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 this https URL

通过构建包含五种视觉错觉的数据集并制定四项任务，研究发现，虽然整体对齐性较低，但更大的模型更接近人类感知且更容易受到视觉错觉的影响，这将促进对人类和机器中的视觉错觉有更深入的理解，并为未来能够更好地使人类和机器在感知和交流共享的视觉世界方面达到一致的计算模型提供基础。