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祁磊  等:弱监督场景下的行人重识别研究综述                                                           2901


         [79]    Liu X, Song ML, Tao DC, Zhou XC, Chen C, Bu JJ. Semi-Supervised coupled dictionary learning for person re-identification. In:
             Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2014. 3550−3557.
         [80]    Wu AC, Zheng WS, Guo XW, Lai JH. Distilled person re-identification: Towards a more scalable system. In: Proc. of the IEEE
             Conf. on Computer Vision and Pattern Recognition. 2019. 1187−1196.
         [81]    Xin XM, Wang JJ, Xie RJ, Zhou SP, Huang WL, Zheng NN. Semi-Supervised person re-identification using multi-view clustering.
             Pattern Recognition, 2019,88:285−297.
         [82]    Wu Y, Lin YT, Dong XY, Yan Y, Bian W, Yang Y. Progressive learning for person re-identification with one example. IEEE Trans.
             on Image Processing, 2019,28(6):2872−2881.
         [83]    Li MX, Zhu XT, Gong SG. Unsupervised person re-identification by deep learning tracklet association. In: Proc. of the European
             Conf. on Computer Vision (ECCV). 2018. 737−753.
         [84]    Li  MX,  Zhu  XT, Gong SG.  Unsupervised tracklet  person re-identification. IEEE Trans. on Pattern  Analysis  and  Machine
             Intelligence, 2019.
         [85]    Qi L,  Wang L, Huo  J,  Shi YH, Gao Y.  Adversarial camera alignment  network for  unsupervised cross-camera  person
             re-identification. arXiv preprint arXiv:1908.00862, 2019.
         [86]    Qi L, Wang L, Huo J, Shi YH, Gao Y. Progressive cross-camera soft-label learning for semi-supervised person re-identification.
             arXiv preprint arXiv:1908.05669, 2019.
         [87]    Zhu XP, Zhu XT, Li MX, Murino V, Gong SG. Intra-Camera supervised person re-identification: A new benchmark. In: Proc. of
             the IEEE Int’l Conf. on Computer Vision Workshops. 2019.
         [88]    Zheng L, Shen LY, Tian L, Wang SJ, Wang JD, Tian Q. Scalable person re-identification: A benchmark. In: Proc. of the IEEE Int’l
             Conf. on Computer Vision. 2015. 1116−1124.
         [89]    Li W, Zhao R, Xiao T, Wang XG. Deepreid: Deep filter pairing neural network for person re-identification. In: Proc. of the IEEE
             Conf. on Computer Vision and Pattern Recognition. 2014. 152−159.
         [90]    Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D. Object detection with discriminatively trained part-based models. IEEE
             Trans. on Pattern Analysis and Machine Intelligence, 2009,32(9):1627−1645.
         [91]    Ristani E, Solera F, Zou R, Cucchiara R, Tomasi C. Performance measures and a data set for multi-target, multi-camera tracking. In:
             Proc. of the European Conf. on Computer Vision. Cham: Springer, 2016. 17−35.
         [92]    Ren SQ, He KM, Girshick R, Sun J. Faster r-CNN: Towards real-time object detection with region proposal networks. In: Proc. of
             the Advances in Neural Information Processing Systems. 2015. 91−99.
         [93]    Zhong Z, Zheng L, Cao DL, Li SZ. Re-Ranking person re-identification with k-reciprocal encoding. In: Proc. of the IEEE Conf. on
             Computer Vision and Pattern Recognition. 2017. 1318−1327.
         [94]    Hirzer M, Beleznai C, Roth PM, Bischof H. Person re-identification by descriptive and discriminative classification. In: Proc. of the
             Scandinavian Conf. on Image analysis. Berlin, Heidelberg: Springer-Verlag, 2011. 91−102.
         [95]    Wang TQ, Gong SG, Zhu XT, Wang SJ. Person re-identification by video ranking. In: Proc. of the European Conf. on Computer
             Vision. Cham: Springer-Verlag, 2014. 688−703.
         [96]    Zheng L, Bie Z, Sun YF, Wang JD, Su C, Wang SJ, Tian Q. Mars: A video benchmark for large-scale person re-identification. In:
             Proc. of the European Conf. on Computer Vision. Cham: Springer-Verlag, 2016. 868−884.
         [97]    Wu Y, Lin YT, Dong XY,  Yan Y, Ouyang  WL, Yang Y. Exploit the  unknown  gradually: One-shot  video-based  person
             re-identification by stepwise learning. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2018. 5177−5186.
         [98]    Dehghan A, Modiri Assari S, Shah M. Gmmcp tracker: Globally optimal generalized maximum multi clique problem for multiple
             object tracking. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2015. 4091−4099.
         [99]    Zeng KW. Hierarchical clustering-guided re-id with triplet loss. arXiv preprint arXiv:1910.12278, 2019.
        [100]    Wu GL, Zhu XT, Gong SG. Tracklet self-supervised learning for unsupervised person re-identification. In: Proc. of the AAAI Conf.
             on Artificial Intelligence. 2019.
        [101]    Xie QK, Zhou WG, Qi GJ, Tian Q, Li HQ. Progressive unsupervised person re-identification by tracklet association with spatio-
             temporal regularization. arXiv preprint arXiv:1910.11560, 2019.
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