Academic Journal of Computing & Information Science, 2025, 8(10); doi: 10.25236/AJCIS.2025.081001.
Junli Feng1, Jingni Ma1
1School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, 400074, China
Visible-Infrared Person Re-identification serves as a core technology in surveillance systems, enabling accurate identification of individuals across different times and locations while breaking through the constraints of lighting conditions. In contrast, traditional methods exhibit poor performance in low-light environments, making it difficult to support the advancement of relevant research. To address the inter-modal and intra-modal differences between infrared and visible light modalities, this paper proposes an Attention and Feature Enhancement Network (AFEN). The network incorporates a median-enhanced spatial-channel attention module, which can effectively capture multi-scale features. The designed feature enhancement module is capable of reducing the distribution gap between modal features, enhancing the discriminative power, robustness, and generalization ability of features, thereby improving the accuracy of cross-modal matching.
Person Re-identification, Attention Mechanism, Convolutional Neural Network, Feature Learning
Junli Feng, Jingni Ma. Attention Mechanism and Feature Enhancement for Visible-Infrared Person Re-Identification. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 10: 1-7. https://doi.org/10.25236/AJCIS.2025.081001.
[1] Zhang Y, Zhao S, Kang Y, et al. Modality synergy complement learning with cascaded aggregation for visible-infrared person re-identification[C]//European conference on computer vision. Cham: Springer Nature Switzerland, 2022: 462-479.
[2] Mukhtar H, Khan M U G. CMOT: A cross-modality transformer for RGB-D fusion in person re-identification with online learning capabilities[J]. Knowledge-Based Systems, 2024, 283: 111155.
[3] Ye M, Shen J, Shao L. Visible-infrared person re-identification via homogeneous augmented tri-modal learning[J]. IEEE Transactions on Information Forensics and Security, 2020, 16: 728-739.
[4] Wang Z, Wang Z, Zheng Y, et al. Learning to reduce dual-level discrepancy for infrared-visible person re-identification[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 618-626.
[5] Ye M, Ruan W, Du B, et al. Channel augmented joint learning for visible-infrared recognition[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 13567-13576.
[6] Li D, Wei X, Hong X, et al. Infrared-visible cross-modal person re-identification with an x modality[C]//Proceedings of the AAAI conference on artificial intelligence. 2020, 34(04): 4610-4617.
[7] Wei Z, Yang X, Wang N, et al. Syncretic modality collaborative learning for visible infrared person re-identification[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 225-234.
[8] Ye M, Shen J, J. Crandall D, et al. Dynamic dual-attentive aggregation learning for visible-infrared person re-identification[C]//Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVII 16. Springer International Publishing, 2020: 229-247.
[9] Wu Q, Dai P, Chen J, et al. Discover cross-modality nuances for visible-infrared person re-identification[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 4330-4339.
[10] Chan S, Du F, Tang T, et al. Parameter sharing and multi-granularity feature learning for cross-modality person re-identification[J]. Complex & Intelligent Systems, 2024, 10(1): 949-962.
[11] Cheng D, Wang X, Wang N, et al. Cross-modality person re-identification with memory-based contrastive embedding[C]//Proceedings of the AAAI conference on artificial intelligence. 2023, 37(1): 425-432.
[12] Liang T, Jin Y, Liu W, et al. Cross-modality transformer with modality mining for visible-infrared person re-identification[J]. IEEE Transactions on Multimedia, 2023, 25: 8432-8444.
[13] Lu H, Zou X, Zhang P. Learning progressive modality-shared transformers for effective visible-infrared person re-identification[C]//Proceedings of the AAAI conference on artificial intelligence. 2023, 37(2): 1835-1843.
[14] Deng J, Dong W, Socher R, et al. Imagenet: A large-scale hierarchical image database[C]//2009 IEEE conference on computer vision and pattern recognition. IEEE, 2009: 248-255.