VDBench: A Benchmark for Visual Diachronic Analysis of Historical Depictions

We introduce Visual Diachronic Analysis (VDA), a new computer vision challenge for studying how the visual context of a depicted subject or theme changes over time and how these changes relate to documented historical shifts in meaning. This raises the question to what extent such changes in meaning can be inferred computationally from historical depictions. To study such phenomena, we introduce VDBench, the first benchmark for VDA. In the absence of available and sufficiently annotated historical image collections, we propose an unconventional approach: we construct a historically grounded synthetic benchmark dataset. We compile a source-grounded collection of human-centred diachronic topics and design a controlled text-to-image pipeline to generate plausible synthetic historical depictions. Domain experts confirm the suitability and plausibility of the data. We specify several computer vision tasks, propose a suitable evaluation methodology, and provide baseline methods and implementations as reference for future work. The dataset and an interactive explorer are available at https://anonymousauthor287.github.io/projectpage/. All code will be released upon acceptance.