[1] Luo H, Jiang W, Fan X et al. A survey on deep learning based person re-identification[J]. Acta Automatica Sinica, 45, 2032-2049(2019).
[2] Zhang T, Yi Z M, Li X et al. Improved algorithm for person re-identification based on global features[J]. Laser & Optoelectronics Progress, 57, 241503(2020).
[3] Li C, Jiang M, Kong J. Multi-branch person re-identification based on multi-scale attention[J]. Laser & Optoelectronics Progress, 57, 201001(2020).
[4] Lu J, Chen X, Luo M X et al. Person re-identification research via deep learning[J]. Laser & Optoelectronics Progress, 57, 160003(2020).
[5] Nguyen D T, Hong H G, Kim K W et al. Person recognition system based on a combination of body images from visible light and thermal cameras[J]. Sensors, 17, 605(2017).
[6] Ye M, Lan X, Li J et al. Hierarchical discriminative learning for visible thermal person re-identification[C], 7501-7508(2018).
[7] Zhu Y X, Yang Z, Wang L et al. Hetero-Center loss for cross-modality person re-identification[J]. Neurocomputing, 386, 97-109(2020).
[8] Dai P, Ji R, Wang H et al. Cross-modality person re-identification with generative adversarial training[C], 677-683(2018).
[9] Yang J H, Ruan D Y, Huang J W et al. An embedding cost learning framework using GAN[J]. IEEE Transactions on Information Forensics and Security, 15, 839-851(2020).
[10] Li D G, Wei X, Hong X P et al. Infrared-visible cross-modal person re-identification with an X modality[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 4610-4617(2020).
[11] Wang X, Girshick R, Gupta A et al. Non-local neural networks[C], 7794-7803(2018).
[12] Liu H J, Tan X H, Zhou X C. Parameter sharing exploration and hetero-center triplet loss for visible-thermal person re-identification[J]. IEEE Transactions on Multimedia, 23, 4414-4425(2021).
[13] Ye M, Shen J B, Lin G J et al. Deep learning for person re-identification: a survey and outlook[EB/OL]. https://arxiv.org/abs/2001.04193
[14] Yin J H, Ma Z Y, Xie J Y et al. DF2AM: dual-level feature fusion and affinity modeling for RGB-infrared cross-modality person re-identification[EB/OL]. https://arxiv.org/abs/2104.00226
[15] Hermans A, Beyer L, Leibe B. In defense of the triplet loss for person re-identification[EB/OL]. https://arxiv.org/abs/1703.07737
[16] Liu T Y, Liu Z X. Overview of cross modality person re-identification research[J]. Modern Computer, 135-139(2021).
[17] Liu H J, Chai Y X, Tan X H et al. Strong but simple baseline with dual-granularity triplet loss for visible-thermal person re-identification[J]. IEEE Signal Processing Letters, 28, 653-657(2021).
[18] Wu A C, Zheng W S, Yu H X et al. RGB-infrared cross-modality person re-identification[C], 5390-5399(2017).
[19] Ye M, Lan X Y, Wang Z et al. Bi-directional center-constrained top-ranking for visible thermal person re-identification[J]. IEEE Transactions on Information Forensics and Security, 15, 407-419(2020).
[20] Wang Z X, Wang Z, Zheng Y Q et al. Learning to reduce dual-level discrepancy for infrared-visible person re-identification[C], 618-626(2019).
[21] Ye M, Lan X Y, Leng Q M et al. Cross-modality person re-identification via modality-aware collaborative ensemble learning[J]. IEEE Transactions on Image Processing, 29, 9387-9399(2020).
[22] Wang G A, Zhang T Z, Cheng J et al. RGB-infrared cross-modality person re-identification via joint pixel and feature alignment[C], 3622-3631(2019).
[23] Ye M, Shen J B, Crandall D J et al. Dynamic dual-attentive aggregation learning for visible-infrared person re-identification[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020. Lecture notes in computer science, 12362, 229-247(2020).