Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style
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 ...
| Authors | Marvin Limpijankit, Milad Alshomary, Yassin Oulad Daoud et al. |
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| Primary category | cs.CV |
| Published | 11 March 2026 |
| Last updated at source | 19 May 2026 |
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