IAACLIP: Image Aesthetics Assessment via CLIP
Zhuo Li, X. H. Yan, Xuebin Wei, Feng Shao · Electronics · 2025
Aesthetics primarily focuses on the study of art, encompassing the aesthetic categories of beauty and ugliness, as well as human aesthetic activities. Image Aesthetics Assessment (IAA) seeks to automatically evaluate the aesthetic quality of images by mimicking the perceptual mechanisms of humans. Recently, researchers have increasingly explored using user comments to assist in IAA tasks. However, human aesthetics are subjective, and individuals may have varying preferences for the same image, leading to diverse comments that can influence model decisions. Moreover, in practical scenarios, user comments are often unavailable. Thus, this paper proposes a CLIP-based method for IAA (IAACLIP) using generative descriptions and prompts. First, leveraging the growing interest in multimodal large language models (MLLMs), we generate objective and consistent aesthetic descriptions (GADs) for images. Second, based on aesthetic images, labels, and GADs, we introduce a unified contrast pre-training approach to transition the network from the general domain to the aesthetic domain. Lastly, we employ prompt templates for perceptual training to address the lack of real-world comments. Experimental validation on three mainstream IAA datasets demonstrates the effectiveness of our proposed method.