[1] CAI X, LIU L, ZHU L, et al. Dual-modality hard mining triplet-center loss for visible infrared person re-identification[J]. Knowledge-Based Systems, 2021, 215: 106772.
[2] WU A, ZHENG W S, YU H X, et al. RGB-infrared cross-modality person re-identification[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision, 2017: 5380-5389.
[3] LIU H, MA S, XIA D, et al. SFANet: a spectrum-aware feature augmentation network for visible-infrared person reidentification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(4): 1958-1971.
[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 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 618-626.
[5] CHOI S, LEE S, KIM Y, et al. Hi-CMD: hierarchical cross-modality disentanglement for visible-infrared person re-identification[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 10257-10266.
[6] ZHAO Z, LIU B, CHU Q, et al. Joint color-irrelevant consistency learning and identity-aware modality adaptation for visible-infrared cross modality person re-identification[C]//Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021: 3520-3528.
[7] YE M, LAN X, WANG Z, et al. Bi-directional center-constrained top-ranking for visible thermal person re-identification[J]. IEEE Transactions on Information Forensics and Security, 2019, 15: 407-419.
[8] YE M, LAN X, LENG Q, et al. Cross-modality person re-identification via modality-aware collaborative ensemble learning[J]. IEEE Transactions on Image Processing, 2020, 29: 9387-9399.
[9] LI D, WEI X, HONG X, et al. Infrared-visible cross-modal person re-identification with an X modality[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020: 4610-4617.
[10] CHEN Y, WAN L, LI Z, et al. Neural feature search for RGB-infrared person re-identification[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 587-597.
[11] YE M, SHEN J, J CRANDALL D, et al. Dynamic dual-attentive aggregation learning for visible-infrared person re-identification[C]//Proceedings of the 16th European Conference on Computer Vision. Cham: Springer, 2020: 229-247.
[12] YE M, SHEN J, LIN G, et al. Deep learning for person re-identification: a survey and outlook[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(6): 2872-2893.
[13] PAN X, LUO P, SHI J, et al. Two at once: enhancing learning and generalization capacities via IBN-net[C]//Proceedings of the 15th European Conference on Computer Vision, 2018: 464-479.
[14] GRAY D, TAO H. Viewpoint invariant pedestrian recognition with an ensemble of localized features[C]//Proceedings of the 10th European Conference on Computer Vision. Berlin, Heidelberg: Springer, 2008: 262-275.
[15] ZHENG W S, GONG S, XIANG T. Person re-identification by probabilistic relative distance comparison[C]//Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, 2011: 649-656.
[16] WANG F, ZUO W, LIN L, et al. Joint learning of single-image and cross-image representations for person re-identification[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1288-1296.
[17] ZHENG L, ZHANG H, SUN S, et al. Person re-identification in the wild[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1367-1376.
[18] SUN Y, ZHENG L, YANG Y, et al. Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline)[C]//Proceedings of the 15th European Conference on Computer Vision, 2018: 480-496.
[19] 肖雅妮, 范馨月, 陈文峰. 多分支融合局部特征的行人重识别算法[J]. 计算机工程与应用, 2021, 57(18): 213-219.
XIAO Y N, FAN X Y, CHEN W F. Research on person re-identification based on integrating local features under multi-branches[J]. Computer Engineering and Applications,2021, 57(18): 213-219.
[20] LUO H, JIANG W, GU Y, et al. A strong baseline and batch normalization neck for deep person re-identification[J]. IEEE Transactions on Multimedia, 2019, 22(10): 2597-2609.
[21] ZHONG Z, ZHENG L, KANG G, et al. Random erasing data augmentation[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020: 13001-13008.
[22] DAI Z, CHEN M, GU X, et al. Batch dropblock network for person re-identification and beyond[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019: 3691-3701.
[23] YE M, RUAN W, DU B, et al. Channel augmented joint learning for visible-infrared recognition[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021: 13567-13576.
[24] LING Y, ZHONG Z, LUO Z, et al. Class-aware modality mix and center-guided metric learning for visible-thermal person re-identification[C]//Proceedings of the 28th ACM International Conference on Multimedia, 2020: 889-897.
[25] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[26] WANG X, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7794-7803.
[27] CUBUK E D, ZOPH B, MANE D, et al. AutoAugment: learning augmentation strategies from data[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 113-123. |