Page 277 - 《软件学报》2020年第9期
P. 277
2898 Journal of Software 软件学报 Vol.31, No.9, September 2020
[11] Bazzani L, Cristani M, Murino V. Symmetry-Driven accumulation of local features for human characterization and re-identification.
Computer Vision and Image Understanding, 2013,117(2):130−144.
[12] Gray D, Tao H. Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Proc. of the European Conf.
on Computer Vision. Berlin, Heidelberg: Springer-Verlag, 2008. 262−275.
[13] Paisitkriangkrai S, Shen CH, Van Den Hengel A. Learning to rank in person re-identification with metric ensembles. In: Proc. of
the IEEE Conf. on Computer Vision and Pattern Recognition. 2015. 1846−1855.
[14] Liao SC, Li SZ. Efficient PSD constrained asymmetric metric learning for person re-identification. In: Proc. of the IEEE Int’l Conf.
on Computer Vision. 2015. 3685−3693.
[15] Xiong F, Gou M, Camps O, Sznaier M. Person re-identification using kernel-based metric learning methods. In: Proc. of the
European Conf. on Computer Vision. Cham: Springer-Verlag, 2014. 1−16.
[16] Liu CX, Change Loy C, Gong SG, Wang GJ. Pop: Person re-identification post-rank optimization. In: Proc. of the IEEE Int’l Conf.
on Computer Vision. 2013. 441−448.
[17] Karanam S, Li Y, Radke RJ. Person re-identification with discriminatively trained viewpoint invariant dictionaries. In: Proc. of the
IEEE Int’l Conf. on Computer Vision. 2015. 4516−4524.
[18] Zhang L, Xiang T, Gong SG. Learning a discriminative null space for person re-identification. In: Proc. of the IEEE Conf. on
Computer Vision and Pattern Recognition. 2016. 1239−1248.
[19] Chen DP, Yuan ZJ, Hua G, Zheng NN, Wang JD. Similarity learning on an explicit polynomial kernel feature map for person
re-identification. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2015. 1565−1573.
[20] Chen BH, Deng WH, Hu JN. Mixed high-order attention network for person re-identification. In: Proc. of the IEEE Int’l Conf. on
Computer Vision. 2019. 371−381.
[21] Xia BN, Gong Y, Zhang YZ, Poellabauer C. Second-Order non-local attention networks for person re-identification. In: Proc. of the
IEEE Int’l Conf. on Computer Vision. 2019. 3760−3769.
[22] Fang PF, Zhou JM, Roy SK, Petersson L, Harandi M. Bilinear attention networks for person retrieval. In: Proc. of the IEEE Int’l
Conf. on Computer Vision. 2019. 8030−8039.
[23] Chen TL, Ding SJ, Xie JY, Yuan Y, Chen WY, Yang Y, Ren Z, Wang ZY. Abd-Net: Attentive but diverse person re-identification.
In: Proc. of the IEEE Int’l Conf. on Computer Vision. 2019. 8351−8361.
[24] Chen GY, Lin CZ, Ren LL, Lu JW, Zhou J. Self-Critical attention learning for person re-identification. In: Proc. of the IEEE Int’l
Conf. on Computer Vision. 2019. 9637−9646.
[25] Tay CP, Roy S, Yap KH. AANet: Attribute attention network for person re-identifications. In: Proc. of the IEEE Conf. on
Computer Vision and Pattern Recognition. 2019. 7134−7143.
[26] Zheng M, Karanam S, Wu ZY, Radke RJ. Re-Identification with consistent attentive siamese networks. In: Proc. of the IEEE Conf.
on Computer Vision and Pattern Recognition. 2019. 5735−5744.
[27] Li W, Zhu XT, Gong SG. Harmonious attention network for person re-identification. In: Proc. of the IEEE Conf. on Computer
Vision and Pattern Recognition. 2018. 2285−2294.
[28] Si JL, Zhang HG, Li CG, Kuen J, Kong XF, Kot AC, Wang G. Dual attention matching network for context-aware feature sequence
based person re-identification. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2018. 5363−5372.
[29] Hermans A, Beyer L, Leibe B. In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737, 2017.
[30] Zhang YY, Zhong QY, Ma L, Xie D, Pu SL. Learning incremental triplet margin for person re-identification. In: Proc. of the AAAI
Conf. on Artificial Intelligence, Vol.33. 2019. 9243−9250.
[31] Chen WH, Chen XT, Zhang JG, Huang KQ. Beyond triplet loss: A deep quadruplet network for person re-identification. In: Proc.
of the IEEE Conf. on Computer Vision and Pattern Recognition. 2017. 403−412.
[32] Zheng ZD, Zheng L, Yang Y. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proc.
of the IEEE Int’l Conf. on Computer Vision. 2017. 3754−3762.
[33] Sun YF, Zheng L, Yang Y, Tian Q, Wang SJ. Beyond part models: Person retrieval with refined part pooling (and a strong
convolutional baseline). In: Proc. of the European Conf. on Computer Vision (ECCV). 2018. 480−496.