The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Infrared-Visible Cross-Modal Person Re-Identification with an X Modality
Open accessAbstract
This paper focuses on the emerging Infrared-Visible cross-modal person re-identification task (IV-ReID), which takes infrared images as input and matches with visible color images. IV-ReID is important yet challenging, as there is a significant gap between the visible and infrared images. To reduce this ‘gap’, we introduce an auxiliary X modality as an assistant and reformulate infrared-visible dual-mode cross-modal learning as an X-Infrared-Visible three-mode learning problem. The X modality restates from RGB channels to a format with which cross-modal learning can be easily performed. With this idea, we propose an X-Infrared-Visible (XIV) ReID cross-modal learning framework. Firstly, the X modality is generated by a lightweight network, which is learnt in a self-supervised manner with the labels inherited from visible images. Secondly, under the XIV framework, cross-modal learning is guided by a carefully designed modality gap constraint, with information exchanged cross the visible, X, and infrared modalities. Extensive experiments are performed on two challenging datasets SYSU-MM01 and RegDB to evaluate the proposed XIV-ReID approach. Experimental results show that our method considerably achieves an absolute gain of over 7% in terms of rank 1 and mAP even compared with the latest state-of-the-art methods.
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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
Few-Shot Class-Incremental Learning
Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., & Gong, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01220 · Cited by 499
Self-citationChannel Augmented Joint Learning for Visible-Infrared Recognition
Ye, M., Ruan, W., Du, B., & Shou, M. Z. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01331 · Cited by 375
Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification
Wu, Q., Dai, P., Chen, J., Lin, C.-W., Wu, Y., Huang, F., Zhong, B., & Ji, R. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00431 · Cited by 313
FMCNet: 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
- Li, D., Wei, X., Hong, X., & Gong, Y.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 04
- Pages
- 4610-4617
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2997877744
- Cited by
- 431
- References
- 32
- FWCI
- 18.49
- Type
- conference paper
- Language
- en
- Open access
- diamond
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