Learning deep representation with large-scale attributes

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 1895-1903

  Ouyang, Wanli ^a   Li, Hongyang ^a   Zeng, Xingyu ^a   Wang, Xiaogang ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; OBJECT RECOGNITION;

DEEP LEARNING; DETECTION ACCURACY; FEATURE REPRESENTATION; LARGE-SCALE OBJECTS; OBJECT CLASS; VISUAL DATA;

OBJECT DETECTION;

EID: 84973862714     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.220     Document Type: Conference Paper

References (49)

1. H. Azizpour and I. Laptev. Object detection using stronglysupervised deformable part models. In ECCV, 2012. 2, 3, 4
2. Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE Trans. PAMI, 35(8):1798-1828, 2013. 2
3. C.-Y. Chen and K. Grauman. Inferring analogous attributes. In CVPR, 2014. 3
4. S. K. Divvala, A. A. Efros, and M. Hebert. How important are deformable parts in the deformable parts model? In ECCV Workshops and Demonstrations, 2012. 7
5. C. Dong, C. C. Loy, K. He, and X. Tang. Learning a deep convolutional network for image super-resolution. In ECCV. 2014. 3
6. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. IJCV, 88(2):303-338, 2010. 1
7. H. Fang, S. Gupta, F. Iandola, R. Srivastava, L. Deng, P. Dollár, J. Gao, X. He, M. Mitchell, J. Platt, et al. From captions to visual concepts and back. ArXiv preprint arXiv:1411. 4952, 2014. 1
8. A. Farhadi, I. Endres, and D. Hoiem. Attribute-centric recognition for cross-category generalization. In CVPR, 2010. 2
9. A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth. Describing objects by their attributes. In CVPR, pages 1778-1785. IEEE, 2009. 2, 3
10. P. Felzenszwalb, R. B. Grishick, D. McAllister, and D. Ramanan. Object detection with discriminatively trained part based models. IEEE Trans. PAMI, 32:1627-1645, 2010. 2, 3, 4, 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, 3, 4, 5, 6, 7, 8
12. R. Girshick, P. Felzenszwalb, and D. McAllester. Discriminatively trained deformable part models, release 5. http: //www. cs. berkeley. edu/rbg/latent-v5/. 8
13. R. Girshick, F. Iandola, T. Darrell, and J. Malik. Deformable part models are convolutional neural networks. ArXiv preprint arXiv:1409. 5403, 2014. 8
14. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV. 2014. 3, 6, 8
15. K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. ArXiv preprint arXiv:1502. 01852, 2015. 1
16. S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. ArXiv preprint arXiv:1502. 03167, 2015. 1, 6
17. A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012. 1, 3
18. C. H. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In CVPR, 2009. 2, 3
19. M. Lin, Q. Chen, and S. Yan. Network in network. ArXiv preprint arXiv:1312. 4400, 2013. 3
20. D. Lowe. Distinctive image features from scale-invarian keypoints. IJCV, 60(2):91-110, 2004. 1
21. P. Luo, X. Wang, and X. Tang. Hierarchical face parsing via deep learning. In CVPR, 2012. 3
22. D. Mahajan, S. Sellamanickam, and V. Nair. A joint learning framework for attribute models and object descriptions. In ICCV, 2011. 3
23. W. Ouyang, X. Chu, and X. Wang. Multi-source deep learning for human pose estimation. In CVPR, 2014. 3
24. W. Ouyang, P. Luo, X. Zeng, S. Qiu, Y. Tian, H. Li, S. Yang, Z. Wang, Y. Xiong, C. Qian, et al. Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection. ArXiv preprint arXiv:1409. 3505, 2014. 6, 7
25. W. Ouyang and X. Wang. A discriminative deep model for pedestrian detection with occlusion handling. In CVPR, 2012. 3
26. W. Ouyang and X. Wang. Joint deep learning for pedestrian detection. In ICCV, 2013. 3
27. W. Ouyang, X. Wang, X. Zeng, S. Qiu, P. Luo, Y. Tian, H. Li, S. Yang, Z. Wang, C.-C. Loy, et al. Deepid-net: Deformable deep convolutional neural networks for object detection. In CVPR, 2015. 6
28. W. Ouyang, X. Zeng, and X. Wang. Modeling mutual visibility relationship in pedestrian detection. In CVPR, 2013. 3
29. G. Patterson and J. Hays. Sun attribute database: Discovering, annotating, and recognizing scene attributes. In CVPR, 2012. 1, 2, 3
30. X. Ren and D. Ramanan. Histograms of sparse codes for object detection. In CVPR, 2013. 8
31. 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. IJCV, 2015. 1, 4
32. O. Russakovsky and L. Fei-Fei. Attribute learning in largescale datasets. In Trends and Topics in Computer Vision, pages 1-14. 2012. 2
33. B. C. Russell, A. Torralba, K. P. Murphy, andW. T. Freeman. Labelme: A database and web-based tool for image annotation. IJCV, 77(1-3):157-173, 2008. 1
34. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. ArXiv preprint arXiv:1312. 6229, 2013. 1, 3
35. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. ArXiv preprint arXiv:1409. 1556, 2014. 1
36. A. Smeulders, T. Gevers, N. Sebe, and C. Snoek. Segmentation as selective search for object recognition. In ICCV, 2011. 6
37. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. ArXiv preprint arXiv:1409. 4842, 2014. 1, 6, 7
38. K. E. A. Van De Sande, C. G. M. Snoek, and A. W. M. Smeulders. Fisher and vlad with flair. In CVPR, 2014. 7, 8
39. O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Show and tell: A neural image caption generator. ArXiv preprint arXiv:1411. 4555, 2014. 1
40. X. Wang, M. Yang, S. Zhu, and Y. Lin. Regionlets for generic object detection. In ICCV, 2013. 8
41. Y. Wang and G. Mori. A discriminative latent model of object classes and attributes. In ECCV. 2010. 3
42. J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In CVPR, 2010. 1
43. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional neural networks. ArXiv preprint arXiv:1311. 2901, 2013. 1, 3
44. X. Zeng, W. Ouyang, and X. Wang. Multi-stage contextual deep learning for pedestrian detection. In ICCV, 2013. 3
45. N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev. Panda: Pose aligned networks for deep attribute modeling. CVPR, 2014. 1, 3
46. W. Zhang, X. Wang, D. Zhao, and X. Tang. Graph degree linkage: Agglomerative clustering on a directed graph. In ECCV. 2012. 5, 7
47. R. Zhao, W. Ouyang, H. Li, and X. Wang. Saliency detection by multi-context deep learning. In CVPR, 2015. 3
48. B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Object detectors emerge in deep scene cnns. ArXiv preprint arXiv:1412. 6856, 2014. 1
49. Z. Zhu, P. Luo, X. Wang, and X. Tang. Deep learning identity preserving face space. In ICCV, 2013. 3