[1] 胡晓强, 魏丹, 王子阳, 等.基于时空关注区域的视频行人重识别[J].计算机工程, 2021, 47(6):277-283. HU X Q, WEI D, WANG Z Y, et al.Person re-identification in video based on spatial-temporal attention region[J]. Computer Engineering, 2021, 47(6):277-283.(in Chinese) [2] 罗浩, 姜伟, 范星, 等.基于深度学习的行人重识别研究进展[J].自动化学报, 2019, 45(11):2032-2049. LUO H, JIANG W, FAN X, et al.A survey on deep learning based person re-identification[J].Acta Automatica Sinica, 2019, 45(11):2032-2049.(in Chinese) [3] WU D, ZHENG S J, ZHANG X P, et al.Deep learning-based methods for person re-identification:a comprehensive review[J].Neurocomputing, 2019, 337:354-371. [4] CHEN G Y, LIN C Z, REN L L, et al.Self-critical attention learning for person re-identification[C]//Proceedings of IEEE/CVF International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2019:9636-9645. [5] 王粉花, 赵波, 黄超, 等.基于多尺度和注意力融合学习的行人重识别[J].电子与信息学报, 2020, 42(12):3045-3052. WANG F H, ZHAO B, HUANG C, et al.Person re-identification based on multi-scale network attention fusion[J].Journal of Electronics & Information Technology, 2020, 42(12):3045-3052.(in Chinese) [6] LI W, ZHU X T, GONG S G.Harmonious attention network for person re-identification[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:2285-2294. [7] 王坤峰, 王飞跃, 李雪松, 等.基于姿态和注意力机制的行人再识别方法, 系统, 装置:CN110659589A[P].2020-01-07. WANG K F, WANG F Y, LI X S, et al.Person re-identification method, system and device based on posture and attention mechanism:CN110659589A[P].2020-01-07.(in Chinese) [8] WANG X L, SHRIVASTAVA A, GUPTA A.A-fast-RCNN:hard positive generation via adversary for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:3039-3048. [9] 刘紫燕, 万培佩.基于注意力机制的行人重识别特征提取方法[J].计算机应用, 2020, 40(3):672-676. LIU Z Y, WAN P P.Pedestrian re-identification feature extraction method based on attention mechanism[J].Journal of Computer Applications, 2020, 40(3):672-676.(in Chinese) [10] CHOE J, SHIM H.Attention-based dropout layer for weakly supervised object localization[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:2214-2223. [11] DAI Z Z, CHEN M Q, GU X D, et al.Batch DropBlock network for person re-identification and beyond[C]//Proceedings of IEEE/CVF International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2019:3690-3700. [12] WANG Q L, WU B G, ZHU P F, et al.ECA-net:efficient channel attention for deep convolutional neural networks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2020:11531-11539. [13] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:770-778. [14] SZEGEDY C, VANHOUCKE V, IOFFE S, et al.Rethinking the inception architecture for computer vision[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:2818-2826. [15] HERMANS A, BEYER L, LEIBE B.In defense of the triplet loss for person re-identification[EB/OL].[2021-06-05].http://arxiv.org/abs/1703.07737. [16] SUN Y F, ZHENG L, YANG Y, et al.Beyond part models:person retrieval with refined part pooling (and a strong convolutional baseline)[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2018:501-518. [17] HU J, SHEN L, SUN G.Squeeze-and-excitation networks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:7132-7141. [18] ZHENG L, SHEN L Y, TIAN L, et al.Scalable person re-identification:a benchmark[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2015:1116-1124. [19] ZHENG Z D, ZHENG L, YANG Y.Unlabeled samples generated by GAN improve the person re-identification baseline in vitro[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2017:3774-3782. [20] SUN Y F, ZHENG L, DENG W J, et al.SVDNet for pedestrian retrieval[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2017:3820-3828. [21] ZHONG Z, ZHENG L, KANG G L, et al.Random erasing data augmentation[C]//Proceedings of AAAI Conference on Artificial Intelligence.[S.1.]:AAAI Press, 2020, 34(7):13001-13008. [22] WANG C, ZHANG Q, HUANG C, et al.Mancs:a multi-task attentional network with curriculum sampling for person re-identification[C]//Proceedings of Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2018:384-400. [23] ZHENG M, KARANAM S, WU Z Y, et al.re-identification with consistent attentive Siamese networks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:5728-5737. [24] WANG G A, YANG S, LIU H Y, et al.High-order information matters:learning relation and topology for occluded person re-identification[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2020:6448-6457. [25] JIN X, LAN C L, ZENG W J, et al.Style normalization and restitution for generalizable person re-identification[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, IEEE Press, 2020:3140-3149. [26] WOO S, PARK J, LEE J Y, et al.CBAM:Convolutional block attention module[C]//Proceedings of the 15th European Conference on Computer Vision.Berlin, Germany:Springer, 2018:3-19. |