Unsupervised person reidentification via quantitative random selection for cluster centroid
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-03439-x.pdf
Reference45 articles.
1. Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: Person retrieval with refined part pooling. In: Proceedings of the European conference on computer vision
2. Su C, Li J, Zhang S, Xing J, Gao W, Tian Q (2017) Pose-driven deep convolutional model for person re-identification. In: ICCV
3. Tian H, Zhang X, Lan L, Luo Z (2019) Person re-identification via adaptive verification loss. Neurocomputing 359:93–101
4. Fan H, Zheng L, Yan C, Yang Y (2018) Unsupervised person re-identification Clustering and finetuning. ACM Trans Multimed Comput Commun Appl 14(4):1–18
5. Lin Y, Dong X, Zheng L, Yan Y, Yang Y (2019) A bottom-up clustering approach to unsupervised person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 8738–8745
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