Benchmarking Visual Cognition of Multimodal LLMs via Matrix Reasoning

Xu Cao, Bolin Lai, Wenqian Ye, Yunsheng Ma, Joerg Heintz, Meihuan Huang, Jintai Chen, Jianguo Cao, Aidong Zhang, James Matthew Rehg

Keywords: Visual Cognition, Matrix Reasoning, Psychometrics, Visual Reasoning, Multimodal LLMs

TL;DR: We propose a new dataset MaRs-VQA and a new benchmark VCog-Bench to evaluate the zero-shot visual cognition capability of MLLMs and compare their performance.

Abstract: Recently, Multimodal Large Language Models (MLLMs) and Vision Language Models (VLMs) have shown great promise in language-guided perceptual tasks such as recognition, segmentation, and object detection. However, their effectiveness in addressing visual cognition problems that require high-level multi-image reasoning and visual working memory is not well-established. One such challenge is matrix reasoning -- the cognitive ability to discern relationships among patterns in a set of images and extrapolate to predict subsequent patterns. This skill is crucial during the early neurodevelopmental stages of children. Inspired by the matrix reasoning tasks in Raven’s Progressive Matrices (RPM) and Wechsler Intelligence Scale for Children (WISC), we propose a new dataset MaRs-VQA and a new benchmark VCog-Bench to evaluate the zero-shot visual cognition capability of MLLMs and compare their performance with existing human visual cognition investigation. Our comparative experiments with different open-source and closed-source MLLMs on the VCog-Bench revealed a gap between MLLMs and human intelligence, highlighting the visual cognitive limitations of current MLLMs. We believe that the public release of VCog-Bench, consisting of MaRs-VQA, and the inference pipeline will drive progress toward the next generation of MLLMs with human-like visual cognition abilities.

Primary Area: applications to neuroscience & cognitive science

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Submission Number: 2479