DeepBox: Learning objectness with convolutional networks

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 2479-2487

  Kuo, Weicheng ^a   Hariharan, Bharath ^a   Malik, Jitendra ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTION; NEURAL NETWORKS; SEMANTICS;

BOTTOM UP; BOTTOM UP CUES; BOTTOM UP METHODS; CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; DATA DRIVEN; LARGER NETWORKS; SEMANTIC APPROACH;

COMPUTER VISION;

EID: 84973861966     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.285     Document Type: Conference Paper

References (30)

1. B. Alexe, T. Deselaers, and V. Ferrari. Measuring the objectness of image windows. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34 (11): 2189-2202, 2012. 1, 2, 6
2. P. Arbeláez, J. Pont-Tuset, J. Barron, F. Marques, and J. Malik. Multiscale combinatorial grouping. In CVPR, 2014. 2, 3, 6, 8
3. J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu. Semantic segmentation with second-order pooling. In ECCV, 2012. 6
4. J. Carreira and C. Sminchisescu. Cpmc: Automatic object segmentation using constrained parametric min-cuts. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34 (7): 1312-1328, 2012. 2
5. M. Cheng, Z. Zhang, W. Lin, and P. Torr. Bing: Binarized normed gradients for objectness estimation at 300fps. In CVPR, 2014. 6
6. I. Endres and D. Hoiem. Category independent object proposals. In Computer Vision-ECCV 2010, pages 575-588. Springer, 2010. 2
7. D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov. Scalable object detection using deep neural networks. In CVPR, 2014. 2
8. M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88 (2): 303-338, 2010. 1, 4
9. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. TPAMI, 32 (9), 2010. 1
10. R. Girshick. Fast R-CNN. In ICCV, 2015. 1, 2, 3, 8
11. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014. 1, 2
12. B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik. Simultaneous detection and segmentation. In ECCV, 2014. 1
13. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV, 2014. 1, 2, 3
14. J. Hosang, R. Benenson, P. Dollár, and B. Schiele. What makes for effective detection proposals? arXiv preprint arXiv: 1502. 05082, 2015. 1, 2, 8
15. J. Hosang, R. Benenson, and B. Schiele. How good are detection proposals, really? In BMVC, 2014. 2
16. H. Kang, M. Hebert, A. A. Efros, and T. Kanade. Connecting missing links: Object discovery from sparse observations using 5 million product images. In ECCV. 2012. 8
17. P. Krähenbühl and V. Koltun. Geodesic object proposals. In ECCV, 2014. 2, 8
18. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012. 1, 2, 3, 7
19. Y. LeCun, 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, 1 (4): 541-551, 1989. 1
20. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C. L. Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 1, 2, 3, 4
21. S. Manen, M. Guillaumin, and L. Van Gool. Prime object proposals with randomized prim's algorithm. In ICCV, 2013. 6
22. P. O. Pinheiro, R. Collobert, and P. Dollár. Learning to segment object candidates. In NIPS, 2015 (To appear). 2, 8
23. E. Rahtu, K. Juho, and B. Matthew. Learning a category independent object detection cascade. In ICCV, 2011. 6
24. R. E. Rantalankila P., Kannala J. Generating object segmentation proposals using global and local search. In CVPR, 2014. 6
25. S. Ren, K. He, R. B. Girshick, and J. Sun. Faster RCNN: towards real-time object detection with region proposal networks. In NIPS, 2015 (To appear). 2
26. B. C. Russell, W. T. Freeman, A. A. Efros, J. Sivic, and A. Zisserman. Using multiple segmentations to discover objects and their extent in image collections. In CVPR, 2006. 1
27. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409. 1556, 2014. 7
28. C. Szegedy, S. Reed, D. Erhan, and D. Anguelov. Scalable, high-quality object detection. arXiv preprint arXiv: 1412. 1441, 2014. 2
29. J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders. Selective search for object recognition. IJCV, 104 (2), 2013. 2, 3, 6, 8
30. C. L. Zitnick and P. Dollár. Edge boxes: Locating object proposals from edges. In ECCV, 2014. 1, 2, 3, 4, 6, 7, 8