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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2405.07459 |
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| _version_ | 1866911684526866432 |
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| author | Deng, Yuchuan Hu, Zhanpeng Xin, Zijie Deng, Chuang Zhao, Qijun |
| author_facet | Deng, Yuchuan Hu, Zhanpeng Xin, Zijie Deng, Chuang Zhao, Qijun |
| contents | Text-based person search (TBPS) aims to retrieve specific images of individuals from large datasets using textual descriptions. Existing TBPS methods focus primarily on identifying explicit positive attributes, often neglecting the critical role of negative descriptions. This oversight can lead to false positives, where images that should be excluded based on negative descriptions are incorrectly included, due to partial alignment with the positive criteria. To address this limitation, we propose the Dual Attribute Prompt Learning (DAPL) framework, which incorporates both positive and negative descriptions to improve the interpretative accuracy of vision-language models in TBPS tasks. DAPL combines Dual Image-Attribute Contrastive (DIAC) learning with Sensitive Image-Attribute Matching (SIAM) learning to enhance the detection of previously unseen attributes. Furthermore, to achieve a balance between coarse and fine-grained alignment of visual and textual embeddings, we introduce the Dynamic Token-wise Similarity (DTS) loss. This loss function refines the representation of both matching and non-matching descriptions at the token level, providing more precise and adaptable similarity assessments, and ultimately improving the accuracy of the matching process. Empirical results demonstrate that DAPL outperforms state-of-the-art methods, enhancing both precision and robustness in TBPS tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07459 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DAPL: Integration of Positive and Negative Descriptions in Text-Based Person Search Deng, Yuchuan Hu, Zhanpeng Xin, Zijie Deng, Chuang Zhao, Qijun Computer Vision and Pattern Recognition Text-based person search (TBPS) aims to retrieve specific images of individuals from large datasets using textual descriptions. Existing TBPS methods focus primarily on identifying explicit positive attributes, often neglecting the critical role of negative descriptions. This oversight can lead to false positives, where images that should be excluded based on negative descriptions are incorrectly included, due to partial alignment with the positive criteria. To address this limitation, we propose the Dual Attribute Prompt Learning (DAPL) framework, which incorporates both positive and negative descriptions to improve the interpretative accuracy of vision-language models in TBPS tasks. DAPL combines Dual Image-Attribute Contrastive (DIAC) learning with Sensitive Image-Attribute Matching (SIAM) learning to enhance the detection of previously unseen attributes. Furthermore, to achieve a balance between coarse and fine-grained alignment of visual and textual embeddings, we introduce the Dynamic Token-wise Similarity (DTS) loss. This loss function refines the representation of both matching and non-matching descriptions at the token level, providing more precise and adaptable similarity assessments, and ultimately improving the accuracy of the matching process. Empirical results demonstrate that DAPL outperforms state-of-the-art methods, enhancing both precision and robustness in TBPS tasks. |
| title | DAPL: Integration of Positive and Negative Descriptions in Text-Based Person Search |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.07459 |