用人类能懂的美学概念解释图像评分，让算法判断更透明。
From Concepts to Judgments: Interpretable Image Aesthetic Assessment
- 基于人理解的美学概念构建可解释模型
- 在摄影与艺术数据集上表现接近顶尖模型
- 适合需要理解评分原因的创作者或评审者
图像美学评估（IAA）旨在预测人类对图像美感的感知。尽管近期模型具备强大预测能力，但难以解释其判断依据。而用户不仅关心分数，更关注为何某图被认为好看或难看，推动了对IAA可解释性的关注。人类评判美学时常依赖高层级视觉线索作为理由。受此启发，我们提出一种基于人类可理解美学概念的可解释IAA框架。通过以直观方式学习这些概念，构建一个基础子空间，使模型本身具备可解释性。为捕捉超出显式概念的细微影响，引入简单有效的残差预测器。在摄影与艺术数据集上的实验表明，该方法在保持竞争性预测性能的同时，提供透明且人类可理解的美学判断。
原文摘要 · Abstract (English)
Image aesthetic assessment (IAA) aims to predict the aesthetic quality of images as perceived by humans. While recent IAA models achieve strong predictive performance, they offer little insight into the factors driving their predictions. Yet for users, understanding why an image is considered pleasing or not is as valuable as the score itself, motivating growing interest in interpretability within IAA. When humans evaluate aesthetics, they naturally rely on high-level cues to justify their judgments. Motivated by this observation, we propose an interpretable IAA framework grounded in human-understandable aesthetic concepts. We learn these concepts in an accessible manner, constructing a subspace that forms the foundation of an inherently interpretable model. To capture nuanced influences on aesthetic perception beyond explicit concepts, we introduce a simple yet effective residual predictor. Experiments on photographic and artistic datasets demonstrate that our method achieves competitive predictive performance while offering transparent, human-understandable aesthetic judgments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。