Faster R-CNN: Towards real-time object detection with region proposal networks

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 91-99

  Ren, Shaoqing ^a,b   He, Kaiming ^a   Girshick, Ross ^a   Sun, Jian ^a  

^a MICROSOFT RESEARCH   (United States)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONVOLUTION; INFORMATION SCIENCE; OBJECT RECOGNITION;

ALTERNATING OPTIMIZATIONS; CONVOLUTIONAL NETWORKS; DETECTION ACCURACY; DETECTION NETWORKS; DETECTION SYSTEM; OBJECT LOCATION; PROPOSAL ALGORITHM; STATE OF THE ART;

OBJECT DETECTION;

EID: 84960980241     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. N. Chavali, H. Agrawal, A. Mahendru, and D. Batra. Object-Proposal Evaluation Protocol is 'Gameable'. arXiv: 1505.05836, 2015.
2. J. Dai, K. He, and J. Sun. Convolutional feature masking for joint object and stuff segmentation. In CVPR, 2015.
3. D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov. Scalable object detection using deep neural networks. In CVPR, 2014.
4. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results, 2007.
5. R. Girshick. Fast R-CNN. arXiv:1504.08083, 2015.
6. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
7. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV. 2014.
8. J. Hosang, R. Benenson, P. Dollár, and B. Schiele. What makes for effective detection proposals? arXiv:1502.05082, 2015.
9. J. Hosang, R. Benenson, and B. Schiele. How good are detection proposals, really? In BMVC, 2014.
10. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv:1408.5093, 2014.
11. A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
12. Y. Le Cun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1989.
13. K. Lenc and A. Vedaldi. R-CNN minus R. arXiv:1506.06981, 2015.
14. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
15. V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, 2010.
16. S. Ren, K. He, R. Girshick, X. Zhang, and J. Sun. Object detection networks on convolutional feature maps. arXiv:1504.06066, 2015.
17. 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 Recognition Challenge. arXiv:1409.0575, 2014.
18. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. Le Cun. Overfeat: Integrated recognition, localization and detection using convolutional networks. In ICLR, 2014.
19. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
20. C. Szegedy, S. Reed, D. Erhan, and D. Anguelov. Scalable, high-quality object detection. arXiv:1412.1441v2, 2015.
21. C. Szegedy, A. Toshev, and D. Erhan. Deep neural networks for object detection. In NIPS, 2013.
22. J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders. Selective search for object recognition. IJCV, 2013.
23. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional neural networks. In ECCV, 2014.
24. C. L. Zitnick and P. Dollár. Edge boxes: Locating object proposals from edges. In ECCV, 2014.