首个面向艺术作品的个性化审美评估数据集，支持研究人类审美差异。
LAPIS: A novel dataset for personalized image aesthetic assessment
- 构建包含11723幅艺术品的个性化审美数据集，融合图像与用户属性。
- 实验证明去除个人或图像特征会显著降低模型性能。
- 适合艺术计算、个性化推荐与人机审美研究者使用。
我们提出了莱文艺术个性化图像集（LAPIS），首个适用于个性化图像审美评估（PIAA）的艺术作品数据集。该数据集包含11,723幅经艺术史学家精心筛选的艺术作品图像，每张图均配有审美评分及与审美感知相关的图像属性。此外，还记录了标注者的丰富个人属性。我们测试了两种现有先进的PIAA模型在LAPIS上的表现，通过消融实验发现移除特定个人或图像属性会导致性能下降。失败案例分析显示，现有模型常犯相似错误，凸显当前艺术图像审美评估仍需改进。项目主页见：https://github.com/Anne-SofieMaerten/LAPIS。
原文摘要 · Abstract (English)
We present the Leuven Art Personalized Image Set (LAPIS), a novel dataset for personalized image aesthetic assessment (PIAA). It is the first dataset with images of artworks that is suitable for PIAA. LAPIS consists of 11,723 images and was meticulously curated in collaboration with art historians. Each image has an aesthetics score and a set of image attributes known to relate to aesthetic appreciation. Besides rich image attributes, LAPIS offers rich personal attributes of each annotator. We implemented two existing state-of-the-art PIAA models and assessed their performance on LAPIS. We assess the contribution of personal attributes and image attributes through ablation studies and find that performance deteriorates when certain personal and image attributes are removed. An analysis of failure cases reveals that both existing models make similar incorrect predictions, highlighting the need for improvements in artistic image aesthetic assessment. The LAPIS project page can be found at: https://github.com/Anne-SofieMaerten/LAPIS
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