The emergence of large Vision-Language Models (VLMs) has recently established new baselines in image classification across multiple domains. However, the performance of VLMs in the specific task of artwork classification, particularly art style classification of paintings - a domain traditionally mastered by art historians - has not been explored yet. Artworks pose a unique challenge compared to natural images due to their inherently complex and diverse structures, characterized by variable compositions and styles. Art historians have long studied the unique aspects of artworks, with style prediction being a crucial component of their discipline. This paper investigates whether large VLMs, which integrate visual and textual data, can effectively predict the art historical attributes of paintings. We conduct an in-depth analysis of four VLMs, namely CLIP, LLaVA, OpenFlamingo, and GPT-4o, focusing on zero-shot classification of art style, author and time period using two public benchmarks of artworks. Additionally, we present ArTest, a well-curated test set of artworks, including pivotal paintings studied by art historians.

本研究探讨了大型视觉语言模型（VLMs）在艺术作品分类，特别是绘画风格分类中的表现，这一领域通常由艺术史学家主导。通过分析四种VLMs并利用两个公共艺术作品基准，我们发现这些模型在艺术风格、作者和时期的零样本分类中表现出一定的能力，推动了艺术作品分类的研究进展。

大型视觉语言模型是否掌握了艺术史?