Learning to segment object candidates

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 1990-1998

  Pinheiro, Pedro O ^a,b,c   Collobert, Ronan ^a   Dollar, Piotr ^a  

^a FACEBOOK AI RESEARCH   (United States)

^b IDIAP RESEARCH INSTITUTE   (Switzerland)

^c EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION; INFORMATION SCIENCE;

CONVOLUTIONAL NETWORKS; CRITICAL STEPS; IMAGE PATCHES; LIKELIHOOD SCORE; OBJECT DETECTION SYSTEMS; SEGMENTATION MASKS; STATE OF THE ART; SYSTEM OUTPUT;

OBJECT DETECTION;

EID: 84965114050     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. B. Alexe, T. Deselaers, and V. Ferrari. Measuring the objectness of image windows. PAMI, 2012. 2
2. N. Chavali, H. Agrawal, A. Mahendru, and D. Batra. Object-proposal evaluation protocol is 'gameable'. arXiv:1505.05836, 2015. 8
3. L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. ICLR, 2015. 2
4. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005. 1
5. J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 1, 3
6. D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov. Scalable object detection using deep neural networks. In CVPR, 2014. 2
7. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL visual object classes (VOC) challenge. IJCV, 2010. 1, 2, 6
8. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. PAMI, 2010. 1
9. R. Girshick. Fast R-CNN. arXiv:1504.08083, 2015. 2, 8
10. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014. 1, 2, 6
11. B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik. Hypercolumns for object segmentation and finegrained localization. In CVPR, 2015. 2
12. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV, 2014. 2
13. J. Hosang, R. Benenson, P. Dollár, and B. Schiele. What makes for effective detection proposals? arXiv:1502.05082, 2015. 2, 6
14. A. Humayun, F. Li, and J. M. Rehg. RIGOR: Reusing Inference in Graph Cuts for generating Object Regions. In CVPR, 2014. 2, 6, 7, 8
15. H. Kaiming, Z. Xiangyu, R. Shaoqing, and S. Jian. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV, 2014. 1
16. P. Krähenbühl and V. Koltun. Geodesic object proposals. In ECCV, 2014. 2, 6, 7, 8
17. P. Krähenbühl and V. Koltun. Learning to propose objects. In CVPR, 2015. 2
18. A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012. 2
19. W. Kuo, B. Hariharan, and J. Malik. Deepbox: Learning objectness with convolutional networks. In arXiv:505.02146v1, 2015. 2, 8
20. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998. 1
21. T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár. Microsoft COCO: Common objects in context. arXiv:1405.0312, 2015. 1, 2, 5, 6
22. M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Is object localization for free?-Weakly-supervised learning with convolutional neural networks. In CVPR, 2015. 2
23. P. O. Pinheiro and R. Collobert. Recurrent conv. neural networks for scene labeling. In ICML, 2014. 2
24. J. Pont-Tuset, P. Arbeláez, J. Barron, F. Marques, and J. Malik. Multiscale combinatorial grouping for image segmentation and object proposal generation. In arXiv:1503.00848, 2015. 2, 6, 7, 8
25. S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In arXiv:1506.01497, 2015. 2
26. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. In ICLR, 2014. 5
27. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015. 2, 3, 5
28. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. JMLR, 2014. 5
29. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, 2015. 2
30. C. Szegedy, S. Reed, D. Erhan, and D. Anguelov. Scalable, high-quality object detection. In arXiv:1412.1441, 2014. 2
31. J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders. Selective search for object recog. IJCV, 2013. 2, 6, 7, 8
32. P. Viola and M. J. Jones. Robust real-time face detection. IJCV, 2004. 1
33. Z. Y. Zhu, R. Urtasun, R. Salakhutdinov, and S. Fidler. segdeepm: Exploiting segmentation and context in deep neural networks for object detection. In CVPR, 2015. 1
34. C. L. Zitnick and P. Dollár. Edge boxes: Locating object proposals from edges. In ECCV, 2014. 2, 6, 7, 8