@Article{cmc.2024.058586,
AUTHOR = {Jiyang Xu, Qi Wang, Xin Xiong, Weidong Min, Jiang Luo, Di Gai, Qing Han},
TITLE = {Pseudo Label Purification with Dual Contrastive Learning for Unsupervised Vehicle Re-Identification},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {82},
YEAR = {2025},
NUMBER = {3},
PAGES = {3921--3941},
URL = {http://www.techscience.com/cmc/v82n3/59879},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2024.058586}
}