M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image
  Quality Assessment
Authors: name: Chuan Cui, name: Kejiang Chen, name: Zhihua Wei, name: Wen Shen, name: Weiming Zhang, name: Nenghai Yu

The rapid advancement of AI-generated image (AGI) models has introduced
significant challenges in evaluating their quality, which requires considering
multiple dimensions such as perceptual quality, prompt correspondence, and
authenticity. To address these challenges, we propose M3-AGIQA, a comprehensive
framework for AGI quality assessment that is Multimodal, Multi-Round, and
Multi-Aspect. Our approach leverages the capabilities of Multimodal Large
Language Models (MLLMs) as joint text and image encoders and distills advanced
captioning capabilities from online MLLMs into a local model via Low-Rank
Adaptation (LoRA) fine-tuning. The framework includes a structured multi-round
evaluation mechanism, where intermediate image descriptions are generated to
provide deeper insights into the quality, correspondence, and authenticity
aspects. To align predictions with human perceptual judgments, a predictor
constructed by an xLSTM and a regression head is incorporated to process
sequential logits and predict Mean Opinion Scores (MOSs). Extensive experiments
conducted on multiple benchmark datasets demonstrate that M3-AGIQA achieves
state-of-the-art performance, effectively capturing nuanced aspects of AGI
quality. Furthermore, cross-dataset validation confirms its strong
generalizability. The code is available at
https://github.com/strawhatboy/M3-AGIQA.