Diversity feature constraint based on heterogeneous data for unsupervised person re-identification-Reference-Cited by-同舟云学术

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Diversity feature constraint based on heterogeneous data for unsupervised person re-identification

Published:2023-05 Issue:3 Volume:60 Page:103304
ISSN:0306-4573
Container-title:Information Processing & Management
language:en
Short-container-title:Information Processing & Management

Author:

Si Tongzhen,He Fazhi,Li Penglei,Song Yupeng,Fan Linkun

Publisher

Elsevier BV

Subject

Library and Information Sciences,Management Science and Operations Research,Computer Science Applications,Media Technology,Information Systems

Reference74 articles.

1. Bai, Z., Wang, Z., Wang, J., Hu, D., & Ding, E. (2021). Unsupervised multi-source domain adaptation for person re-identification. In IEEE conference on computer vision and pattern recognition (pp. 12914–12923).

2. Futuristic person re-identification over internet of biometrics things (IoBT): Technical potential versus practical reality;Behera;Pattern Recognition Letters,2021

3. Chen, H., Lagadec, B., & Bremond, F. (2021). Ice: Inter-instance contrastive encoding for unsupervised person re-identification. In IEEE international conference on computer vision (pp. 14960–14969).

4. Chen, H., Wang, Y., Lagadec, B., Dantcheva, A., & Bremond, F. (2021). Joint generative and contrastive learning for unsupervised person re-identification. In IEEE conference on computer vision and pattern recognition (pp. 2004–2013).

5. Structure-aware positional transformer for visible-infrared person re-identification;Chen;IEEE Transactions on Image Processing,2022

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