Vision-Language Models (VLMs) are trained on vast amounts of data captured by humans emulating our understanding of the world. However, known as visual illusions, human's perception of reality isn't always faithful to the physical world. This raises a key question: do VLMs have the similar kind of illusions as humans do, or do they faithfully learn to represent reality? To investigate this question, we build a dataset containing five types of visual illusions and formulate four tasks to examine visual illusions in state-of-the-art VLMs. Our findings have shown that although the overall alignment is low, larger models are closer to human perception and more susceptible to visual illusions. Our dataset and initial findings will promote a better understanding of visual illusions in humans and machines and provide a stepping stone for future computational models that can better align humans and machines in perceiving and communicating about the shared visual world. The code and data are available at https://github.com/vl-illusion/dataset.

翻译：视觉-语言模型（VLMs）在人类捕获的海量数据上进行训练，模拟了我们对世界的理解。然而，人类对现实的感知并非始终忠实于物理世界，这即所谓的视觉错觉。这引出一个关键问题：VLMs是否具有与人类相似的错觉，还是它们忠实地学习并表征了现实？为探究此问题，我们构建了一个包含五类视觉错觉的数据集，并设计了四项任务来检验当前最先进VLMs中的视觉错觉。研究结果表明，尽管整体对齐程度较低，但更大规模的模型更接近人类感知，且更易受视觉错觉影响。我们的数据集与初步发现将促进对人类与机器中视觉错觉的深入理解，并为未来能够更好地对齐人类与机器在感知和沟通共享视觉世界方面的计算模型奠定基础。代码与数据可在https://github.com/vl-illusion/dataset获取。