Dual branch guided contrastive learning for unsupervised pedestrian re-identification
The current unsupervised pedestrian re-identification algorithms using residual networks can only extract rough global features, but it can’t adequately reflect subtle local features. In addition, the pseudo labels generated by clustering methods introduce noise, which will affect the performance of...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2025-06-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025125/ |
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| _version_ | 1849472562361270272 |
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| author | REN Hangjia LIANG Fengmei |
| author_facet | REN Hangjia LIANG Fengmei |
| author_sort | REN Hangjia |
| collection | DOAJ |
| description | The current unsupervised pedestrian re-identification algorithms using residual networks can only extract rough global features, but it can’t adequately reflect subtle local features. In addition, the pseudo labels generated by clustering methods introduce noise, which will affect the performance of feature discrimination. A dual branch guided contrastive learning method was proposed. Firstly, an effective feature extraction method was introduced, which divided the extracted features into global branches and local branches to improve the utilization of local information. Secondly, the consistency between global and local features was proposed to refine the pseudo labels for global feature prediction, utilizing the complementary relationship between local and global features, thereby effectively reducing the noise generated by pseudo label clustering. Finally, a contrastive learning module was proposed to perform contrastive learning on refined labels and improve the robustness of the model. The experimental results on the Market1501, DukeMTMC-ReID, and MSMT17 datasets validate the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-aeb8570cabcd4c45ac3dc450c45b4fa1 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-aeb8570cabcd4c45ac3dc450c45b4fa12025-08-20T03:24:30ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012025-06-014192102112430246Dual branch guided contrastive learning for unsupervised pedestrian re-identificationREN HangjiaLIANG FengmeiThe current unsupervised pedestrian re-identification algorithms using residual networks can only extract rough global features, but it can’t adequately reflect subtle local features. In addition, the pseudo labels generated by clustering methods introduce noise, which will affect the performance of feature discrimination. A dual branch guided contrastive learning method was proposed. Firstly, an effective feature extraction method was introduced, which divided the extracted features into global branches and local branches to improve the utilization of local information. Secondly, the consistency between global and local features was proposed to refine the pseudo labels for global feature prediction, utilizing the complementary relationship between local and global features, thereby effectively reducing the noise generated by pseudo label clustering. Finally, a contrastive learning module was proposed to perform contrastive learning on refined labels and improve the robustness of the model. The experimental results on the Market1501, DukeMTMC-ReID, and MSMT17 datasets validate the effectiveness of the proposed method.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025125/unsupervised pedestrian re-identificationglobal featurelocal featurelabel refinementcontrastive learning |
| spellingShingle | REN Hangjia LIANG Fengmei Dual branch guided contrastive learning for unsupervised pedestrian re-identification Dianxin kexue unsupervised pedestrian re-identification global feature local feature label refinement contrastive learning |
| title | Dual branch guided contrastive learning for unsupervised pedestrian re-identification |
| title_full | Dual branch guided contrastive learning for unsupervised pedestrian re-identification |
| title_fullStr | Dual branch guided contrastive learning for unsupervised pedestrian re-identification |
| title_full_unstemmed | Dual branch guided contrastive learning for unsupervised pedestrian re-identification |
| title_short | Dual branch guided contrastive learning for unsupervised pedestrian re-identification |
| title_sort | dual branch guided contrastive learning for unsupervised pedestrian re identification |
| topic | unsupervised pedestrian re-identification global feature local feature label refinement contrastive learning |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025125/ |
| work_keys_str_mv | AT renhangjia dualbranchguidedcontrastivelearningforunsupervisedpedestrianreidentification AT liangfengmei dualbranchguidedcontrastivelearningforunsupervisedpedestrianreidentification |