Yi, Ran, Tian, Haoyuan, Gu, Zhihao, Lai, Yukun  ORCID: https://orcid.org/0000-0002-2094-5680 and Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884
2023.
Towards artistic image aesthetics assessment: a large-scale dataset and a new method.
Presented at: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
Vancouver, Canada,
18-22 June 2023.
Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
IEEE,
pp. 22388-22397.
10.1109/CVPR52729.2023.02144
|
Preview |
PDF
- Accepted Post-Print Version
Download (1MB) | Preview |
Abstract
Image aesthetics assessment (IAA) is a challenging task due to its highly subjective nature. Most of the current studies rely on large-scale datasets (e.g., AVA and AADB) to learn a general model for all kinds of photography images. However, little light has been shed on measuring the aesthetic quality of artistic images, and the existing datasets only contain relatively few artworks. Such a defect is a great obstacle to the aesthetic assessment of artistic images. To fill the gap in the field of artistic image aesthetics assessment (AIAA), we first introduce a large-scale AIAA dataset: Boldbrush Artistic Image Dataset (BAlD), which consists of 60,337 artistic images covering various art forms, with more than 360,000 votes from online users. We then propose a new method, SAAN (Style-specific Art Assessment Network), which can effectively extract and utilize style-specific and generic aesthetic information to evaluate artistic images. Experiments demonstrate that our proposed approach outperforms existing lAA methods on the proposed BAlD dataset according to quantitative comparisons. We believe the proposed dataset and method can serve as a foundation for future AIAA works and inspire more research in this field. Dataset and code are available at: https://github.com/Dreemurr-T/BAID.git
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | IEEE |
| ISBN: | 979-8-3503-0130-4 |
| ISSN: | 1063-6919 |
| Date of First Compliant Deposit: | 20 April 2023 |
| Date of Acceptance: | 27 February 2023 |
| Last Modified: | 13 May 2025 14:41 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/158972 |
Actions (repository staff only)
 |
Edit Item |
Dimensions
Dimensions