Learning to Co-Generate Object Proposals with a Deep Structured Network

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 2565-2573

  Hayder, Zeeshan ^a,b   He, Xuming ^a,b   Salzmann, Mathieu ^a,b  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; IMAGE SEGMENTATION; INFERENCE ENGINES; LEARNING ALGORITHMS; NEURAL NETWORKS; RANDOM PROCESSES;

BENCHMARK DATASETS; CONDITIONAL RANDOM FIELD; CONVOLUTIONAL NEURAL NETWORK; INDIVIDUAL OBJECTS; MULTIPLE OBJECTS; PROPOSAL ALGORITHM; STATE OF THE ART; STRUCTURED NETWORKS;

PATTERN RECOGNITION;

EID: 84986292761     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.281     Document Type: Conference Paper

References (25)

1. B. Alexe, T. Deselaers, and V. Ferrari. What is an object? In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
2. S. Bao, Y. Xiang, and S. Savarese. Object co-detection. In The European Conference on Computer Vision (ECCV), 2012.
3. L. Chen, A. G. Schwing, A. L. Yuille, and R. Urtasun. Learning deep structured models. In International Conference on Machine Learning (ICML), 2014.
4. M. Cheng, Z. Zhang, W. Lin, and P. H. S. Torr. BING: binarized normed gradients for objectness estimation at 300fps. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
5. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. The International Journal of Computer Vision (IJCV), 2010.
6. R. B. Girshick. Fast R-CNN. In IEEE International Conference on Computer Vision (ICCV), 2015.
7. R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
8. X. Guo, D. Liu, B. Jou, M. Zhu, A. Cai, and S. F. Chang. Robust Object Co-detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
9. B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik. Simultaneous detection and segmentation. In The European Conference on Computer Vision (ECCV). 2014.
10. Z. Hayder, X. He, and M. Salzmann. Structural kernel learning for large scale multiclass object co-detection. In IEEE International Conference on Computer Vision (ICCV), 2015.
11. Z. Hayder, M. Salzmann, and X. He. Object co-detection via efficient inference in a fully-connected crf. In The European Conference on Computer Vision (ECCV). 2014.
12. J. H. Hosang, R. Benenson, P. Dollár, and B. Schiele. What makes for effective detection proposals? IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015.
13. P. Krähenbühl and V. Koltun. Efficient inference in fully connected crfs with Gaussian edge potentials. In The Conference on Neural Information Processing Systems (NIPS), 2011.
14. P. Krähenbühl and V. Koltun. Parameter learning and convergent inference for dense random fields. In International Conference on Machine Learning (ICML), 2013.
15. P. Krähenbühl and V. Koltun. Geodesic object proposals. In The European Conference on Computer Vision (ECCV), 2014.
16. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradientbased learning applied to document recognition. Proceedings of the IEEE, 1998.
17. T. Lin, M. Maire, S. Belongie, L. D. Bourdev, R. B. Girshick, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft COCO: common objects in context. In The European Conference on Computer Vision (ECCV), 2014.
18. P. O. Pinheiro, R. Collobert, and P. Dollár. Learning to segment object candidates. In The Conference on Neural Information Processing Systems (NIPS), 2015.
19. A. G. Schwing and R. Urtasun. Fully connected deep structured networks. CoRR, 2015.
20. J. Shi, R. Liao, and J. Jia. CoDeL: An efficient human codetection and labeling framework. In IEEE International Conference on Computer Vision (ICCV), 2013.
21. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR), 2015.
22. J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders. Selective search for object recognition. The International Journal of Computer Vision (IJCV), 2013.
23. S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. Torr. Conditional random fields as recurrent neural networks. In IEEE International Conference on Computer Vision (ICCV), 2015.
24. S. Zheng, V. A. Prisacariu, M. Averkiou, M.-M. Cheng, N. J. Mitra, J. Shotton, P. H. Torr, and C. Rother. Object proposal estimation in depth images using compact 3d shape manifolds. German Conference on Pattern Recognition (GCPR), 2015.
25. C. L. Zitnick and P. Dollár. Edge boxes: Locating object proposals from edges. In The European Conference on Computer Vision (ECCV), 2014.