Cross-Modality Interactive Attention Network for AI-generated image quality assessment

Abstract

Recently, AI-generative techniques have revolutionized image creation, prompting the need for AI-generated image (AGI) quality assessment. This paper introduces CIA-Net, a Cross-modality Interactive Attention Network, for blind AGI quality evaluation. Using a multi-task framework, CIA-Net processes text and image inputs to output consistency, visual quality, and authenticity scores. Specifically, CIA-Net first encodes two-modal data to obtain textual and visual embeddings. Next, for consistency score prediction, it computes the similarity between these two kinds of embeddings in view of that text-to-image alignment. For visual quality prediction, it fuses textural and visual embeddings using a well-designed cross-modality interactive attention module. For authenticity score prediction, it constructs a textural template that contains authenticity labels and computes the joint probability from the similarity between the textural embeddings of each element and the visual embeddings. Experimental results show that CIA-Net is more competent for the AGI quality assessment task than 11 state-of-the-art competing methods.