Keep Knowledge in Perception: Zero-Shot Image Aesthetic Assessment

Guolong Wang, Yike Tan, Hangyu Lin, Chuchun Zhang

Abstract: Image aesthetic assessment is an important issue in multimedia, but most existing studies employ supervised learning methods that rely on large-scale annotated data. However, aesthetic scoring annotations are difficult to obtain in large quantities. Therefore, this paper explores zero-shot image aesthetic assessment. We predict aesthetic scores by introducing knowledge of different attributes (e.g., Focus). First, we use prompt tuning to obtain a unique prompt for each aesthetic attribute as external knowledge. Second, we leverage image relations considering sentiment polarity as internal knowledge. Specifically, we obtain aesthetic attribute representations from pre-trained models via prompt learning, then select anchor images on specific attributes by sentiment polarity, computing aesthetic scores. Notably, annotated aesthetic scores are not used in the process. Experiments show that our zero-shot approach outperforms many comparisons using only a few anchor images.