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.

翻译：本研究提出了一个大规模的浮世绘木版画数据集。与艺术领域中主要关注西方艺术的先前工作和数据集不同，本文探索了这一前现代日本艺术形式，旨在拓宽风格分析的研究范围，并为评估各类以艺术为核心的计算机视觉方法提供基准。我们的数据集包含超过$175.000$幅从17世纪至今的木版画及其对应元数据（例如艺术家、时代和创作日期）。通过将风格分析视为多任务问题，我们旨在更有效地利用现有元数据，并学习更具普适性的风格表征。我们展示了经典基线模型与先进多任务学习框架的实验结果，以支持未来的比较研究，并推动该艺术领域的风格分析工作。