Evaluating Graphical Perception Capabilities of Vision Transformers

Abstract

Vision Transformers, ViTs, have emerged as a powerful alternative to convolutional neural networks, CNNs, in a variety of image-based tasks. While CNNs have previously been evaluated for their ability to perform graphical perception tasks, which are essential for interpreting visualizations, the perceptual capabilities of ViTs remain largely unexplored. In this work, we investigate the performance of ViTs in elementary visual judgment tasks inspired by the foundational studies of Cleveland and McGill, which quantified the accuracy of human perception across different visual encodings. Inspired by their study, we benchmark ViTs against CNNs and human participants in a series of controlled graphical perception tasks. Our results reveal that, although ViTs demonstrate strong performance in general vision tasks, their alignment with human-like graphical perception in the visualization domain is limited. This study highlights key perceptual gaps and points to important considerations for the application of ViTs in visualization systems and graphical perceptual modeling.

References in corpus (9)

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• scikit-image: Image processing in Python
• Gaussian Error Linear Units (GELUs)
• A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization
• Five Points to Check when Comparing Visual Perception in Humans and Machines
• PaLI-X: On Scaling up a Multilingual Vision and Language Model
• ChartLlama: A Multimodal LLM for Chart Understanding and Generation
• ChartDETR: A Multi-shape Detection Network for Visual Chart Recognition
• GPU-Friendly Laplacian Texture Blending