league. It consists of two parts: one with ground truth pose in 2D and one with ground truth pose in both 2D and 3D.
13. Disney Research light field datasets This dataset includes: camera calibration information, raw input images we have captured, radially undistorted, rectified, and cropped images, depth maps resulting from our reconstruction and propagation algorithm, depth maps computed at each available view by the reconstruction algorithm without the propagation applied.
14. CMU Panoptic Studio Dataset Multiple people social interaction dataset captured by 500+ synchronized video cameras, with 3D full body skeletons and calibration data.
15. 4D Light Field Dataset 24 synthetic scenes. Available data per scene: 9x9 input images (512x512x3) , ground truth (disparity and depth), camera parameters, disparity ranges, evaluation masks.

Action

1. UCF Sports Action Dataset This dataset consists of a set of actions collected from various sports which are typically featured on broadcast television channels such as the BBC and ESPN. The video sequences were obtained from a wide range of stock footage websites including BBC Motion gallery, and GettyImages.
2. UCF Aerial Action Dataset This dataset features video sequences that were obtained using a R/C-controlled blimp equipped with an HD camera mounted on a gimbal.The collection represents a diverse pool of actions featured at different heights and aerial viewpoints. Multiple instances of each action were recorded at different flying altitudes which ranged from 400-450 feet and were performed by different actors.
3. UCF YouTube Action Dataset It contains 11 action categories collected from YouTube.
4. Weizmann action recognition Walk, Run, Jump, Gallop sideways, Bend, One-hand wave, Two-hands wave, Jump in place, Jumping Jack, Skip.
5. UCF50 UCF50 is an action recognition dataset with 50 action categories, consisting of realistic videos taken from YouTube.
6. ASLAN The Action Similarity Labeling (ASLAN) Challenge.
7. MSR Action Recognition Datasets The dataset was captured by a Kinect device. There are 12 dynamic American Sign Language (ASL) gestures, and 10 people. Each person performs each gesture 2-3 times.
8. KTH Recognition of human actions Contains six types of human actions (walking, jogging, running, boxing, hand waving and hand clapping) performed several times by 25 subjects in four different scenarios: outdoors, outdoors with scale variation, outdoors with different clothes and indoors.
9. Hollywood-2 Human Actions and Scenes dataset Hollywood-2 datset contains 12 classes of human actions and 10 classes of scenes distributed over 3669 video clips and approximately 20.1 hours of video in total.
10. Collective Activity Dataset This dataset contains 5 different collective activities : crossing, walking, waiting, talking, and queueing and 44 short video sequences some of which were recorded by consumer hand-held digital camera with varying view point.
11. Olympic Sports Dataset The Olympic Sports Dataset contains YouTube videos of athletes practicing different sports.
12. SDHA 2010 Surveillance-type videos
13. VIRAT Video Dataset The dataset is designed to be realistic, natural and challenging for video surveillance domains in terms of its resolution, background clutter, diversity in scenes, and human activity/event categories than existing action recognition datasets.
14. HMDB: A Large Video Database for Human Motion Recognition Collected from various sources, mostly from movies, and a small proportion from public databases, YouTube and Google videos. The dataset contains 6849 clips divided into 51 action categories, each containing a minimum of 101 clips.
15. Stanford 40 Actions Dataset Dataset of 9,532 images of humans performing 40 different actions, annotated with bounding-boxes.
16. 50Salads dataset Fully annotated dataset of RGB-D video data and data from accelerometers attached to kitchen objects capturing 25 people preparing two mixed salads each (4.5h of annotated data). Annotated activities correspond to steps in the recipe and include phase (pre-/ core-/ post) and the ingredient acted upon.
17. Penn Sports Action The dataset contains 2326 video sequences of 15 different sport actions and human body joint annotations for all sequences.
18. CVRR-HANDS 3D A Kinect dataset for hand detection in naturalistic driving settings as well as a challenging 19 dynamic hand gesture recognition dataset for human machine interfaces.
19. TUM Kitchen Data Set Observations of several subjects setting a table in different ways. Contains videos, motion capture data, RFID tag readings,...
20. TUM Breakfast Actions Dataset
21. This dataset comprises of 10 actions related to breakfast preparation, performed by 52 different individuals in 18 different kitchens.
22. MPII Cooking Activities Dataset Cooking Activities dataset.
23. GTEA Gaze+ Dataset This dataset consists of seven meal-preparation activities, each performed by 10 subjects. Subjects perform the activities based on the given cooking recipes.
24. UTD-MHAD: multimodal human action recogniton dataset The dataset consists of four temporally synchronized data modalities. These modalities include RGB videos, depth videos, skeleton positions, and inertial signals (3-axis acceleration and 3-axis angular velocity) from a Kinect RGB-D camera and a wearable inertial sensor for a comprehensive set of 27 human actions.

Human pose/Expression

1. AFEW (Acted Facial Expressions In The Wild)/SFEW (Static Facial Expressions In The Wild) Dynamic temporal facial expressions data corpus consisting of close to real world environment extracted from movies.
2. Expression in-the-Wild (ExpW) Dataset Contains 91,793 faces manually labeled with expressions. Each of the face images was manually annotated as one of the seven basic expression categories: “angry”, “disgust”, “fear”, “happy”, “sad”, “surprise”, or “neutral”.
3. ETHZ CALVIN Dataset CALVIN research group datasets
4. HandNet (annotated depth images of articulating hands) This dataset includes 214971 annotated depth images of hands captured by a RealSense RGBD sensor of hand poses. Annotations: per pixel classes, 6D fingertip pose, heatmap. Images -> Train: 202198, Test: 10000, Validation: 2773. Recorded at GIP Lab, Technion.
5. 3D Human Pose Estimation Depth videos + ground truth human poses from 2 viewpoints to improve 3D human pose estimation.

Medical

1. VIP Laparoscopic / Endoscopic Dataset Collection of endoscopic and laparoscopic (mono/stereo) videos and images
2. Mouse Embryo Tracking Database DB Contains 100 examples with the uncompressed frames, up to the 10th frame after the appearance of the 8th cell; a text file with the trajectories of all the cells, from appearance to division; a movie file showing the trajectories of the cells.
3. FIRE Fundus Image Registration Dataset 134 retinal image pairs and ground truth for registration.

Misc

1. Zurich Buildings Database ZuBuD Image Database contains over 1005 images about Zurich city building.
2. Color Name Data Sets
3. Mall dataset The mall dataset was collected from a publicly accessible webcam for crowd counting and activity profiling research.
4. QMUL Junction Dataset A busy traffic dataset for research on activity analysis and behaviour understanding.
5. Miracl-VC1 Miracl-VC1 is a lip-reading dataset including both depth and color images. Fifteen speakers positioned in the frustum of a MS Kinect sensor and utter ten times a set of ten words and ten phrases.
6. NYU Symmetry Database The mirror symmetry database contains 176 single-symmetry and 63 multiple-symmetry images (.png files) with accompanying ground-truth annotations (.mat files).
7. RGB-W: When Vision Meets Wireless Data with the wireless signal emitted by individuals' cell phones, referred to as RGB-W.

Challenge

1. Microsoft COCO Image Captioning Challengehttps://competitions.codalab.org/competitions/3221
2. ImageNet Large Scale Visual Recognition Challengehttp://www.image-net.org/
3. COCO 2017 Detection Challengehttp://cocodataset.org/#detections-challenge2017
4. Visual Domain Adaptation (VisDA2017) Segmentation Challengehttps://competitions.codalab.org/competitions/17054
5. The PASCAL Visual Object Classes Homepagehttp://host.robots.ox.ac.uk/pascal/VOC/
6. YouTube-8M Large-Scale Video Understandinghttps://research.google.com/youtube8m/workshop.html
7. joint COCO and Places Challengehttps://places-coco2017.github.io/
8. Places Challenge 2017: Deep Scene Understanding is held jointly with COCO Challenge at ICCV'17http://placeschallenge.csail.mit.edu/
9. COCO Challenges.http://cocodataset.org/#home
10. CBCL StreetScenes Challenge Frameworkhttp://cbcl.mit.edu/software-datasets/streetscenes/
11. CoronARe: A Coronary Artery Reconstruction Challengehttps://challenge.kitware.com/#phase/58d2925ecad3a532cfa20e37
12. NUS-PRO: A New Visual Tracking Challengehttp://www.visionbib.com/bibliography/journal/pam.html#PAMI(38)
13. i-LIDS: Bag and vehicle detection challengehttp://www.elec.qmul.ac.uk/staffinfo/andrea/avss2007_d.html
14. The Action Similarity Labeling Challengehttp://www.visionbib.com/bibliography/journal/pam.html#PAMI(34)
15. The PASCAL Visual Object Classes Challenge 2012http://www.visionbib.com/bibliography/journal-listeu.html#TT1663

創業公司

1. 曠視科技：讓機器看懂世界 [https://megvii.com/]
2. 雲從科技：源自計算機視覺之父的人臉識別技術 [http://www.cloudwalk.cn/]
3. 格林深瞳：讓計算機看懂世界 [http://www.deepglint.com/]
4. 北京陌上花科技有限公司：人工智能計算機視覺引擎 [http://www.dressplus.cn/]
5. 依圖科技：與您一起構建計算機視覺的未來 [http://www.yitutech.com/]
6. 碼隆科技：最時尚的人工智能 [https://www.malong.com/]
7. Linkface臉雲科技：全球領先的人臉識別技術服務 [https://www.linkface.cn/]
8. 速感科技：讓機器人認識世界，用機器人改變世界 [http://www.qfeeltech.com/]
9. 圖森： 中國自動駕駛商業化領跑者 [http://www.tusimple.com/]
10. Sense TIme商湯科技：教會計算機看懂這個世界 [https://www.sensetime.com/]
11. 圖普科技：專注於圖像識別 [https://us.tuputech.com/?from=gz]
12. 亮風台： 專注增強現實，引領人機交互 [https://www.hiscene.com/]
13. 中科視拓 ： 知人識麵辨萬物，開源賦能共發展 [http://www.seetatech.com/]

公眾號

1. 視覺求索 thevisionseeker
2. 深度學習大講堂 deeplearningclass
3. VALSE valse_wechat