DEArt: Dataset of European Art

Artem Reshetnikov, Maria Cristina Marinescu, Joaquim More Lopez

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image/manually annotated metadata, where images are photographs of everyday life. Scholarly and historical content, on the other hand, treat subjects that are not necessarily popular to a general audience, they may not always contain a large number of data points, and new data may be difficult or impossible to collect. Some exceptions do exist, for instance, scientific or health data, but this is not the case for cultural heritage (CH). The poor performance of the best models in computer vision - when tested over artworks - coupled with the lack of extensively annotated datasets for CH, and the fact that artwork images depict objects and actions not captured by photographs, indicate that a CH-specific dataset would be highly valuable for this community. We propose DEArt, at this point primarily an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are CH-specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art. Additionally, existing datasets do not include pose annotations. Our results show that object detectors for the cultural heritage domain can achieve a level of precision comparable to state-of-art models for generic images via transfer learning.

Idioma originalInglés
Título de la publicación alojadaComputer Vision – ECCV 2022 Workshops, Proceedings
EditoresLeonid Karlinsky, Tomer Michaeli, Ko Nishino
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas218-233
Número de páginas16
ISBN (versión impresa)9783031250552
DOI
EstadoPublicada - 2023
Publicado de forma externa
Evento17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel
Duración: 23 oct 202227 oct 2022

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen13801 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia17th European Conference on Computer Vision, ECCV 2022
País/TerritorioIsrael
CiudadTel Aviv
Período23/10/2227/10/22

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