Attribute Recognition by Joint Recurrent Learning of Context and Correlation

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2017-October, Issue , 2017, Pages 531-540

  Wang, Jingya ^a   Zhu, Xiatian ^b   Gong, Shaogang ^a   Li, Wei ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

^b VISION SEMANTICS LTD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPUTER VISION; INFORMATION DISSEMINATION; SEMANTICS;

ATTRIBUTE RECOGNITION; COMPLEX BACKGROUND; HIGH ORDER CORRELATION; IMAGE ANNOTATION; RECURRENT NETWORKS; SEQUENTIAL ORDERING; STATE OF THE ART; VIEWING CONDITIONS;

IMAGE ENHANCEMENT;

EID: 85041926749     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.65     Document Type: Conference Paper

References (52)

1. D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate. In ICLR, 2014
2. Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798-1828, 2013
3. H. Chen, A. Gallagher, and B. Girod. Describing clothing by semantic attributes. In ECCV, 2012
4. K. Cho, A. Courville, and Y. Bengio. Describing multimedia content using attention-based encoder-decoder networks. IEEE Transactions on Multimedia, 17(11):1875-1886, 2015
5. K. Cho, B. Van Merriënboer, D. Bahdanau, and Y. Bengio. On the properties of neural machine translation: Encoderdecoder approaches. ArXiv e-prints, 2014
6. K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In Conference on Empirical Methods in Natural Language Processing, 2014
7. Y. DENG, P. Luo, C. C. Loy, and X. Tang. Pedestrian attribute recognition at far distance. In ACMMM, 2014
8. Y. Deng, P. Luo, C. C. Loy, and X. Tang. Learning to recognize pedestrian attribute. ArXiv e-prints, 2015
9. Q. Dong, S. Gong, and X. Zhu. Class rectification hard mining for imbalanced deep learning. In ICCV, 2017
10. Q. Dong, S. Gong, and X. Zhu. Multi-task curriculum transfer deep learning of clothing attributes. In IEEE Winter Conference on Applications of Computer Vision, 2017
11. G. Gkioxari, R. Girshick, and J. Malik. Contextual action recognition with r∗cnn. In ICCV, 2015
12. D. Gray and H. Tao. Viewpoint invariant pedestrian recognition with an ensemble of localized features. In ECCV, 2008
13. M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid. Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation. In ICCV, 2009
14. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997
15. E. S. Jaha and M. S. Nixon. Soft biometrics for subject identification using clothing attributes. In IEEE International Joint Conference on Biometrics, 2014
16. D. Kingma and J. Ba. Adam: A method for stochastic optimization. In ICLR, 2014
17. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012
18. N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar. Attribute and simile classifiers for face verification. In ICCV, 2009
19. R. Layne, T. M. Hospedales, and S. Gong. Attributes-based re-identification. In Person Re-Identification, pages 93-117. Springer, 2014
20. R. Layne, T. M. Hospedales, S. Gong, et al. Person reidentification by attributes. In BMVC, 2012
21. D. Li, X. Chen, and K. Huang. Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios. In ACPR, 2015
22. D. Li, Z. Zhang, X. Chen, H. Ling, and K. Huang. A richly annotated dataset for pedestrian attribute recognition. ArXiv e-prints, 2016
23. Y. Li, C. Huang, C. C. Loy, and X. Tang. Human attribute recognition by deep hierarchical contexts. In ECCV, 2016
24. Y. Li, G. Lin, B. Zhuang, L. Liu, C. Shen, and A. v. d. Hengel. Sequential person recognition in photo albums with a recurrent network. In CVPR, 2017
25. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014
26. C. Liu, S. Gong, C. Loy, and X. Lin. Person re-identification: What features are important In First International Workshop on Re-Identification, European Conference on Computer Vision, 2012
27. F. Liu, T. Xiang, T. M. Hospedales, W. Yang, and C. Sun. Semantic regularisation for recurrent image annotation. In CVPR, 2017
28. A. Makadia, V. Pavlovic, and S. Kumar. A new baseline for image annotation. In ECCV, 2008
29. K. O. May. A set of independent necessary and sufficient conditions for simple majority decision. Econometrica: Journal of the Econometric Society, pages 680-684, 1952
30. T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. ArXiv e-prints, 2013
31. R. Pascanu, T. Mikolov, and Y. Bengio. On the difficulty of training recurrent neural networks. In ICML, 2013
32. P. Peng, Y. Tian, T. Xiang, Y. Wang, and T. Huang. Joint learning of semantic and latent attributes. In ECCV, 2016
33. P. Peng, Y. Tian, T. Xiang, Y.Wang, M. Pontil, and T. Huang. Joint semantic and latent attribute modelling for cross-class transfer learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
34. J. Read, B. Pfahringer, G. Holmes, and E. Frank. Classifier chains for multi-label classification. Machine Learning, 85(3):333, 2011
35. D. A. Reid, M. S. Nixon, and S. V. Stevenage. Soft biometrics; human identification using comparative descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6):1216-1228, 2014
36. 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. International Journal of Computer Vision, 115(3):211-252, 2015
37. F. Samaria and F. Fallside. Face identification and feature extraction using hidden markov models. Citeseer, 1993
38. Z. Shi, T. M. Hospedales, and T. Xiang. Transferring a semantic representation for person re-identification and search. In CVPR, 2015
39. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015
40. C. Su, S. Zhang, J. Xing, W. Gao, and Q. Tian. Deep attributes driven multi-camera person re-identification. In ECCV, 2016
41. P. Sudowe, H. Spitzer, and B. Leibe. Person attribute recognition with a jointly-trained holistic cnn model. In Proceedings of the IEEE International Conference on Computer Vision Workshops, 2015
42. I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In NIPS, 2014
43. R. R. Varior, B. Shuai, J. Lu, D. Xu, and G. Wang. A siamese long short-term memory architecture for human reidentification. In ECCV, 2016
44. O. Vinyals, Kaiser, T. Koo, S. Petrov, I. Sutskever, and G. Hinton. Grammar as a foreign language. In NIPS, 2015
45. O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Show and tell: A neural image caption generator. In CVPR
46. J. Wang, Y. Yang, J. Mao, Z. Huang, C. Huang, and W. Xu. Cnn-rnn: A unified framework for multi-label image classification. In CVPR, 2016
47. J. Wang, X. Zhu, and S. Gong. Video semantic clustering with sparse and incomplete tags. In AAAI Conference on Artificial Intelligence, 2016
48. J. Wang, X. Zhu, and S. Gong. Discovering visual concept structure with sparse and incomplete tags. Artificial Intelligence, 250:16-36, 2017
49. P. J. Werbos. Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10):1550-1560, 1990
50. J. Zhu, S. Liao, Z. Lei, and S. Z. Li. Multi-label convolutional neural network based pedestrian attribute classification. Image and Vision Computing, 2016
51. J. Zhu, S. Liao, Z. Lei, D. Yi, and S. Li. Pedestrian attribute classification in surveillance: Database and evaluation. In Workshop of IEEE International Conference on Computer Vision, 2013
52. J. Zhu, S. Liao, D. Yi, Z. Lei, and S. Z. Li. Multi-label cnn based pedestrian attribute learning for soft biometrics. In International Conference on Biometrics, 2015.