全球最全计算机视觉资料（2：分类与检测）

目标检测和深度学习

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/very_deep/] [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

1. Deep Neural Networks for Object Detection （基于DNN的对象检测）NIPS2013:
   • [https://cis.temple.edu/~yuhong/teach/2014_spring/papers/NIPS2013_DNN_OD.pdf]
2. R-CNN Rich 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-CNN Faster 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-Net Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
   • [http://arxiv.org/abs/1406.4729]
8. DeepID-Net DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
   • [https://arxiv.org/abs/1412.5661]
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]
31. Mask-RCNN 2017 ICCV 2017 Best paper award. Facebook AI Research - [https://arxiv.org/pdf/1703.06870.pdf]

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