Scene-Aware Image Aesthetic Quality Assessment

Abstract

Image aesthetic attribute assessment provides explainable outputs for Image Aesthetic Quality Assessment (IAQA), evaluating attributes such as rule of thirds, symmetry, and lighting. These attributes are context-dependent, as their importance varies across different photography scenes. However, most attribute-based IAQA methods remain scene-agnostic, limiting their ability to model scene–attribute dependencies. We propose a scene-aware IAQA model based on a vision transformer that extracts multi-level features and integrates learned scene embeddings within a two-tower module to capture both general and scene-specific patterns, with adaptive gating for context-aware fusion. Experimental results show improved correlation for both overall score and attribute prediction, outperforming state-of-the-art attribute-based IAQA methods.