Image–text feature learning for unsupervised visible–infrared person re-identification
Visible–infrared person re-identification (VI-ReID) focuses on matching infrared and visible images of the same person. To reduce labeling costs, unsupervised VI-ReID (UVI-ReID) methods typically use clustering algorithms to generate pseudo-labels and iteratively optimize the model based on these ps...
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| Published in: | Image and vision computing Vol. 158; p. 105520 |
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01.05.2025
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| Abstract | Visible–infrared person re-identification (VI-ReID) focuses on matching infrared and visible images of the same person. To reduce labeling costs, unsupervised VI-ReID (UVI-ReID) methods typically use clustering algorithms to generate pseudo-labels and iteratively optimize the model based on these pseudo-labels. Although existing UVI-ReID methods have achieved promising performance, they often overlook the effectiveness of text semantics in inter-modality matching and modality-invariant feature learning. In this paper, we propose an image–text feature learning (ITFL) method, which not only leverages text semantics to enhance intra-modality identity-related learning but also incorporates text semantics into inter-modality matching and modality-invariant feature learning. Specifically, ITFL first performs modality-aware feature learning to generate pseudo-labels within each modality. Then, ITFL employs modality-invariant text modeling (MTM) to learn a text feature for each cluster in the visible modality, and utilizes inter-modality dual-semantics matching (IDM) to match inter-modality positive clusters. To obtain modality-invariant and identity-related image features, we not only introduce a cross-modality contrastive loss in ITFL to mitigate the impact of modality gaps, but also develop a text semantic consistency loss to further promote modality-invariant feature learning. Extensive experimental results on VI-ReID datasets demonstrate that ITFL not only outperforms existing unsupervised methods but also competes with some supervised approaches.
•We introduce text semantics into both inter-modality matching and learning.•We match inter-modality positive clusters based on dual semantics.•Text semantic consistency loss is introduced for modality-invariant learning. |
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| AbstractList | Visible–infrared person re-identification (VI-ReID) focuses on matching infrared and visible images of the same person. To reduce labeling costs, unsupervised VI-ReID (UVI-ReID) methods typically use clustering algorithms to generate pseudo-labels and iteratively optimize the model based on these pseudo-labels. Although existing UVI-ReID methods have achieved promising performance, they often overlook the effectiveness of text semantics in inter-modality matching and modality-invariant feature learning. In this paper, we propose an image–text feature learning (ITFL) method, which not only leverages text semantics to enhance intra-modality identity-related learning but also incorporates text semantics into inter-modality matching and modality-invariant feature learning. Specifically, ITFL first performs modality-aware feature learning to generate pseudo-labels within each modality. Then, ITFL employs modality-invariant text modeling (MTM) to learn a text feature for each cluster in the visible modality, and utilizes inter-modality dual-semantics matching (IDM) to match inter-modality positive clusters. To obtain modality-invariant and identity-related image features, we not only introduce a cross-modality contrastive loss in ITFL to mitigate the impact of modality gaps, but also develop a text semantic consistency loss to further promote modality-invariant feature learning. Extensive experimental results on VI-ReID datasets demonstrate that ITFL not only outperforms existing unsupervised methods but also competes with some supervised approaches.
•We introduce text semantics into both inter-modality matching and learning.•We match inter-modality positive clusters based on dual semantics.•Text semantic consistency loss is introduced for modality-invariant learning. |
| ArticleNumber | 105520 |
| Author | Pang, Zhiqi Guo, Jifeng |
| Author_xml | – sequence: 1 givenname: Jifeng surname: Guo fullname: Guo, Jifeng email: guojifeng@guat.edu.cn organization: College of Computer Science and Engineering, Guilin University Of Aerospace Technology, Guilin, 541000, Guangxi, China – sequence: 2 givenname: Zhiqi orcidid: 0000-0003-0940-3351 surname: Pang fullname: Pang, Zhiqi email: 22b903055@stu.hit.edu.cn organization: Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China |
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| Keywords | Vision-language models Visible–infrared person re-identification Unsupervised learning Contrastive learning |
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