Coarse-to-Fine Image Aesthetics Assessment With Dynamic Attribute Selection

Image aesthetics assessment (IAA) is an interesting but challenging task, owing to the ineffable nature of human sense of beauty. The study of IAA has evolved from simple binary classification to more complex score regression and distribution prediction. It is effortless for people to perform aesthetic binary classification, i.e., aesthetically pleasing or not. However, further judgment on the fine-level scalar aesthetic score is complex and typically determined by aesthetic attributes presented in the image, such as content, lighting and color. Motivated by the above facts, this paper presents a Coarse-to-fine image Aesthetics assessment model guided by Dynamic Attribute Selection, dubbed CADAS. The underlying idea is to simulate the process of human aesthetic perception by performing coarse-to-fine aesthetic reasoning. Specifically, a hierarchical AttributeNet is first pre-trained by imitating the staged mechanism of human aesthetic experience, producing the candidate aesthetic attributes. Then, an AestheticNet is introduced to perform the coarse-level binary classification, based on which a confidence-based attribute selection strategy is designed to dynamically pick out the dominant aesthetic attributes from the candidate ones. Finally, a self-attention-based FusionNet is designed to explore the interaction between dominant aesthetic attributes and aesthetic features, producing the fine-level aesthetic prediction. Extensive experiments demonstrate that the proposed model is superior to the state-of-the-arts. Furthermore, CADAS is also able to output the dominant aesthetic attributes in images, facilitating model explainability.