Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans?
This work addresses the problem of understanding human-machine perceptual alignment in vision-language AI, with incremental contributions to benchmarking and dataset creation.
The paper investigates whether Vision-Language Models (VLMs) perceive visual illusions similarly to humans by building a dataset with five types of illusions and testing state-of-the-art models, finding that larger models align more closely with human perception and are more susceptible to illusions, though overall alignment is low.
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.