Multi-level Factorisation Net for Person Re-identification

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

Volumn , Issue , 2018, Pages 2109-2118

  Chang, Xiaobin ^a   Hospedales, Timothy M ^b   Xiang, Tao ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FACTORIZATION; NETWORK ARCHITECTURE; SEMANTICS;

FEATURE REPRESENTATION; HUMAN ANNOTATIONS; MANUAL ANNOTATION; MULTIPLE FACTORS; PERSON RE IDENTIFICATIONS; STATE OF THE ART; VIEW INVARIANTS; VISUAL APPEARANCE;

COMPUTER VISION;

EID: 85062841347     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2018.00225     Document Type: Conference Paper

References (57)

1. K. Ahmed, M. H. Baig, and L. Torresani. Network of experts for large-scale image categorization. In ECCV, 2016.
2. S. Bai, X. Bai, and Q. Tian. Scalable person re-identification on supervised smoothed manifold. CVPR, 2017.
3. S. Cai, W. Zuo, and L. Zhang. Higher-order integration of hierarchical convolutional activations for fine-grained visual categorization. In CVPR, 2017.
4. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248-255. IEEE, 2009.
5. D. Eigen, M. Ranzato, and I. Sutskever. Learning factored representations in a deep mixture of experts. ICLR Workshop, 2014.
6. H. Fan, X. Mei, D. Prokhorov, and H. Ling. Multi-level contextual rnns with attention model for scene labeling. arXiv:1607.02537, 2016.
7. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. IEEE Trans. PAMI, 32(9):1627-1645, 2010.
8. B. Hariharan, P. Arbeĺaez, R. Girshick, and J. Malik. Hypercolumns for object segmentation and fine-grained localization. In CVPR, pages 447-456, 2015.
9. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. CVPR, 2015.
10. K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. In ECCV, pages 630-645. Springer, 2016.
11. S. Hou, X. Liu, and Z.Wang. Dualnet: Learn complementary features for image recognition. In CVPR, pages 502-510, 2017.
12. G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten. Densely connected convolutional networks. CVPR, 2016.
13. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton. Adaptive mixtures of local experts. Neural Computation, 3(1):79-87, 1991.
14. X. Jin, Y. Chen, J. Dong, J. Feng, and S. Yan. Collaborative layer-wise discriminative learning in deep neural networks. In ECCV, pages 733-749. Springer, 2016.
15. S. Khamis, C.-H. Kuo, V. K. Singh, V. D. Shet, and L. S. Davis. Joint learning for attribute-consistent person reidentification. In ECCV Workshops, pages 134-146, 2014.
16. D. Kingma and J. Ba. Adam: A method for stochastic optimization. ICLR, 2014.
17. A. Krizhevsky and G. Hinton. Learning multiple layers of features from tiny images. 2009.
18. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1097-1105, 2012.
19. R. Layne, T. M. Hospedales, and S. Gong. Person reidentification by attributes. In BMVC, 2012.
20. Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521(7553):436-444, 2015.
21. C.-Y. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu. Deeply-supervised nets. In AISTATS, 2015.
22. D. Li, X. Chen, Z. Zhang, and K. Huang. Learning deep context-aware features over body and latent parts for person re-identification. In CVPR, 2017.
23. W. Li, R. Zhao, T. Xiao, and X.Wang. Deepreid: Deep filter pairing neural network for person re-identification. In CVPR, pages 152-159, 2014.
24. W. Li, X. Zhu, and S. Gong. Person re-identification by deep joint learning of multi-loss classification. IJCAI, 2017.
25. W. Li, X. Zhu, and S. Gong. Harmonious attention network for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition, 2018.
26. Y. Lin, L. Zheng, Z. Zheng, Y. Wu, and Y. Yang. Improving person re-identification by attribute and identity learning. arXiv:1703.07220, 2017.
27. H. Liu, J. Feng, M. Qi, J. Jiang, and S. Yan. End-to-end comparative attention networks for person re-identification. IEEE Trans. IP, 2016.
28. J. Liu, Z.-J. Zha, Q. Tian, D. Liu, T. Yao, Q. Ling, and T. Mei. Multi-scale triplet cnn for person re-identification. In ACM MM, 2016.
29. X. Liu, H. Zhao, M. Tian, L. Sheng, J. Shao, S. Yi, J. Yan, and X. Wang. Hydraplus-net: Attentive deep features for pedestrian analysis. ICCV, 2017.
30. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, pages 3431-3440, 2015.
31. T. Matsukawa and E. Suzuki. Person re-identification using cnn features learned from combination of attributes. In ICPR, 2016.
32. N. McLaughlin, J. M. del Rincon, and P. C. Miller. Person reidentification using deep convnets with multitask learning. IEEE Trans. CSVT, 27(3):525-539, 2017.
33. T. Nortcliffe. People Analysis CCTV Investigator Handbook. Home Office Centre of Applied Science and Technology, 2011.
34. E. Ristani, F. Solera, R. Zou, R. Cucchiara, and C. Tomasi. Performance measures and a data set for multi-target, multicamera tracking. In ECCV Workshop on Benchmarking Multi-Target Tracking, 2016.
35. A. Schumann and R. Stiefelhagen. Person re-identification by deep learning attribute-complementary information. In CVPR Workshops, 2017.
36. N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, and J. Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. ICLR, 2017.
37. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015.
38. Y. Sun, L. Zheng, W. Deng, and S. Wang. Svdnet for pedestrian retrieval. ICCV, 2017.
39. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, pages 1-9, 2015.
40. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. Rethinking the inception architecture for computer vision. In CVPR, pages 2818-2826, 2016.
41. J. Wang, X. Zhu, S. Gong, and W. Li. Transferable joint attribute-identity deep learning for unsupervised person reidentification. In IEEE Conference on Computer Vision and Pattern Recognition, 2018.
42. T. Xiao, H. Li,W. Ouyang, and X.Wang. Learning deep feature representations with domain guided dropout for person re-identification. In CVPR, 2016.
43. T. Xiao, S. Li, B. Wang, L. Lin, and X. Wang. Joint detection and identification feature learning for person search. In CVPR, 2017.
44. S. Xie, R. Girshick, P. Dolĺar, Z. Tu, and K. He. Aggregated residual transformations for deep neural networks. CVPR, 2016.
45. S. Xie and Z. Tu. Holistically-nested edge detection. In CVPR, pages 1395-1403, 2015.
46. C. Yanbei, Z. Xiatian, and G. Shaogang. Person reidentification by deep learning multi-scale representations. ICCV Workshop, 2017.
47. S. Yang and D. Ramanan. Multi-scale recognition with dagcnns. In ICCV, pages 1215-1223, 2015.
48. X. Yang, P. Molchanov, and J. Kautz. Multilayer and multimodal fusion of deep neural networks for video classification. In ACM MM, 2016.
49. R. Yu, Z. Zhou, S. Bai, and X. Bai. Divide and fuse: A reranking approach for person re-identification. BMVC, 2017.
50. W. Yu, K. Yang, H. Yao, X. Sun, and P. Xu. Exploiting the complementary strengths of multi-layer cnn features for image retrieval. Neural Computing, 237:235-241, 2017.
51. S. E. Yuksel, J. N. Wilson, and P. D. Gader. Twenty years of mixture of experts. IEEE Trans. NNLS, 23(8):1177-1193, 2012.
52. Y. Zhang, T. Xiang, T. M. Hospedales, and H. Lu. Deep mutual learning. arXiv:1706.00384, 2017.
53. H. Zhao, M. Tian, S. Sun, J. Shao, J. Yan, S. Yi, X. Wang, and X. Tang. Spindle net: Person re-identification with human body region guided feature decomposition and fusion. In CVPR, 2017.
54. L. Zhao, X. Li, J.Wang, and Y. Zhuang. Deeply-learned partaligned representations for person re-identification. ICCV, 2017.
55. L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian. Scalable person re-identification: A benchmark. In ICCV, 2015.
56. Z. Zheng, L. Zheng, and Y. Yang. Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In ICCV, 2017.
57. Z. Zhong, L. Zheng, D. Cao, and S. Li. Re-ranking person re-identification with k-reciprocal encoding. CVPR, 2017.