Stylistic Multi-Task Analysis of Ukiyo-e Woodblock Prints
This work addresses the need for broader artistic datasets in computer vision, specifically for pre-modern Japanese art, though it is incremental as it applies existing methods to new data.
The authors tackled the problem of limited datasets for stylistic analysis in non-western art by creating a large-scale dataset of over 175,000 Ukiyo-e woodblock prints with metadata, and they provided benchmark results using multi-task learning frameworks to encourage future research in this domain.
In this work we present a large-scale dataset of \textit{Ukiyo-e} woodblock prints. Unlike previous works and datasets in the artistic domain that primarily focus on western art, this paper explores this pre-modern Japanese art form with the aim of broadening the scope for stylistic analysis and to provide a benchmark to evaluate a variety of art focused Computer Vision approaches. Our dataset consists of over $175.000$ prints with corresponding metadata (\eg artist, era, and creation date) from the 17th century to present day. By approaching stylistic analysis as a Multi-Task problem we aim to more efficiently utilize the available metadata, and learn more general representations of style. We show results for well-known baselines and state-of-the-art multi-task learning frameworks to enable future comparison, and to encourage stylistic analysis on this artistic domain.