Personalized Image Aesthetics Assessment via Multi-Attribute Interactive Reasoning

Due to the subjective nature of people's aesthetic experiences with respect to images, personalized image aesthetics assessment (PIAA), which can simulate the aesthetic experiences of individual users to estimate images, has received extensive attention from researchers in the computational intelligence and computer vision communities. Existing PIAA models are usually built on prior knowledge that directly learns the generic aesthetic results of images from most people or the personalized aesthetic results of images from a large number of individuals. However, the learned prior knowledge ignores the mutual influence of the multiple attributes of images and users in their personalized aesthetic experiences. To this end, this paper proposes a personalized image aesthetics assessment method via multi-attribute interactive reasoning. Different from existing PIAA models, the multi-attribute interaction constructed from both images and users is used as more effective prior knowledge. First, we designed a generic aesthetics extraction module from the perspective of images to obtain the aesthetic score distribution and multiple objective attributes of images rated by most users. Then, we propose a multi-attribute interactive reasoning network from the perspective of users. By interacting multiple subjective attributes of users with multiple objective attributes of images, we fused the obtained multi-attribute interactive features and aesthetic score distribution to predict personalized aesthetic scores. Experimental results on multiple PIAA datasets demonstrated our method outperformed state-of-the-art PIAA methods.