PMD-Transformer: A Domain Generalization Approach for Person Re-Identification
Domain generalization person re-identification (DG-ReID) aims to address the performance degradation caused by domain shift between the training domain and unseen target domains. Transformers often demonstrate better generalization ability than CNNs due to their self-attention mechanism and capacity...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11015796/ |
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| author | Xingguo Jiang Ling Yu Guojun Lin Yuchao Zhang |
| author_facet | Xingguo Jiang Ling Yu Guojun Lin Yuchao Zhang |
| author_sort | Xingguo Jiang |
| collection | DOAJ |
| description | Domain generalization person re-identification (DG-ReID) aims to address the performance degradation caused by domain shift between the training domain and unseen target domains. Transformers often demonstrate better generalization ability than CNNs due to their self-attention mechanism and capacity for capturing global features. However, the supervised learning strategy on the source domain makes the Transformer-based ReID model inevitably overfit the bias in some regions. Therefore, a transformer model (PMD Transformer) for DG-ReID is proposed. First, by introducing relative position coding, it prevents the person image block from losing its relative position information when passing through the attention mechanism and improves the extraction of person features. Second, in order to solve the excessive attention of the model to the features of some regions and thus the overfitting phenomenon, the part mask attention (PMA) module is designed to limit the scope of the attention mechanism and improve the computational efficiency. Finally, a new feedforward network (DRFNN) is used to enhance the mastery of spatial information and further improve the generalization performance of the model. The experimental results show that, in the setting where Market is the source domain and Duke is the target domain, the average precision (mAP) and Rank-1 metrics increased by 12.5% and 9.2%, respectively, compared to the optimal algorithm. In the setting where Duke is the source domain and CUHK03-NP is the target domain, the mAP and Rank-1 metrics increased by 10.5% and 8.6%, respectively. The experiments demonstrate that the proposed model exhibits outstanding generalization ability in DG-ReID. |
| format | Article |
| id | doaj-art-b1bc0cf1084942978072dc57bc6f4a4a |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b1bc0cf1084942978072dc57bc6f4a4a2025-08-20T02:03:47ZengIEEEIEEE Access2169-35362025-01-0113931789318910.1109/ACCESS.2025.357392111015796PMD-Transformer: A Domain Generalization Approach for Person Re-IdentificationXingguo Jiang0https://orcid.org/0009-0006-9623-8479Ling Yu1https://orcid.org/0009-0002-6317-2275Guojun Lin2https://orcid.org/0000-0002-8707-5720Yuchao Zhang3https://orcid.org/0009-0008-8845-3093School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, ChinaDomain generalization person re-identification (DG-ReID) aims to address the performance degradation caused by domain shift between the training domain and unseen target domains. Transformers often demonstrate better generalization ability than CNNs due to their self-attention mechanism and capacity for capturing global features. However, the supervised learning strategy on the source domain makes the Transformer-based ReID model inevitably overfit the bias in some regions. Therefore, a transformer model (PMD Transformer) for DG-ReID is proposed. First, by introducing relative position coding, it prevents the person image block from losing its relative position information when passing through the attention mechanism and improves the extraction of person features. Second, in order to solve the excessive attention of the model to the features of some regions and thus the overfitting phenomenon, the part mask attention (PMA) module is designed to limit the scope of the attention mechanism and improve the computational efficiency. Finally, a new feedforward network (DRFNN) is used to enhance the mastery of spatial information and further improve the generalization performance of the model. The experimental results show that, in the setting where Market is the source domain and Duke is the target domain, the average precision (mAP) and Rank-1 metrics increased by 12.5% and 9.2%, respectively, compared to the optimal algorithm. In the setting where Duke is the source domain and CUHK03-NP is the target domain, the mAP and Rank-1 metrics increased by 10.5% and 8.6%, respectively. The experiments demonstrate that the proposed model exhibits outstanding generalization ability in DG-ReID.https://ieeexplore.ieee.org/document/11015796/Person re-identificationdomain generalizationtransformerrelative positional encoding |
| spellingShingle | Xingguo Jiang Ling Yu Guojun Lin Yuchao Zhang PMD-Transformer: A Domain Generalization Approach for Person Re-Identification IEEE Access Person re-identification domain generalization transformer relative positional encoding |
| title | PMD-Transformer: A Domain Generalization Approach for Person Re-Identification |
| title_full | PMD-Transformer: A Domain Generalization Approach for Person Re-Identification |
| title_fullStr | PMD-Transformer: A Domain Generalization Approach for Person Re-Identification |
| title_full_unstemmed | PMD-Transformer: A Domain Generalization Approach for Person Re-Identification |
| title_short | PMD-Transformer: A Domain Generalization Approach for Person Re-Identification |
| title_sort | pmd transformer a domain generalization approach for person re identification |
| topic | Person re-identification domain generalization transformer relative positional encoding |
| url | https://ieeexplore.ieee.org/document/11015796/ |
| work_keys_str_mv | AT xingguojiang pmdtransformeradomaingeneralizationapproachforpersonreidentification AT lingyu pmdtransformeradomaingeneralizationapproachforpersonreidentification AT guojunlin pmdtransformeradomaingeneralizationapproachforpersonreidentification AT yuchaozhang pmdtransformeradomaingeneralizationapproachforpersonreidentification |