Art Comes From Life: Artistic Image Aesthetics Assessment via Attribute Knowledge Amalgamation

Assessing the aesthetic quality and visual appeal of artworks has become one of the hotspots in current research. The existing artistic image aesthetics assessment (AIAA) methods directly learn aesthetics from images, while ignoring the impact of variations in visual attributes on human aesthetic perception, which hampers the further development of AIAA. To address this issue, this paper presents a new AIAA method based on attribute knowledge amalgamation, named AKA-Net. Specifically, we initially learn common attribute aesthetic rules (e.g., composition and color) through pre-training on natural aesthetic images. Then, we devise a multi-model amalgamation strategy based on contrastive learning to transfer different types of prior attribute knowledge into a single target model, enabling flexible and efficient aesthetic prediction. Finally, an attribute-aware feature enhancement module (AFEM) is introduced to better establish the relationship between aesthetic quality and attribute knowledge. Experimental results on three public benchmark AIAA databases demonstrate that the proposed AKA-Net outperforms the state-of-the-art AIAA metrics.