ImageDoctor：基于接地图像推理的文本生成图像诊断方法
ImageDoctor: Diagnosing Text-to-Image Generation via Grounded Image Reasoning
AI总结：
针对现有文本生成图像评估仅用单标量、缺乏可解释性的问题，提出多维度评估框架ImageDoctor，通过“观察-思考-预测”范式实现多维度评估与像素级缺陷指示，在人类偏好对齐及奖励调优效果上均优于传统标量方法。
摘要：
文本生成图像（T2I）模型的快速发展提升了对可靠人类偏好建模的需求，而偏好对齐强化学习的最新进展进一步放大了这一需求。但现有方法通常用单个标量量化生成图像的质量，限制了其对图像质量提供全面、可解释反馈的能力。为解决这一问题，我们提出ImageDoctor——一个统一的多维度T2I模型评估框架，从合理性、语义对齐度、美学效果和整体质量四个互补维度评估图像质量。ImageDoctor还以热力图形式提供像素级缺陷指示器，高亮显示对齐错误或不合理的区域，可作为T2I模型偏好对齐的密集奖励。受诊断流程启发，我们引入“观察-思考-预测”范式提升ImageDoctor的细节敏感度和推理能力：模型先定位潜在缺陷，再生成推理过程，最后给出带量化分数的评估结论。ImageDoctor基于视觉语言模型构建，通过监督微调与强化学习结合的方式训练，在多个数据集上均表现出与人类偏好的高度一致性，验证了其作为评估指标的有效性。此外，当作为偏好调优的奖励模型使用时，ImageDoctor显著提升了生成质量——相比基于标量的奖励模型实现了10%的性能提升。
英文摘要：
The rapid advancement of text-to-image (T2I) models has increased the need for reliable human preference modeling, a demand further amplified by recent progress in reinforcement learning for preference alignment. However, existing approaches typically quantify the quality of a generated image using a single scalar, limiting their ability to provide comprehensive and interpretable feedback on image quality. To address this, we introduce ImageDoctor, a unified multi-aspect T2I model evaluation framework that assesses image quality across four complementary dimensions: plausibility, semantic alignment, aesthetics, and overall quality. ImageDoctor also provides pixel-level flaw indicators in the form of heatmaps, which highlight misaligned or implausible regions, and can be used as a dense reward for T2I model preference alignment. Inspired by the diagnostic process, we improve the detail sensitivity and reasoning capability of ImageDoctor by introducing a "look-think-predict" paradigm, where the model first localizes potential flaws, then generates reasoning, and finally concludes the evaluation with quantitative scores. Built on top of a vision-language model and trained through a combination of supervised fine-tuning and reinforcement learning, ImageDoctor demonstrates strong alignment with human preference across multiple datasets, establishing its effectiveness as an evaluation metric. Furthermore, when used as a reward model for preference tuning, ImageDoctor significantly improves generation quality -- achieving an improvement of 10% over scalar-based reward models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
