TY  - EJOU
AU  - Xu, Jiyang 
AU  - Wang, Qi 
AU  - Xiong, Xin 
AU  - Min, Weidong 
AU  - Luo, Jiang 
AU  - Gai, Di 
AU  - Han, Qing 

TI  - Pseudo Label Purification with Dual Contrastive Learning for Unsupervised Vehicle Re-Identification
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 82
IS  - 3
SN  - 1546-2226

AB  - 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.
KW  - Unsupervised vehicle re-identification; dual contrastive learning; pseudo label refinement; knowledge distillation

DO  - 10.32604/cmc.2024.058586