In this paper, we address the challenges of automatic metadata annotation in
the domain of Galleries, Libraries, Archives, and Museums (GLAMs) by
introducing a novel dataset, EUFCC340K, collected from the Europeana portal.
Comprising over 340,000 images, the EUFCC340K dataset is organized across
multiple facets: Materials, Object Types, Disciplines, and Subjects, following
a hierarchical structure based on the Art & Architecture Thesaurus (AAT). We
developed several baseline models, incorporating multiple heads on a ConvNeXT
backbone for multi-label image tagging on these facets, and fine-tuning a CLIP
model with our image text pairs. Our experiments to evaluate model robustness
and generalization capabilities in two different test scenarios demonstrate the
utility of the dataset in improving multi-label classification tools that have
the potential to alleviate cataloging tasks in the cultural heritage sector.

本研究讨论了在文化遗产领域中自动元数据注释的挑战，并介绍了一种新颖的数据集 EUFCC340K。该数据集采集自 Europeana 门户网站，包含超过 340,000 张图像，并按照材料、对象类型、学科和主题等多个维度进行组织，遵循 AAT 的分层结构。研究开发了多种基线模型，基于 ConvNeXT 骨干网络在图像各个维度上进行多标签标注，并使用图像文本对对 CLIP 模型进行微调。两个不同的测试场景下的实验证明了该数据集在改进多标签分类工具方面的鲁棒性和泛化能力，有潜力缓解文化遗产部门的编目任务。