Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2017-October, Issue , 2017, Pages 4743-4752

  Xu, Shuangjie ^a   Cheng, Yu ^b   Gu, Kang ^a   Yang, Yang ^c   Chang, Shiyu ^d   Zhou, Pan ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b IBM RESEARCH   (United States)

^c IBM T J WATSON RESEARCH CENTER   (United States)

^d NORTHWESTERN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION;

CURRENT INPUT; FEATURE EXTRACTOR; ITS APPLICATIONS; PERSON RE IDENTIFICATIONS; SPATIAL TEMPORALS; STATE-OF-ART METHODS; TEMPORAL POOLING; VISUAL SURVEILLANCE;

HUMAN COMPUTER INTERACTION;

EID: 85041912284     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.507     Document Type: Conference Paper

References (41)

1. D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473, 2014.
2. A. Barhillel, T. Hertz, N. Shental, and D. Weinshall. Learning a mahalanobis metric from equivalence constraints. JMLR, pages 937-965, 2005.
3. Y. Cheng, L. M. Brown, Q. Fan, R. S. Feris, S. Pankanti, and T. Zhang. Riskwheel: Interactive visual analytics for surveillance event detection. In IEEE International Conference on Multimedia and Expo, ICME 2014, Chengdu, China, July 14-18, 2014, pages 1-6, 2014.
4. Y. Cheng, Q. Fan, S. Pankanti, and A. Choudhary. Temporal sequence modeling for video event detection. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014.
5. C. N. dos Santos, M. Tan, B. Xiang, and B. Zhou. Attentive pooling networks. CoRR, abs/1602.03609, 2016.
6. M. Farenzena, L. Bazzani, A. Perina, V. Murino, and M. Cristani. Person re-identification by symmetry-driven accumulation of local features. In :IEEE CVPR, pages 2360-2367, 2010.
7. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. CoRR, abs/1406.4729, 2014.
8. M. Hirzer, C. Beleznai, P. M. Roth, and H. Bischof. Person re-identification by descriptive and discriminative classification. In scandinavian conference on image analysis, pages 91-102, 2011.
9. S. Karanam, Y. Li, and R. Radke. Person re-identification with discriminatively trained viewpoint invariant dictionaries. In :IEEE ICCV, pages 4516-4524, 2015.
10. S. Karanam, Y. Li, and R. Radke. Sparse re-id: Block sparsity for person re-identification. In :IEEE CVPR Workshops, pages 33-40, 2015.
11. I. Kviatkovsky, A. Adam, and E. Rivlin. Color invariants for person reidentification. IEEE TPAMI, 35(7):1622-1634, 2013.
12. Y. Li, Z. Wu, S. Karanam, and R. Radke. Multi-shot human re-identification using adaptive fisher discriminant analysis. In BMVC, 2015.
13. Z. Li, S. Chang, F. Liang, T. S. Huang, L. Cao, and J. R. Smith. Learning locally-adaptive decision functions for person verification. In :IEEE CVPR, pages 3610-3617, 2013.
14. S. Liao and S. Z. Li. Efficient psd constrained asymmetric metric learning for person re-identification. In :IEEE ICCV, pages 3685-3693, 2015.
15. C. Liu, S. Gong, and C. C. Loy. Person re-identification: what features are important? In :IEEE ICCV, pages 391-401, 2012.
16. H. Liu, J. Feng, M. Qi, J. Jiang, and S. Yan. End-to-end comparative attention networks for person re-identification. CoRR, abs/1606.04404, 2016.
17. K. Liu, B. Ma, W. Zhang, and R. Huang. A spatiotemporal appearance representation for video-based pedestrian re-identification. In :IEEE CVPR, pages 3810-3818, 2015.
18. B. D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In IJCAI, pages 674-679, 1981.
19. B. Ma, Y. Su, and F. Jurie. Local descriptors encoded by fisher vectors for person re-identification. In :IEEE ICCV, pages 413-422, 2012.
20. N. Mclaughlin, J. M. Rincon, and P. Miller. Recurrent convolutional network for video-based person re-identification. In :IEEE CVPR, pages 1325-1334, 2016.
21. S. Paisitkriangkrai, C. Shen, and A. V. D. Hengel. Learning to rank in person re-identification with metric ensembles. In :IEEE CVPR, pages 1846-1855, 2015.
22. D. G. S, S. Brennan, and H. Tao. Evaluating appearance models for recognition, reacquisition, and tracking. PETS, 3, 2007.
23. S. Sharma, R. Kiros, and R. Salakhutdinov. Action recognition using visual attention. CoRR, abs/1511.04119, 2015.
24. A. Subramaniam, M. Chatterjee, and A. Mittal. Deep neural networks with inexact matching for person re-identification. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in NIPS, pages 2667-2675. Curran Associates, Inc., 2016.
25. R. R. Varior, B. Shuai, J. Lu, D. Xu, and G. Wang. A siamese long short-term memory architecture for human reidentification. In ECCV, pages 135-153, 2016.
26. J. Wang, Y. Cheng, and R. Schmidt Feris. Walk and learn: Facial attribute representation learning from egocentric video and contextual data. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
27. T. Wang, S. Gong, X. Zhu, and S. Wang. Person reidentification by video ranking. In ECCV, pages 688-703, 2014.
28. X. Wang, G. Doretto, T. Sebastian, J. Rittscher, and P. Tu. Shape and appearance context modeling. In :IEEE ICCV, pages 1-8, 2007.
29. K. Q. Weinberger and L. K. Saul. Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, pages 207-244, 2009.
30. F. Xiong, M. Gou, O. Camps, and M. Sznaier. Person reidentification using kernel-based metric learning methods. In ECCV, pages 1-16, 2014.
31. K. Xu, J. Ba, R. Kiros, K. Cho, A. C. Courville, R. Salakhutdinov, R. S. Zemel, and Y. Bengio. Show, attend and tell: Neural image caption generation with visual attention. In ICML, pages 2048-2057, 2015.
32. Y. Yan, B. Ni, Z. Song, C. Ma, Y. Yan, and X. Yang. Person re-identification via recurrent feature aggregation. In ECCV, pages 701-716, 2016.
33. D. Yi, Z. Lei, S. Liao, and S. Z. Li. Deep metric learning for person re-identification. In :IEEE CVPR, pages 24-39, 2014.
34. W. Yin, H. Schutze, B. Xiang, and B. Zhou. ABCNN: attention-based convolutional neural network for modeling sentence pairs. TACL, 4:259-272, 2016.
35. J. You, A. Wu, X. Li, and W. Zheng. Top-push video-based person re-identification. In :IEEE CVPR, pages 1345-1253, 2016.
36. Z. Zhang, Y. chen, and V. Saligrama. Group membership prediction. In :IEEE ICCV, pages 3916-3924, 2015.
37. R. Zhao, W. Ouyang, and X. Wang. Person re-identification by salience matching. In :IEEE ICCV, pages 2528-2535, 2013.
38. R. Zhao, W. Ouyang, and X. Wang. Learning mid-level filters for person re-identification. In :IEEE CVPR, pages 144-151, 2014.
39. L. Zheng, Z. Bie, Y. Sun, J. Wang, C. Su, S. Wang, and Q. Tian. Mars: A video benchmark for large-scale person re-identification. In ECCV, pages 868-884, 2016.
40. L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, J. Bu, and Q. Tian. Scalable person re-identification: a benchmark. In :IEEE ICCV, pages 1116-1124, 2015.
41. W. S. Zheng, S. Gong, and T. Xiang. Reidentification by relative distance comparison. IEEE TPAMI, 35(3):653-668, 2013.