[1] Y X Shi, Y Zhou. Person re-identification based on stepped feature space segmentation and local attention mechanism. J Electron Inf Technol, 44, 195-202(2022).

[2] L Liu, X Li, X M Lei. A person re-identification method with multi-scale and multi-feature fusion. J Comput-Aided Des Comput Graphics, 34, 1868-1876(2022).

[3] S Y Cheng, Y Chen. Camera-aware unsupervised person re-identification method guided by pseudo-label refinement. Opto-Electron Eng, 50, 230239(2023).

[4] H J Zheng, B Ge, C X Xia et al. Infrared-visible person re-identification based on multi feature aggregation. Opto-Electron Eng, 50, 230136(2023).

[5] Y K Zhang, H Z Wang. Diverse embedding expansion network and low-light cross-modality benchmark for visible-infrared person re-identification, 2153-2162(2023). https://doi.org/10.1109/CVPR52729.2023.00214

[6] Q Wu, P Y Dai, J Chen et al. Discover cross-modality nuances for visible-infrared person re-identification, 4330-4339(2021). https://doi.org/10.1109/CVPR46437.2021.00431

[7] Y Y Zhang, Y H Kang, S Y Zhao et al. Dual-semantic consistency learning for visible-infrared person re-identification. IEEE Trans Inf Forensics Secur, 18, 1554-1565(2022).

[8] M Ye, J B Shen, D J Crandall et al. Dynamic dual-attentive aggregation learning for visible-infrared person re-identification, 229-247(2020). https://doi.org/10.1007/978-3-030-58520-4_14

[9] S Choi, S Lee, Y Kim et al. Hi-CMD: hierarchical cross-modality disentanglement for visible-infrared person re-identification, 10257-10266(2020). https://doi.org/10.1109/CVPR42600.2020.01027

[10] Y K Zhang, Y Yan, Y Lu et al. Towards a unified middle modality learning for visible-infrared person re-identification, 788-796(2021). https://doi.org/10.1145/3474085.3475250

[11] L Ma, Z B Guan, X G Dai et al. A cross-modality person re-identification method based on joint middle modality and representation learning. Electronics, 12, 2687(2023).

[12] M Ye, J B Shen, L Shao. Visible-infrared person re-identification via homogeneous augmented tri-modal learning. IEEE Trans Inf Forensics Secur, 16, 728-739(2021).

[13] Q Zhang, C Z Lai, J N Liu et al. FMCNet: feature-level modality compensation for visible-infrared person re-identification, 7349-7358(2022). https://doi.org/10.1109/CVPR52688.2022.00720

[14] J Lu, S S Zhang, M D Chen et al. Cross-modality person re-identification based on intermediate modal generation. Opt Lasers Eng, 177, 108117(2024).

[15] D G Li, X Wei, X P Hong et al. Infrared-visible cross-modal person re-identification with an X modality, 4610-4617(2020). https://doi.org/10.1609/aaai.v34i04.5891

[16] M Ye, J B Shen, G J Lin et al. Deep learning for person re-identification: a survey and outlook. IEEE Trans Pattern Anal Mach Intell, 44, 2872-2893(2022).

[17] M Ye, W J Ruan, B Du et al. Channel augmented joint learning for visible-infrared recognition, 13567-13576(2021). https://doi.org/10.1109/ICCV48922.2021.01331

[18] X G Pan, P Luo, J P Shi et al. Two at once: enhancing learning and generalization capacities via IBN-net, 464-479(2018). https://doi.org/10.1007/978-3-030-01225-0_29

[19] J Hu, L Shen, G Sun. Squeeze-and-excitation networks, 7132-7141(2018). https://doi.org/10.1109/CVPR.2018.00745

[20] X L Wang, R Girshick, A Gupta et al. Non-local neural networks, 7794-7803(2018). https://doi.org/10.1109/CVPR.2018.00813

[21] A C Wu, W S Zheng, H X Yu et al. RGB-infrared cross-modality person re-identification, 5380-5389(2017). https://doi.org/10.1109/ICCV.2017.575

[22] D T Nguyen, H G Hong, K W Kim et al. Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors, 17, 605(2017).

[23] G A Wang, T Z Zhang, J Cheng et al. RGB-infrared cross-modality person re-identification via joint pixel and feature alignment, 3623-3632(2019). https://doi.org/10.1109/ICCV.2019.00372

[24] Z X Wang, Z Wang, Y Q Zheng et al. Learning to reduce dual-level discrepancy for infrared-visible person re-identification, 618-626(2019). https://doi.org/10.1109/CVPR.2019.00071

[25] G A Wang, T Z Zhang, Y Yang et al. Cross-modality paired-images generation for RGB-infrared person re-identification, 12144-12151(2020). https://doi.org/10.1609/aaai.v34i07.6894

[26] X Hao, S Y Zhao, M Ye et al. Cross-modality person re-identification via modality confusion and center aggregation, 16403-16412(2021). https://doi.org/10.1109/ICCV48922.2021.01609

[27] X T Zheng, X M Chen, X Q Lu. Visible-infrared person re-identification via partially interactive collaboration. IEEE Trans Image Process, 31, 6951-6963(2022).

[28] H Lu, X Z Zou, P P Zhang. Learning progressive modality-shared transformers for effective visible-infrared person re-identification, 1835-1843(2023). https://doi.org/10.1609/aaai.v37i2.25273

[29] N C Huang, J N Liu, Y J Luo et al. Exploring modality-shared appearance features and modality-invariant relation features for cross-modality person Re-IDentification. Pattern Recognit, 135, 109145(2023).

[30] H J Liu, D X Xia, W Jiang. Towards homogeneous modality learning and multi-granularity information exploration for visible-infrared person re-identification. IEEE J Sel Top Signal Process, 17, 545-559(2023).

[31] N C Huang, B C Xing, Q Zhang et al. Co-segmentation assisted cross-modality person re-identification. Inf Fusion, 104, 102194(2024).

[32] Z F Lu, R H Lin, H F Hu. Tri-level modality-information disentanglement for visible-infrared person re-identification. IEEE Trans Multimedia, 26, 2700-2714(2024).

[33] der Maaten L van, G Hinton. Visualizing data using t-SNE. J Mach Learn Res, 9, 2579-2605(2008).

[34] R R Selvaraju, M Cogswell, A Das et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis, 128, 336-359(2020).