M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image
  Quality Assessment
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.
67bc7ea06f88ef9a2b8283d3
Chuan Cui
claimed_verified
2025-02-26T15:37:40.037Z
60534c7e9d7c1d4d81b7e519
/avatars/1496a1d25c07ccf7446d74edc6bda7c0.svg
草帽不是猫
user
strawhat
67bc7ea06f88ef9a2b8283d4
Kejiang Chen
admin_assigned
2025-02-25T16:43:06.621Z
63231a6a9aa01cafc8da1b62
/avatars/d5adb473f43902ba0e2c4cb7f5be394b.svg
Kejiang Chen
user
kejiangchen
67bc7ea06f88ef9a2b8283d5
Zhihua Wei
67bc7ea06f88ef9a2b8283d6
Wen Shen
67bc7ea06f88ef9a2b8283d7
Weiming Zhang
admin_assigned
2025-02-25T16:41:43.726Z
64c54c28c097c6c2b3ab22cf
/avatars/de1f74516d03bb2e01811c0a53dce9c8.svg
weiming zhang
user
xtwfnjezhang
67bc7ea06f88ef9a2b8283d8
Nenghai Yu
M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image
  Quality Assessment
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.
M3-AGIQA, a framework for evaluating AI-generated image quality, uses Multimodal Large Language Models and Low-Rank Adaptation to assess perceptual quality, prompt correspondence, and authenticity across multiple benchmark datasets.
Multimodal Large Language Models MLLMs Low-Rank Adaptation LoRA xLSTM regression head Mean Opinion Scores MOSs