k[原创]Faster R-CNN论文翻译
物体检测论文翻译系列:
- R-CNN
- SPP-net
- Fast R-CNN
- Faster R-CNN
Faster R-CNN论文翻译
Faster R-CNN是互怼完了的好基友一起合作出来的巅峰之作,本文翻译的比例比较小,主要因为本paper是前述paper的一个简单改进,方法清晰,想法自然。什么想法?就是把那个一直明明应该换掉却一直被几位大神挤牙膏般地拖着不换的选择性搜索算法,即区域推荐算法。在Fast R-CNN的基础上将区域推荐换成了神经网络,而且这个神经网络和Fast R-CNN的卷积网络一起复用,大大缩短了计算时间。同时mAP又上了一个台阶,我早就说过了,他们一定是在挤牙膏。
Faster R-CNN: Towards Real-Time Object
Detection with Region Proposal Networks
摘要
1. 介绍
2 相关工作
3 FASTER R-CNN
3.1 区域推荐网络
3.1.1 锚点
平移不变性锚点
多尺度锚点作为回归参照物
3.1.2 损失函数
3.1.3 训练RPNs
3.2 RPN and Fast R-CNN之间共享特征
3.3 实现细节
4 EXPERIMENTS
5 CONCLUSION
参考文献
[2] R. Girshick, “Fast R-CNN,” in IEEE International Conference onComputer Vision (ICCV), 2015.
[3] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in InternationalConference on Learning Representations (ICLR), 2015.
[4] J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders, “Selective search for object recognition,” InternationalJournal of Computer Vision (IJCV), 2013.
[5] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich featurehierarchies for accurate object detection and semantic segmentation,” in IEEE Conference on Computer Vision and PatternRecognition (CVPR), 2014.
[6] C. L. Zitnick and P. Dollar, “Edge boxes: Locating object ´proposals from edges,” in European Conference on ComputerVision (ECCV), 2014.
[7] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutionalnetworks for semantic segmentation,” in IEEE Conference onComputer Vision and Pattern Recognition (CVPR), 2015.
[8] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained partbased models,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2010.
[9] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus,and Y. LeCun, “Overfeat: Integrated recognition, localizationand detection using convolutional networks,” in InternationalConference on Learning Representations (ICLR), 2014.
[10] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” inNeural Information Processing Systems (NIPS), 2015.
[11] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, andA. Zisserman, “The PASCAL Visual Object Classes Challenge2007 (VOC2007) Results,” 2007.
[12] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick, “Microsoft COCO: Com- ´mon Objects in Context,” in European Conference on ComputerVision (ECCV), 2014.
[13] S. Song and J. Xiao, “Deep sliding shapes for amodal 3d objectdetection in rgb-d images,” arXiv:1511.02300, 2015.
[14] J. Zhu, X. Chen, and A. L. Yuille, “DeePM: A deep part-basedmodel for object detection and semantic part localization,”arXiv:1511.07131, 2015.
[15] J. Dai, K. He, and J. Sun, “Instance-aware semantic segmentation via multi-task network cascades,” arXiv:1512.04412, 2015.
[16] J. Johnson, A. Karpathy, and L. Fei-Fei, “Densecap: Fullyconvolutional localization networks for dense captioning,”arXiv:1511.07571, 2015.
[17] D. Kislyuk, Y. Liu, D. Liu, E. Tzeng, and Y. Jing, “Human curation and convnets: Powering item-to-item recommendationson pinterest,” arXiv:1511.04003, 2015.
[18] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learningfor image recognition,” arXiv:1512.03385, 2015.
[19] J. Hosang, R. Benenson, and B. Schiele, “How good are detection proposals, really?” in British Machine Vision Conference(BMVC), 2014.
[20] J. Hosang, R. Benenson, P. Dollar, and B. Schiele, “What makes ´for effective detection proposals?” IEEE Transactions on PatternAnalysis and Machine Intelligence (TPAMI), 2015.
[21] N. Chavali, H. Agrawal, A. Mahendru, and D. Batra,“Object-Proposal Evaluation Protocol is ’Gameable’,” arXiv:1505.05836, 2015.
[22] J. Carreira and C. Sminchisescu, “CPMC: Automatic object segmentation using constrained parametric min-cuts,”IEEE Transactions on Pattern Analysis and Machine Intelligence(TPAMI), 2012.
[23] P. Arbelaez, J. Pont-Tuset, J. T. Barron, F. Marques, and J. Malik, ´“Multiscale combinatorial grouping,” in IEEE Conference onComputer Vision and Pattern Recognition (CVPR), 2014.
[24] B. Alexe, T. Deselaers, and V. Ferrari, “Measuring the objectness of image windows,” IEEE Transactions on Pattern Analysisand Machine Intelligence (TPAMI), 2012.
[25] C. Szegedy, A. Toshev, and D. Erhan, “Deep neural networksfor object detection,” in Neural Information Processing Systems(NIPS), 2013.
[26] D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov, “Scalableobject detection using deep neural networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
[27] C. Szegedy, S. Reed, D. Erhan, and D. Anguelov, “Scalable,high-quality object detection,” arXiv:1412.1441 (v1), 2015.
[28] P. O. Pinheiro, R. Collobert, and P. Dollar, “Learning tosegment object candidates,” in Neural Information ProcessingSystems (NIPS), 2015.
[29] J. Dai, K. He, and J. Sun, “Convolutional feature maskingfor joint object and stuff segmentation,” in IEEE Conference onComputer Vision and Pattern Recognition (CVPR), 2015.
[30] S. Ren, K. He, R. Girshick, X. Zhang, and J. Sun, “Object detection networks on convolutional feature maps,”arXiv:1504.06066, 2015.
[31] J. K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, andY. Bengio, “Attention-based models for speech recognition,”in Neural Information Processing Systems (NIPS), 2015.
[32] M. D. Zeiler and R. Fergus, “Visualizing and understandingconvolutional neural networks,” in European Conference onComputer Vision (ECCV), 2014.
[33] V. Nair and G. E. Hinton, “Rectified linear units improverestricted boltzmann machines,” in International Conference onMachine Learning (ICML), 2010.
[34] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov,D. Erhan, and A. Rabinovich, “Going deeper with convolutions,” in IEEE Conference on Computer Vision and PatternRecognition (CVPR), 2015.
[35] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard,W. Hubbard, and L. D. Jackel, “Backpropagation applied tohandwritten zip code recognition,” Neural computation, 1989.
[36] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma,Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg,and L. Fei-Fei, “ImageNet Large Scale Visual RecognitionChallenge,” in International Journal of Computer Vision (IJCV),2015.
[37] A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet classification with deep convolutional neural networks,” in NeuralInformation Processing Systems (NIPS), 2012.
[38] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutionalarchitecture for fast feature embedding,” arXiv:1408.5093, 2014.
[39] K. Lenc and A. Vedaldi, “R-CNN minus R,” in British MachineVision Conference (BMVC), 2015.
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