---
title: "当组合不成立：人类识别AI生成图像中的缺陷"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.25933v1"
permalink: "https://feed.astralor.com/items/itm_8c5302303049d284690b6b59"
published: "2026-08-26T15:47:47.000Z"
score: 75
channel: "ai"
tags: ["ChatGPT","CO-AID","Future-IQA","数据集","文生图模型","组合性缺陷"]
---

# 当组合不成立：人类识别AI生成图像中的缺陷

## AI 点评

> 这篇论文值得关注，因为它首次为AI生图组合缺陷提供了大规模人工标注基准，对做文生图模型评测和调优的工程师尤其有用。相比依赖人工抽检或简单指标，CO-AID让缺陷定位和类型识别有了可训练的数据基础，可能会成为该领域的常用评测资源。

## 速览

本文针对最先进的文生图模型在涉及多实体、多属性等复杂组合提示时出现的系统性缺陷，构建了CO-AID数据集。研究从人物、手部、物体、场景四类参考图中人工筛选651张复杂组合图像，通过编辑ChatGPT生成的提示词，输入三个文生图模型生成图像，并由29名参与者对每张图像进行多标签缺陷标注。实验表明，在CO-AID上训练的深度模型既能预测AI图像缺陷，也能优化生成效果，验证了数据集的有效性和实用性。

## 关键点

- 构建了CO-AID数据集，包含参考图、提示词、AI生成图及缺陷位置与类型标注。
- 涉及人物、手部、物体、场景四类复杂组合场景，每张图由29名参与者标注。
- 实验证明基于CO-AID训练的模型可预测缺陷并改善图像生成质量。

## 全文

*Chulin Zhao and Ruoqi Hu contributed equally to this work. State-of-the-art text-to-image (T2I) models exhibit pronounced and systematic defects when prompts involve intricate compositional factors such as multiple entities and multiple attributes. In this paper, we investigate how humans identify such defects. Specifically, we manually select 651 reference images from the four categories of people, hand, object, and scene that exhibit complex compositional characteristics, from which prompts emphasizing compositional factors are derived by manually editing ChatGPT-generated prompts. We then feed the prompts into three selected T2I models to generate AI images and conduct a comprehensive subjective study to identify their defects. For each image, 29 participants provide multi-label assessments specifying defect types and locations. The study yields the compositional AI-generated image defect (CO-AID) dataset, including reference images, prompts, AI-generated images, and information on defect locations and types. Experimental results show that training a deep model on CO-AID can both predict defects in AI-generated images and optimize AI image generation, demonstrating its usability and effectiveness. The database and supplementary materials are available at: https://github.com/Future-IQA/CO-AID .

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_8c5302303049d284690b6b59