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arXiv 2412.06184cs.CV

评估真实感场景中模型对颜色错觉的感知

Evaluating Model Perception of Color Illusions in Photorealistic Scenes

University of California, Berkeley(加州大学伯克利分校)

Lingjun Mao, Zineng Tang, Alane Suhr

AI总结:

该研究针对视觉语言模型对颜色错觉的感知偏差问题,提出自动化生成框架构建含19000张图像的RCID数据集,实验发现VLM存在与人类相似的感知偏差,并训练模型区分人类感知与实际像素差异。

摘要:

我们研究视觉语言模型对颜色错觉的感知。颜色错觉指人的视觉系统感知到的颜色与实际颜色存在差异,这一现象在人类视觉领域已得到充分研究。然而,基于大规模人类数据训练的视觉语言模型(VLM)在面对此类颜色错觉时是否表现出相似的感知偏差,目前仍未得到充分探究。我们提出了一个用于生成颜色错觉图像的自动化框架,由此构建了包含19000张真实错觉图像的数据集RCID(真实颜色错觉数据集)。我们的实验表明,所有被研究的VLM都表现出与人类视觉相似的感知偏差。最后,我们训练了一个模型来区分人类感知与实际像素差异。

英文摘要:

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.

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。

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