通过利用人类方法诊断视觉语言模型的感知能力：针对色觉缺陷的色觉研究
Diagnosing Vision Language Models' Perception by Leveraging Human Methods for Color Vision Deficiencies
AI总结：
本研究通过Ishihara测试评估视觉语言模型对色觉差异的感知能力，发现模型无法再现色觉缺陷个体的感知结果，揭示了多模态AI在包容性部署中的不足。
摘要：
大规模视觉语言模型（LVLMs）正在被部署到需要视觉推理的现实环境中。随着能力的提升，导航、教育和可访问性等应用变得实用。这些环境要求适应感知差异，而不是假设统一的视觉体验。色觉体现了这一要求：它对于视觉理解至关重要，但因色觉缺陷而个体间存在差异，这一方面在多模态AI中被忽视。在本工作中，我们探讨了LVLMs是否能够通过Ishihara测试来考虑色觉感知的变化。我们通过生成、置信度和内部表示来评估模型行为，使用Ishihara板作为受控刺激，以暴露感知差异。尽管模型具备关于色觉缺陷的事实知识，并能描述测试，但它们无法再现受影响个体所经历的感知结果，而是默认采用规范色觉感知。这些结果表明，当前系统缺乏表示替代感知体验的机制，对多模态环境中的可访问性和包容性部署提出了担忧。
英文摘要：
Large-scale Vision-Language Models (LVLMs) are being deployed in real-world settings that require visual inference. As capabilities improve, applications in navigation, education, and accessibility are becoming practical. These settings require accommodation of perceptual variation rather than assuming a uniform visual experience. Color perception illustrates this requirement: it is central to visual understanding yet varies across individuals due to Color Vision Deficiencies, an aspect largely ignored in multimodal AI. In this work, we examine whether LVLMs can account for variation in color perception using the Ishihara Test. We evaluate model behavior through generation, confidence, and internal representation, using Ishihara plates as controlled stimuli that expose perceptual differences. Although models possess factual knowledge about color vision deficiencies and can describe the test, they fail to reproduce the perceptual outcomes experienced by affected individuals and instead default to normative color perception. These results indicate that current systems lack mechanisms for representing alternative perceptual experiences, raising concerns for accessibility and inclusive deployment in multimodal settings.
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。
