Abstract
Many Art History articles discuss artworks in general as well as specific parts of works, such as layout, iconography, or material culture. However, when viewing an artwork, it is not trivial to identify what different articles have said about the piece. Therefore, we propose ArtContext, a pipeline for taking a corpus of Open-Access Art History articles and Wikidata Knowledge and annotating Artworks with this information. We do this using a novel corpus collection pipeline, then learn a bespoke CLIP model adapted using Low-Rank Adaptation (LoRA) to make it domain-specific. We show that the new model, PaintingCLIP, which is weakly supervised by the collected corpus, outperforms CLIP and provides context for a given artwork. The proposed pipeline is generalisable and can be readily applied to numerous humanities areas.
Abstract (translated)
许多艺术史文章会讨论艺术品的总体情况以及作品的具体部分，如布局、图象学或物质文化。然而，在观看一件艺术品时，很难确定不同文章对该件作品的说法。因此，我们提出了ArtContext这一流程，它能够从一个开放获取的艺术史文献语料库和维基数据知识中提取信息，并将这些信息标注到艺术作品上。为此，我们使用了一种新型的语料收集流程，然后通过低秩适应（LoRA）技术对CLIP模型进行调整，使其具有特定领域的特性。我们展示了一个新模型PaintingCLIP，在该模型被收集的语料库弱监督下，其表现优于原始的CLIP模型，并能为给定的艺术作品提供上下文信息。所提出的流程是通用化的，可以轻松应用于多个人文领域。
URL
https://arxiv.org/abs/2602.11349
