Open Access

ARTICLE

Pseudo Label Purification with Dual Contrastive Learning for Unsupervised Vehicle Re-Identification

Jiyang Xu¹, Qi Wang^1,*, Xin Xiong², Weidong Min^1,3, Jiang Luo⁴, Di Gai¹, Qing Han^1,3

1 School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China
2 The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
3 Institute of Metaverse, Nanchang University, Nanchang, 330031, China
4 Jiangxi Fangxing Technology Company Limited, Nanchang, 330025, China

* Corresponding Author: Qi Wang. Email:

Computers, Materials & Continua 2025, 82(3), 3921-3941. https://doi.org/10.32604/cmc.2024.058586

Received 15 September 2024; Accepted 13 December 2024; Issue published 06 March 2025

Abstract

The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information. Due to the higher similarity in appearance between vehicles compared to pedestrians, pseudo-labels generated through clustering are ineffective in mitigating the impact of noise, and the feature distance between inter-class and intra-class has not been adequately improved. To address the aforementioned issues, we design a dual contrastive learning method based on knowledge distillation. During each iteration, we utilize a teacher model to randomly partition the entire dataset into two sub-domains based on clustering pseudo-label categories. By conducting contrastive learning between the two student models, we extract more discernible vehicle identity cues to improve the problem of imbalanced data distribution. Subsequently, we propose a context-aware pseudo label refinement strategy that leverages contextual features by progressively associating granularity information from different bottleneck blocks. To produce more trustworthy pseudo-labels and lessen noise interference during the clustering process, the context-aware scores are obtained by calculating the similarity between global features and contextual ones, which are subsequently added to the pseudo-label encoding process. The proposed method has achieved excellent performance in overcoming label noise and optimizing data distribution through extensive experimental results on publicly available datasets.

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Keywords

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