CoDoL: Conditional Domain Prompt Learning for Out-of-Distribution Generalization
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908546440888320 |
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| author | Zhang, Min Jiang, Bo Zhou, Jie Liu, Yimeng Lin, Xin |
| author_facet | Zhang, Min Jiang, Bo Zhou, Jie Liu, Yimeng Lin, Xin |
| contents | Recent advances in pre-training vision-language models (VLMs), e.g., contrastive language-image pre-training (CLIP) methods, have shown great potential in learning out-of-distribution (OOD) representations. Despite showing competitive performance, the prompt-based CLIP methods still suffer from: i) inaccurate text descriptions, which leads to degraded accuracy and robustness, and poses a challenge for zero-shot CLIP methods. ii) limited vision-language embedding alignment, which significantly affects the generalization performance. To tackle the above issues, this paper proposes a novel Conditional Domain prompt Learning (CoDoL) method, which utilizes readily-available domain information to form prompts and improves the vision-language embedding alignment for improving OOD generalization. To capture both instance-specific and domain-specific information, we further propose a lightweight Domain Meta Network (DMN) to generate input-conditional tokens for images in each domain. Extensive experiments on four OOD benchmarks (PACS, VLCS, OfficeHome and DigitDG) validate the effectiveness of our proposed CoDoL in terms of improving the vision-language embedding alignment as well as the out-of-distribution generalization performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15330 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoDoL: Conditional Domain Prompt Learning for Out-of-Distribution Generalization Zhang, Min Jiang, Bo Zhou, Jie Liu, Yimeng Lin, Xin Computer Vision and Pattern Recognition Recent advances in pre-training vision-language models (VLMs), e.g., contrastive language-image pre-training (CLIP) methods, have shown great potential in learning out-of-distribution (OOD) representations. Despite showing competitive performance, the prompt-based CLIP methods still suffer from: i) inaccurate text descriptions, which leads to degraded accuracy and robustness, and poses a challenge for zero-shot CLIP methods. ii) limited vision-language embedding alignment, which significantly affects the generalization performance. To tackle the above issues, this paper proposes a novel Conditional Domain prompt Learning (CoDoL) method, which utilizes readily-available domain information to form prompts and improves the vision-language embedding alignment for improving OOD generalization. To capture both instance-specific and domain-specific information, we further propose a lightweight Domain Meta Network (DMN) to generate input-conditional tokens for images in each domain. Extensive experiments on four OOD benchmarks (PACS, VLCS, OfficeHome and DigitDG) validate the effectiveness of our proposed CoDoL in terms of improving the vision-language embedding alignment as well as the out-of-distribution generalization performance. |
| title | CoDoL: Conditional Domain Prompt Learning for Out-of-Distribution Generalization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.15330 |