Toward Multi-Dimensional Quality Assessment of AI-Generated Images via Prompt-Guided Relational Learning

Abstract

The explosive growth of Artificial Intelligence Generated Images (AGIs) has created an urgent need for automated quality assessment. Unlike traditional image quality assessment, AGIs evaluation faces core challenges such as the absence of reference images, the need to integrate textual information, and the multi-dimensional nature of evaluation criteria. Existing methods typically rely on shared multimodal features for unified modeling, failing to effectively address two key issues: the differentiated extraction of dimension-specific features and the modeling of intrinsic relationships between dimensions. To address this, this paper proposes a Prompt-guided Relationship Learning Network (PRL-Net). Specifically, we first design a dimension-specific prompt attention (DPA) module, which introduces learnable prompts for each quality dimension to guide the model in adaptively focusing on dimension-related feature representations from shared features. Furthermore, we introduce a cross-dimensional graph relational attention (CGRA) module that explicitly models the complex dependencies between different quality dimensions (such as quality, consistency, and authenticity) through a dynamic graph neural network, enabling cross-dimensional feature interaction and information complementarity. Experimental results on multiple public benchmark datasets demonstrate that PRL-Net outperforms existing state-of-the-art methods across several evaluation metrics, validating its effectiveness.