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
Hlavní autoři: Lin, Zhenyuan, Xie, Shengyong, Liu, Danhua, Li, Weikun, Gao, Ang, Dong, Yubo
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 06.04.2025
Témata:
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
ISSN:2379-190X
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Shrnutí: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.
ISSN:2379-190X
DOI:10.1109/ICASSP49660.2025.10889622