R4-CGQA：基于检索的视觉语言模型用于计算机图形图像质量评估
R4-CGQA: Retrieval-based Vision Language Models for Computer Graphics Image Quality Assessment
提出双流检索增强生成框架，利用视觉相似图像的质量描述提升VLM对细粒度图像质量的判断能力，可迁移至其他需要细粒度感知的视觉语言任务。
原文依据 we adopt retrieval-augmented generation and propose a two-stream retrieval framework that effectively enhances the CG quality assessment capabilities of VLMs
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论沉浸式计算机图形（CG）渲染在现代日常生活中已无处不在。然而，全面评估CG质量仍面临两个挑战：（1）现有CG数据集缺乏对渲染质量的系统描述；（2）现有CG质量评估方法无法提供合理的基于文本的解释。为解决这些问题，我们首先从用户角度确定了CG质量的六个关键感知维度，并构建了一个包含3.5K张CG图像及其对应质量描述的数据集。每个描述涵盖CG风格、内容以及所选维度下的感知质量。此外，我们利用该数据集的一个子集，基于描述构建了若干问答基准，以评估现有视觉语言模型（VLM）的响应。我们发现当前VLM在判断细粒度CG质量方面不够准确，但视觉相似图像的描述能显著提升VLM对给定CG图像的理解。受此观察启发，我们采用检索增强生成，并提出了一种双流检索框架，有效增强了VLM的CG质量评估能力。在多个代表性VLM上的实验表明，我们的方法大幅提升了它们在CG质量评估上的性能。公开数据集和代码见：https://github.com/lizhuangzi/R4-CGQA
英文原文摘要 1449 字符
Immersive Computer Graphics (CGs) rendering has become ubiquitous in modern daily life. However, comprehensively evaluating CG quality remains challenging for two reasons: (1) existing CG datasets lack systematic descriptions of rendering quality; and (2) existing CG quality assessment methods cannot provide reasonable text-based explanations. To address these issues, we first identify six key perceptual dimensions of CG quality from the user perspective and construct a dataset of 3.5\mathrm K CG images with corresponding quality descriptions. Each description covers CG style, content, and perceived quality along the selected dimensions. Furthermore, we use a subset of the dataset to build several question-answer benchmarks based on the descriptions in order to evaluate the responses of existing Vision Language Models (VLMs). We find that current VLMs are not sufficiently accurate in judging fine-grained CG quality, but that descriptions of visually similar images can significantly improve a VLM's understanding of a given CG image. Motivated by this observation, we adopt retrieval-augmented generation and propose a two-stream retrieval framework that effectively enhances the CG quality assessment capabilities of VLMs. Experiments on several representative VLMs demonstrate that our method substantially improves their performance on CG quality assessment. The public dataset and code are at: https://github.com/lizhuangzi/R4-CGQA