用自动标注和神经网络解释图像审美评分的视觉特征。
Explaining Automatic Image Assessment
- 通过训练神经网络自动分类视觉审美特征。
- 在不同数据集版本上评估模型，捕捉审美趋势。
- 适合研究图像审美生成与可解释性的学者。
以往的美学分类与可解释性研究依赖人工标注和分类来解释美学评分，但标注过程复杂且规模受限。本文提出新方法，通过可视化数据集趋势，并在不同版本的数据集上训练神经网络，实现视觉美学特征的自动分类与解释。通过现有及新提出的度量指标，评估各模态适配的模型，有效捕获并可视化美学特征与趋势。
原文摘要 · Abstract (English)
Previous work in aesthetic categorization and explainability utilizes manual labeling and classification to explain aesthetic scores. These methods require a complex labeling process and are limited in size. Our proposed approach attempts to explain aesthetic assessment models through visualizing dataset trends and automatic categorization of visual aesthetic features through training neural networks on different versions of the same dataset. By evaluating the models adapted to each specific modality using existing and novel metrics, we can capture and visualize aesthetic features and trends.
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