(80)
Y.-J. Li, Y.-C. Chen, Y.-Y. Lin, X. Du, and Y.-C. F. Wang, “Recover and identify: A generative
dual model for cross-resolution person re-identification,” in ICCV, 2019, pp. 8090–8099.
(81) Z. Zheng, L. Zheng, and Y. Yang, “Unlabeled samples generated by gan improve the person re-
identification baseline in vitro,” in ICCV, 2017, pp. 3754–3762.
(82)
T. Yu, D. Li, Y. Yang, T. Hospedales, and T. Xiang, “Robust person re-identification by
modelling feature uncertainty,” in ICCV, 2019, pp. 552–561.
(83)
M. Ye and P. C. Yuen, “Purifynet: A robust person reidentification model with noisy labels,”
IEEE TIFS, 2020.
(84) A. Das, A. Chakraborty, and A. K. Roy-Chowdhury, “Consistent re-identification in a camera
network,” in ECCV, 2014.
(85) N. Martinel, A. Das, C. Micheloni, and A. K. Roy-Chowdhury, “Temporal model adaptation for
person re-identification,” in ECCV, 2016.
(86) A. Das, R. Panda, and A. Roy-Chowdhury, “Active image pair selection for continuous person
re-identification,” in ICIP, 2015.
(87) A. Das, R. Panda, and A. K. Roy-Chowdhury, “Continuous adaptation of multi-camera person
identification models through sparse non-redundant representative selection,” CVIU, vol.
15.2017.
(88) Mang Ye, JianbingShen, Gaojie Lin, Tao Xiang, Ling Shao, Steven C. H. Hoi, Deep Learning
for Person Re-identification:A Survey and Outlook, IEEE TRANSACTIONS ON PATTERN
ANALYSIS AND MACHINE INTELLIGENCE-2021
(89) C. Su, S. Zhang, J. Xing, W. Gao, and Q. Tian, ``Multi-type attributes driven multi-camera
person re-identi_cation,'' Pattern Recognit., vol. 75, pp. 77_89, Mar. 2018.
(90) T. Elsken, J. H. Metzen, and F. Hutter, ``Correction to: Neural architecture search,'' in
Automated Machine Learning: Methods, Systems, Challenges, F. Hutter, L. Kotthoff, and J.
Vanschoren, Eds. Cham, Switzerland: Springer, 2019.
(91) W. Li, X. Zhu, and S. Gong, “Harmonious attention network for person re-identification,” in
CVPR, 2018, pp. 2285–2294.
(92) K. Zhou, Y. Yang, A. Cavallaro, and T. Xiang, “Omni-scale feature learning for person re-
identification,” in ICCV, 2019, pp. 3702– 3712.
(93) A. Wu, W.-S. Zheng, X. Guo, and J.-H. Lai, “Distilled person reidentification: Towards a more
scalable system,” in CVPR, 2019,pp. 1187–1196.
(94) D. Gray, S. Brennan, and H. Tao, ``Evaluating appearance models for recognition, reacquisition,
and tracking,'' in Proc. 10th Int. Workshop Perform. Eval. Tracking Surveill. (PETS), vol. 3,
2007, pp. 41_47.
(95) W. Li, R. Zhao, and X. Wang, ``Human reidenti_cation with transferred metric learning,'' in
Computer Vision_ACCV (Lecture Notes in Computer Science), vol. 7724. Berlin, Germany:
Springer, 2013, pp. 31_44.
(96) L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian, ``Scalable person re-
identi_cation:Abenchmark,'' in Proc. IEEE Int. Conf. Comput Vis. (ICCV), Dec. 2015, pp.
1116_1124.
(97) E. Ristani, F. Solera, R. Zou, R. Cucchiara, and C. Tomasi, ``Performance measures and a data
set for multi-target, multi-camera tracking,'' in Computer Vision_ECCV 2016 Workshops
(Lecture Notes in Computer Science), vol. 9914. Cham, Switzerland: Springer, 2016, pp. 17_35.
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