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 (BAID), 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 IAA methods on the proposed BAID 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

翻译：图像美学评估（IAA）是一项极具挑战性的任务，因其高度主观性。目前大多数研究依赖大规模数据集（如AVA和AADB）来学习适用于各类摄影图像的通用模型。然而，针对艺术图像美学质量的评估研究尚属空白，现有数据集仅包含少量艺术品，这一缺陷严重阻碍了艺术图像美学评估的发展。为填补艺术图像美学评估（AIAA）领域的空白，我们首先引入了一个大规模AIAA数据集：Boldbrush艺术图像数据集（BAID），该数据集包含60,337张涵盖多种艺术形式的艺术图像，并汇集了超过36万条来自在线用户的投票。随后，我们提出了一种新方法——SAAN（风格特定艺术评估网络），该方法能够有效提取并利用风格特定与通用的美学信息来评估艺术图像。实验表明，在BAID数据集上的定量比较中，我们提出的方法优于现有IAA方法。我们相信，所提出的数据集和方法将为未来AIAA研究奠定基础，并激发该领域的更多探索。数据集与代码已开源于：https://github.com/Dreemurr-T/BAID.git