测试视觉语言模型对真实场景色彩错觉的感知偏差。
Evaluating Model Perception of Color Illusions in Photorealistic Scenes
- 构建自动化生成框架，创建1.9万张真实错觉图像数据集
- 所有测试模型均表现出与人类相似的色彩感知偏差
- 可区分人类感知差异与像素真实差异，适合视觉认知研究
我们研究了视觉语言模型对色彩错觉的感知。色彩错觉指人的视觉系统对颜色的感知与实际颜色存在差异，这在人类视觉中已有深入研究。然而，基于大规模人类数据训练的视觉语言模型（VLMs）在面对此类错觉时是否表现出类似感知偏差仍不明确。为此，我们提出一种自动化框架，生成真实感错觉图像，构建了包含19,000张图像的RCID（Realistic Color Illusion Dataset）。实验表明，所有测试的VLMs均表现出与人类相似的感知偏差。最后，我们训练了一个模型，用于区分人类感知差异与实际像素差异。
原文摘要 · Abstract (English)
We study the perception of color illusions by vision-language models. Color illusion, where a person's visual system perceives color differently from actual color, is well-studied in human vision. However, it remains underexplored whether vision-language models (VLMs), trained on large-scale human data, exhibit similar perceptual biases when confronted with such color illusions. We propose an automated framework for generating color illusion images, resulting in RCID (Realistic Color Illusion Dataset), a dataset of 19,000 realistic illusion images. Our experiments show that all studied VLMs exhibit perceptual biases similar human vision. Finally, we train a model to distinguish both human perception and actual pixel differences.
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