Multi Queue for Unsupervised Person Re-identification
Recently, cluster-based methods have achieved significant success in unsupervised re-ID tasks. The hierarchical clustering algorithm, exemplified by SpCL, has been widely adopted in unsupervised cross-domain adaptation and unsupervised learning. The momentum-based feature update mechanism in SpCL ha...
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| Vydáno v: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5 |
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06.04.2025
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| ISSN: | 2379-190X |
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| Abstract | Recently, cluster-based methods have achieved significant success in unsupervised re-ID tasks. The hierarchical clustering algorithm, exemplified by SpCL, has been widely adopted in unsupervised cross-domain adaptation and unsupervised learning. The momentum-based feature update mechanism in SpCL has been integrated into various algorithms, achieving notable results in subsequent studies. In this paper, we propose a multi-queue feature updating algorithm that stores feature vectors corresponding to person IDs in multiple queues. Random sampling is then applied to construct the negative sample matrix for contrastive loss, addressing the limitations of momentum-based updating methods. Additionally, we replace the static temperature coefficient in contrastive loss with a trainable temperature coefficient, enabling the model to automatically balance sensitivity between easy and hard samples. The code is available at https://github.com/bmfarer/multi-queue.git. |
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| AbstractList | Recently, cluster-based methods have achieved significant success in unsupervised re-ID tasks. The hierarchical clustering algorithm, exemplified by SpCL, has been widely adopted in unsupervised cross-domain adaptation and unsupervised learning. The momentum-based feature update mechanism in SpCL has been integrated into various algorithms, achieving notable results in subsequent studies. In this paper, we propose a multi-queue feature updating algorithm that stores feature vectors corresponding to person IDs in multiple queues. Random sampling is then applied to construct the negative sample matrix for contrastive loss, addressing the limitations of momentum-based updating methods. Additionally, we replace the static temperature coefficient in contrastive loss with a trainable temperature coefficient, enabling the model to automatically balance sensitivity between easy and hard samples. The code is available at https://github.com/bmfarer/multi-queue.git. |
| Author | Lin, Zhenyuan Dong, Yubo Li, Weikun Gao, Ang Liu, Danhua Xie, Shengyong |
| Author_xml | – sequence: 1 givenname: Zhenyuan surname: Lin fullname: Lin, Zhenyuan email: linzhenyuan@stu.xidian.edu.cn organization: Xidian University,Guangzhou Institute of Technology,Guangzhou,China – sequence: 2 givenname: Shengyong surname: Xie fullname: Xie, Shengyong email: 2416057194@qq.com organization: Guilin University Of Electronic Technology,School of Computer Science and Information Security,Guilin,China – sequence: 3 givenname: Danhua surname: Liu fullname: Liu, Danhua email: dhliu@xidian.edu.cn organization: Xidian University,School of Artificial Intelligence,Xian,China – sequence: 4 givenname: Weikun surname: Li fullname: Li, Weikun email: liweikun1105@163.com organization: Guilin University Of Electronic Technology,School of Computer Science and Information Security,Guilin,China – sequence: 5 givenname: Ang surname: Gao fullname: Gao, Ang email: anggao@stu.xidian.edu.cn organization: Xidian University,School of Artificial Intelligence,Xian,China – sequence: 6 givenname: Yubo surname: Dong fullname: Dong, Yubo email: ybdong@stu.xidian.edu.cn organization: Xidian University,School of Artificial Intelligence,Xian,China |
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| Snippet | Recently, cluster-based methods have achieved significant success in unsupervised re-ID tasks. The hierarchical clustering algorithm, exemplified by SpCL, has... |
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| SubjectTerms | Classification algorithms Clustering algorithms hierarchical clustering algorithm Image classification multi queue features updating algorithm Sensitivity Signal processing Signal processing algorithms Speech processing Temperature sensors Unsupervised learning unsupervised re-ID tasks updating feature vectors Vectors |
| Title | Multi Queue for Unsupervised Person Re-identification |
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