Seeing the Differences in Artistry among Art Fields by using Multi-task Learning

Ryo Sato, Fumihiko Sakaue, Jun Sato

2022

Abstract

In this paper, we propose a method for analyzing the relevance of artistry among multiple art fields by using deep neural networks. Artistry is thought to exist in various man-made objects, such as paintings, sculptures, architectures, and gardens. However, we are not sure if the artistry or the human aesthetic sensitivities in these different art fields is the same or different. Therefore, we in this paper propose a method for analyzing the relevance of artistry among multiple art fields by using deep neural networks. In particular, we show that by using the multi-task learning, the relevance of multiple art fields can be analyzed efficiently.

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Paper Citation


in Harvard Style

Sato R., Sakaue F. and Sato J. (2022). Seeing the Differences in Artistry among Art Fields by using Multi-task Learning. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 609-616. DOI: 10.5220/0010902300003124


in Bibtex Style

@conference{visapp22,
author={Ryo Sato and Fumihiko Sakaue and Jun Sato},
title={Seeing the Differences in Artistry among Art Fields by using Multi-task Learning},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={609-616},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010902300003124},
isbn={978-989-758-555-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Seeing the Differences in Artistry among Art Fields by using Multi-task Learning
SN - 978-989-758-555-5
AU - Sato R.
AU - Sakaue F.
AU - Sato J.
PY - 2022
SP - 609
EP - 616
DO - 10.5220/0010902300003124
PB - SciTePress