Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914578823118848 |
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| author | Limpijankit, Marvin Alshomary, Milad Daoud, Yassin Oulad Ananthram, Amith Trombley, Tim Spratt, Emily L. Filonenko, Anna Pivo, Hannah Stengel-Eskin, Elias Bansal, Mohit Elcott, Noam M. McKeown, Kathleen |
| author_facet | Limpijankit, Marvin Alshomary, Milad Daoud, Yassin Oulad Ananthram, Amith Trombley, Tim Spratt, Emily L. Filonenko, Anna Pivo, Hannah Stengel-Eskin, Elias Bansal, Mohit Elcott, Noam M. McKeown, Kathleen |
| contents | VLMs have become increasingly proficient at a range of computer vision tasks, such as visual question answering and object detection. This includes increasingly strong capabilities in the domain of art, from analyzing artwork to generation of art. In an interdisciplinary collaboration between computer scientists and art historians, we characterize the mechanisms underlying VLMs' ability to predict artistic style and assess the extent to which they align with the criteria art historians use to reason about artistic style. We employ a latent-space decomposition approach to identify concepts that drive art style prediction and conduct quantitative evaluations, causal analysis and assessment by art historians. Our findings indicate that 73% of the extracted concepts are judged by art historians to exhibit a coherent and semantically meaningful visual feature and 90% of concepts used to predict style of a given artwork were judged relevant. In cases where an irrelevant concept was used to successfully predict style, art historians identified possible reasons for its success; for example, the model might "understand" a concept in more formal terms, such as dark/light contrasts. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_11024 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style Limpijankit, Marvin Alshomary, Milad Daoud, Yassin Oulad Ananthram, Amith Trombley, Tim Spratt, Emily L. Filonenko, Anna Pivo, Hannah Stengel-Eskin, Elias Bansal, Mohit Elcott, Noam M. McKeown, Kathleen Computer Vision and Pattern Recognition Artificial Intelligence VLMs have become increasingly proficient at a range of computer vision tasks, such as visual question answering and object detection. This includes increasingly strong capabilities in the domain of art, from analyzing artwork to generation of art. In an interdisciplinary collaboration between computer scientists and art historians, we characterize the mechanisms underlying VLMs' ability to predict artistic style and assess the extent to which they align with the criteria art historians use to reason about artistic style. We employ a latent-space decomposition approach to identify concepts that drive art style prediction and conduct quantitative evaluations, causal analysis and assessment by art historians. Our findings indicate that 73% of the extracted concepts are judged by art historians to exhibit a coherent and semantically meaningful visual feature and 90% of concepts used to predict style of a given artwork were judged relevant. In cases where an irrelevant concept was used to successfully predict style, art historians identified possible reasons for its success; for example, the model might "understand" a concept in more formal terms, such as dark/light contrasts. |
| title | Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.11024 |