Uncertainty-Aware Art-Historical Dating with Vision-Language Models
TL;DR
This work evaluates several pretrained vision models on a temporally controlled Wikidata corpus of artworks and shows that these models contain usable temporal information, however, a qualitative analysis indicates that this temporal knowledge is shaped by various biases.
Abstract
Museum and archival datasets do not mirror historical artistic production, but materialize the contingent histories of collecting, preservation, cataloging, and digitization. This has direct consequences for interpreting pretrained image representations: they may appear to encode historical time while actually encoding the institutional conditions under which objects become visible as data. We describe this phenomenon as temporal entanglement and investigate it by formulating artwork dating as an uncertainty-aware regression task over frozen image embeddings. We evaluate several pretrained vision models on a temporally controlled Wikidata corpus of artworks. Our results show that these models contain usable temporal information, with Vision-Language Models (VLMs) outperforming purely visual self-supervised baselines. However, a qualitative analysis indicates that this temporal knowledge is shaped by various biases.