The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Bi-Directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification
Abstract
Visible thermal person re-identification (VT-REID) is a task of matching person images captured by thermal and visible cameras, which is an extremely important issue in night-time surveillance applications. Existing cross-modality recognition works mainly focus on learning sharable feature representations to handle the cross-modality discrepancies. However, apart from the cross-modality discrepancy caused by different camera spectrums, VT-REID also suffers from large cross-modality and intra-modality variations caused by different camera environments and human poses, and so on. In this paper, we propose a dual-path network with a novel bi-directional dual-constrained top-ranking (BDTR) loss to learn discriminative feature representations. It is featured in two aspects: 1) end-to-end learning without extra metric learning step and 2) the dual-constraint simultaneously handles the cross-modality and intra-modality variations to ensure the feature discriminability. Meanwhile, a bi-directional center-constrained top-ranking (eBDTR) is proposed to incorporate the previous two constraints into a single formula, which preserves the properties to handle both cross-modality and intra-modality variations. The extensive experiments on two cross-modality re-ID datasets demonstrate the superiority of the proposed method compared to the state-of-the-arts.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Scalable Person Re-identification: A Benchmark
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.133 · Cited by 4,506
A Discriminative Feature Learning Approach for Deep Face Recognition
Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_31 · Cited by 4,077
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00393 · Cited by 3,664
In Defense of the Triplet Loss for Person Re-Identification
Hermans, A., Beyer, L., & Leibe, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07737 · Cited by 2,687
Person re-identification by Local Maximal Occurrence representation and metric learning
Liao, S., Hu, Y., Zhu, X., & Li, S. Z. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298832 · Cited by 2,091
Deep Metric Learning via Lifted Structured Feature Embedding
Song, H. O., Xiang, Y., Jegelka, S., & Savarese, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.434 · Cited by 1,630
Person Re-identification: Past, Present and Future
Zheng, L., Yang, Y. P., & Hauptmann, A. G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.02984 · Cited by 922
RGB-Infrared Cross-Modality Person Re-identification
Wu, A., Zheng, W.-S., Yu, H.-X., Gong, S., & Lai, J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.575 · Cited by 862
Cited by
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-identification
Ye, M., Shen, J., J. Crandall, D., Shao, L., & Luo, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58520-4_14 · Cited by 523
Self-citationFMCNet: Feature-Level Modality Compensation for Visible-Infrared Person Re-Identification
Zhang, Q., Lai, C., Liu, J., Huang, N., & Han, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00720 · Cited by 279
Metadata
- Authors
- Ye, M., Lan, X., Wang, Z., & Yuen, P. C.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2020
- Volume
- 15
- Pages
- 407-419
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2970390221
- Cited by
- 331
- References
- 57
- FWCI
- 17.76
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ye, M., Lan, X., Wang, Z., & Yuen, P. C. (2020). Bi-Directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/tifs.2019.2921454
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