title: M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image
    Quality Assessment
summary: 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.
paper_authors._id: 67bc7ea06f88ef9a2b8283d3
paper_authors.name: Chuan Cui
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-26T15:37:40.037Z
paper_authors._id: 60534c7e9d7c1d4d81b7e519
paper_authors.avatarUrl: /avatars/1496a1d25c07ccf7446d74edc6bda7c0.svg
paper_authors.fullname: 草帽不是猫
paper_authors.type: user
paper_authors.user: strawhat
paper_authors._id: 67bc7ea06f88ef9a2b8283d4
paper_authors.name: Kejiang Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:43:06.621Z
paper_authors._id: 63231a6a9aa01cafc8da1b62
paper_authors.avatarUrl: /avatars/d5adb473f43902ba0e2c4cb7f5be394b.svg
paper_authors.fullname: Kejiang Chen
paper_authors.type: user
paper_authors.user: kejiangchen
paper_authors._id: 67bc7ea06f88ef9a2b8283d5
paper_authors.name: Zhihua Wei
paper_authors._id: 67bc7ea06f88ef9a2b8283d6
paper_authors.name: Wen Shen
paper_authors._id: 67bc7ea06f88ef9a2b8283d7
paper_authors.name: Weiming Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:41:43.726Z
paper_authors._id: 64c54c28c097c6c2b3ab22cf
paper_authors.avatarUrl: /avatars/de1f74516d03bb2e01811c0a53dce9c8.svg
paper_authors.fullname: weiming zhang
paper_authors.type: user
paper_authors.user: xtwfnjezhang
paper_authors._id: 67bc7ea06f88ef9a2b8283d8
paper_authors.name: Nenghai Yu
paper_title: M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image
    Quality Assessment
paper_summary: 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.
paper_ai_keywords: Multimodal Large Language Models (MLLMs) Low-Rank Adaptation (LoRA) fine-tuning xLSTM Mean Opinion Scores (MOSs)