发表机构
Xidian University（西安电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文推出含4000张图像的AGIDefect-4K数据集，并提出基于多模态大语言模型的AGIDA基准框架，用于AGI缺陷检测、定位、解释及质量预测，凸显了AGI缺陷理解研究的价值。
AI 中文摘要
生成式AI如今可产出高度逼真的图像，但现有模型仍存在细微却关键的缺陷，损害其可靠性。尽管现有的AI生成图像（AGI）评估基准已取得显著进展，全面的AGI缺陷诊断仍未得到充分探索。为填补这一空白，我们推出AGIDefect-4K，这是一个包含4000张图像的富标注数据集，图像来自15个最先进的生成模型，涵盖开源与闭源系统。AGIDefect-4K具有分层缺陷标注：（1）识别是否存在缺陷的检测标签；（2）定位缺陷区域的像素级分割掩码；（3）描述缺陷类型及其感知影响的详细文本解释。每张图像还标注了总体质量评分。在此基础上，我们提出AGIDA（AGI缺陷助手），这是一个利用多模态大语言模型（MLLMs）进行联合缺陷检测、定位、解释和质量预测的基准框架。在AGIDefect-4K上进行的综合基准测试表明，AGI缺陷理解仍具挑战性，凸显了该数据集的价值。该数据集可通过此URL公开获取。
英文摘要
Generative AI can now produce highly realistic images, yet current models still exhibit subtle but critical defects that undermine their reliability. While existing AI-generated image (AGI) evaluation benchmarks have made notable progress, comprehensive AGI defect diagnosis remains underexplored. To bridge this gap, we introduce AGIDefect-4K, a richly annotated dataset of 4,000 images from 15 state-of-the-art generative models spanning both open-source and closed-source systems. AGIDefect-4K features hierarchical defect annotations: (1) detection labels identifying whether defects exist, (2) pixel-level segmentation masks localizing defective regions, and (3) detailed textual explanations characterizing defect types and their perceptual impact. Each image is further annotated with an overall quality score. Building on this, we present AGIDA (AGI Defect Assistant), a baseline framework leveraging Multimodal Large Language Models (MLLMs) for joint defect detection, localization, explanation, and quality prediction. Comprehensive benchmarking on AGIDefect-4K reveals that AGI defect understanding remains challenging, underscoring the value of this dataset. The dataset is publicly available at https://github.com/sxfly99/AGIDefect-4K.
Comments8 pages, 6 figures. Accepted by ACM Multimedia 2026
