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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.11153 |
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| _version_ | 1866911057940840448 |
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| author | Ye, Hongfei Chen, Bin Liu, Wenxi Zhang, Yu Li, Zhao Ni, Dandan Chen, Hongyang |
| author_facet | Ye, Hongfei Chen, Bin Liu, Wenxi Zhang, Yu Li, Zhao Ni, Dandan Chen, Hongyang |
| contents | With the widespread adoption of large vision-language models, the capacity for color vision in these models is crucial. However, the color vision abilities of large visual-language models have not yet been thoroughly explored. To address this gap, we define a color vision testing task for large vision-language models and construct a dataset \footnote{Anonymous Github Showing some of the data https://anonymous.4open.science/r/color-vision-test-dataset-3BCD} that covers multiple categories of test questions and tasks of varying difficulty levels. Furthermore, we analyze the types of errors made by large vision-language models and propose fine-tuning strategies to enhance their performance in color vision tests. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assessing Color Vision Test in Large Vision-language Models Ye, Hongfei Chen, Bin Liu, Wenxi Zhang, Yu Li, Zhao Ni, Dandan Chen, Hongyang Computer Vision and Pattern Recognition Artificial Intelligence With the widespread adoption of large vision-language models, the capacity for color vision in these models is crucial. However, the color vision abilities of large visual-language models have not yet been thoroughly explored. To address this gap, we define a color vision testing task for large vision-language models and construct a dataset \footnote{Anonymous Github Showing some of the data https://anonymous.4open.science/r/color-vision-test-dataset-3BCD} that covers multiple categories of test questions and tasks of varying difficulty levels. Furthermore, we analyze the types of errors made by large vision-language models and propose fine-tuning strategies to enhance their performance in color vision tests. |
| title | Assessing Color Vision Test in Large Vision-language Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.11153 |