Personalized Image Aesthetics Assessment with Attribute-guided Fine-grained Feature Representation

Personalized image aesthetics assessment (PIAA) has gained increasing attention from researchers due to its ability to measure individual users' specific aesthetic experiences. However, most existing PIAA methods rely on holistic features or simplistic coding to characterize users' aesthetic preferences for images, and we believe that more rich explicit features are needed in modeling PIAA. Consequently, we propose an attribute-guided fine-grained feature-aware personalized image aesthetics assessment method, which can fully capture fine-grained features from multiple attributes to represent users' aesthetic preferences for images. To achieve this, we first build a fine-grained feature extraction (FFE) module to obtain the refined local features of image attributes to compensate for holistic features. The FFE module is then used to generate user-level features, which are combined with the image-level features to obtain user-preferred fine-grained feature representations. By training extensive users' PIAA tasks, the aesthetic distribution of most users can be transferred to the personalized scores of individual users. To enable our proposed model to learn more generalizable aesthetics among individual users, we incorporate the degree of dispersion between users' personalized scores and image aesthetic distribution as a coefficient in the loss function during model training. Experimental results on several PIAA databases show that our method outperforms existing mainstream PIAA methods, and can effectively infer users' personalized aesthetics of images.