Benchmarking Multi-Image Understanding in Vision and Language Models:
  Perception, Knowledge, Reasoning, and Multi-Hop Reasoning
Authors: name: Bingchen Zhao, name: Yongshuo Zong, name: Letian Zhang, name: Timothy Hospedales

The advancement of large language models (LLMs) has significantly broadened
the scope of applications in natural language processing, with multi-modal LLMs
extending these capabilities to integrate and interpret visual data. However,
existing benchmarks for visual language models (VLMs) predominantly focus on
single-image inputs, neglecting the crucial aspect of multi-image
understanding. In this paper, we introduce a Multi-Image Relational Benchmark
MIRB, designed to evaluate VLMs' ability to compare, analyze, and reason across
multiple images. Our benchmark encompasses four categories: perception, visual
world knowledge, reasoning, and multi-hop reasoning. Through a comprehensive
evaluation of a wide range of open-source and closed-source models, we
demonstrate that while open-source VLMs were shown to approach the performance
of GPT-4V in single-image tasks, a significant performance gap remains in
multi-image reasoning tasks. Our findings also reveal that even the
state-of-the-art GPT-4V model struggles with our benchmark, underscoring the
need for further research and development in this area. We believe our
contribution of MIRB could serve as a testbed for developing the
next-generation multi-modal models.