Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences

• URL: http://arxiv.org/abs/2401.10529v2
• Date: Thu, 25 Jan 2024 04:11:57 GMT
• Title: Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences
• Authors: Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang
• Abstract summary: This paper introduces Mementos, a new benchmark designed to assess MLLMs' sequential image reasoning abilities. We find that MLLMs struggle to accurately describe dynamic information about given image sequences, often leading to hallucinations/misrepresentations of objects.
• Score: 80.54979242912944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks. However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate from image sequences, which is essential for understanding our ever-changing world, has been less investigated. To address this challenge, this paper introduces Mementos, a new benchmark designed to assess MLLMs' sequential image reasoning abilities. Mementos features 4,761 diverse image sequences with varying lengths. We also employ a GPT-4 assisted method to evaluate MLLM reasoning performance. Through a careful evaluation of nine recent MLLMs on Mementos, including GPT-4V and Gemini, we find that they struggle to accurately describe dynamic information about given image sequences, often leading to hallucinations/misrepresentations of objects and their corresponding behaviors. Our quantitative analysis and case studies identify three key factors impacting MLLMs' sequential image reasoning: the correlation between object and behavioral hallucinations, the influence of cooccurring behaviors, and the compounding impact of behavioral hallucinations. Our dataset is available at https://github.com/umd-huang-lab/Mementos.

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