Page 278 - 《软件学报》2020年第9期
P. 278

祁磊  等:弱监督场景下的行人重识别研究综述                                                           2899


         [34]    Wei LH, Zhang SL, Yao HT, Gao W, Tian Q. Glad: Global-local-alignment descriptor for pedestrian retrieval. In: Proc. of the 25th
             ACM Int’l Conf. on Multimedia. ACM, 2017. 420−428.
         [35]    Su C, Li JN, Zhang SL, Xing JL, Gao W, Tian Q. Pose-Driven deep convolutional model for person re-identification. In: Proc. of
             the IEEE Int’l Conf. on Computer Vision. 2017. 3960−3969.
         [36]    Ma AJ, Yuen  PC, Li  JW.  Domain  transfer  support vector ranking  for  person re-identification without target camera label
             information. In: Proc. of the IEEE Int’l Conf. on Computer Vision. 2013. 3567−3574.
         [37]    Wang XJ, Zheng WS, Li X, Zhang JG. Cross-Scenario transfer person reidentification. IEEE Trans. on Circuits and Systems for
             Video Technology, 2015,26(8):1447−1460.
         [38]    Peng PX, Xiang T, Wang YW, Pontil M, Gong SG, Huang TJ, Tian YH. Unsupervised cross-dataset transfer learning for person
             re-identification. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2016. 1306−1315.
         [39]    Kodirov E, Xiang T, Fu ZY, Gong SG. Person re-identification by unsupervised l 1 graph learning. In: Proc. of the European Conf.
             on Computer Vision. Cham: Springer, 2016. 178−195.
         [40]    Kodirov E, Xiang T, Gong SG. Dictionary learning with iterative laplacian regularisation for unsupervised person re-identification.
             BMVC, 2015,3:8.
         [41]    Zhao R, Ouyang WL,  Wang XG. Unsupervised  salience learning for  person  re-identification. In:  Proc.  of  the IEEE  Conf.  on
             Computer Vision and Pattern Recognition. 2013. 3586−3593.
         [42]    Yang Y, Wen LY, Lyu SW, Li SZ. Unsupervised learning of multi-level descriptors for person re-identification. In: Proc. of the
             31st AAAI Conf. on Artificial Intelligence. 2017.
         [43]    Yu HX, Zheng WS, Wu AC, Guo XW, Gong SG, Lai JH. Unsupervised person re-identification by soft multilabel learning. In: Proc.
             of the IEEE Conf. on Computer Vision and Pattern Recognition. 2019. 2148−2157.
         [44]    Yang QZ, Yu HX, Wu AC, Zheng WS. Patch-Based discriminative feature learning for unsupervised person re-identification. In:
             Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2019. 3633−3642.
         [45]    Dosovitskiy A, Springenberg JT, Riedmiller M, Brox T. Discriminative unsupervised feature learning with convolutional neural
             networks. In: Proc. of the Advances in Neural Information Processing Systems. 2014. 766−774.
         [46]    Wang JY, Zhu XT, Gong SG, Li W. Transferable joint attribute-identity deep learning for unsupervised person re-identification. In:
             Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2018. 2275−2284.
         [47]    Lv  JM, Chen WH, Li Q, Yang C. Unsupervised cross-dataset  person  re-identification  by  transfer learning of  spatial-temporal
             patterns. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2018. 7948−7956.
         [48]    Fu Y, Wei YC, Wang GS, Zhou YQ, Shi HH, Huang TS. Self-Similarity grouping: A simple unsupervised cross domain adaptation
             approach for person re-identification. In: Proc. of the IEEE Int’l Conf. on Computer Vision. 2019. 6112−6121.
         [49]    Ester M, Kriegel HP, Sander J, Xu XW. A density-based algorithm for discovering clusters in large spatial databases with noise.
             KDD, 1996,96(34):226−231.
         [50]    Zhang  XY,  Cao JW, Shen  CH, You MY. Self-Training with progressive  augmentation for unsupervised  cross-domain person
             re-identification. In: Proc. of the IEEE Int’l Conf. on Computer Vision. 2019. 8222−8231.
         [51]    Campello RJGB, Moulavi D, Sander J. Density-Based clustering based on hierarchical density estimates. In: Proc. of the Pacific-
             Asia Conf. on Knowledge Discovery and Data Mining. Berlin, Heidelberg: Springer-Verlag, 2013. 160−172.
         [52]    Lin YT, Dong XY, Zheng L, Yan Y, Yang Y. A bottom-up clustering approach to unsupervised person re-identification. In: Proc.
             of the AAAI Conf. on Artificial Intelligence, Vol.33. 2019. 8738−8745.
         [53]    Tang HT, Zhao YR, Lu HT. Unsupervised person re-identification with iterative self-supervised domain adaptation. In: Proc. of the
             IEEE Conf. on Computer Vision and Pattern Recognition Workshops. 2019.
         [54]    Yang FX, Li K, Zhong Z, Luo ZM, Sun X, Cheng H, Guo XW, Huang FY, Ji RR, Li SZ. Asymmetric co-teaching for unsupervised
             cross domain person re-identification. arXiv preprint arXiv:1912.01349, 2019.
         [55]    Ding GD, Khan S,  Tang  ZM,  Zhang J, Porikli F. Towards better validity:  Dispersion based  clustering  for unsupervised person
             re-identification. arXiv preprint arXiv:1906.01308, 2019.
         [56]    Huang Y, Wu Q, Xu JS, Zhong Y. SBSGAN: Suppression of inter-domain background shift for person re-identification. In: Proc. of
             the IEEE Int’l Conf. on Computer Vision. 2019. 9527−9536.
   273   274   275   276   277   278   279   280   281   282   283