构建17.5万幅浮世绘数据集，推动艺术风格多任务分析
Stylistic Multi-Task Analysis of Ukiyo-e Woodblock Prints
- 将艺术风格分析视为多任务学习问题，利用元数据提升模型泛化能力
- 涵盖17世纪至今的17.5万幅浮世绘作品，含艺术家、年代等详细信息
- 为艺术视觉计算提供新基准，适合对日本传统艺术感兴趣的研究者
本文构建了一个大规模的浮世绘木版画数据集。与以往主要关注西方艺术的研究不同，该工作旨在拓展前现代日本艺术的风格分析范畴，并为多种面向艺术的计算机视觉方法提供评估基准。数据集包含超过17.5万幅作品，附带艺术家、时代和创作日期等元数据，时间跨度从17世纪至今。通过将风格分析建模为多任务学习问题，我们希望更高效地利用现有元数据，学习更具通用性的风格表征。本文展示了多个经典基线与前沿多任务学习框架的结果，以支持未来对比研究，并鼓励在该艺术领域开展更多风格分析工作。
原文摘要 · Abstract (English)
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.
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