Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style
2026-03-11 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors studied how vision-language models (VLMs) recognize and predict artistic styles. They worked with art historians to see if the computer's reasoning matches how humans think about art styles. By breaking down how the models make predictions, they found that most of the model's key ideas about art are meaningful and relevant to style. Sometimes the model used unexpected clues, but experts could explain why those clues still helped it guess the style.
Vision-language modelsArtistic styleLatent-space decompositionVisual question answeringComputer visionArt historyConcept extractionCausal analysisVisual features
Authors
Marvin Limpijankit, Milad Alshomary, Yassin Oulad Daoud, Amith Ananthram, Tim Trombley, Elias Stengel-Eskin, Mohit Bansal, Noam M. Elcott, Kathleen McKeown
Abstract
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.