On the Explainability of Vision-Language Models in Art History

Fuente: arXiv
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Main Author: Schneider, Stefanie
Format: Preprint
Published: 2026
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author Schneider, Stefanie
author_facet Schneider, Stefanie
contents Vision-Language Models (VLMs) transfer visual and textual data into a shared embedding space. In so doing, they enable a wide range of multimodal tasks, while also raising critical questions about the nature of machine 'understanding.' In this paper, we examine how Explainable Artificial Intelligence (XAI) methods can render the visual reasoning of a VLM - namely, CLIP - legible in art-historical contexts. To this end, we evaluate seven methods, combining zero-shot localization experiments with human interpretability studies. Our results indicate that, while these methods capture some aspects of human interpretation, their effectiveness hinges on the conceptual stability and representational availability of the examined categories.
format Preprint
id arxiv_https___arxiv_org_abs_2602_20853
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle On the Explainability of Vision-Language Models in Art History
Schneider, Stefanie
Computer Vision and Pattern Recognition
Vision-Language Models (VLMs) transfer visual and textual data into a shared embedding space. In so doing, they enable a wide range of multimodal tasks, while also raising critical questions about the nature of machine 'understanding.' In this paper, we examine how Explainable Artificial Intelligence (XAI) methods can render the visual reasoning of a VLM - namely, CLIP - legible in art-historical contexts. To this end, we evaluate seven methods, combining zero-shot localization experiments with human interpretability studies. Our results indicate that, while these methods capture some aspects of human interpretation, their effectiveness hinges on the conceptual stability and representational availability of the examined categories.
title On the Explainability of Vision-Language Models in Art History
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2602.20853