MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models
<|reference_start|>MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models: The capability to process multiple images is crucial for Large Vision-Language Models (LVLMs) to develop a more thorough and nuanced understanding of a scene. Recent multi-image LVLMs have begun to address this need. However, their evaluation has not kept pace with their development. To fill this gap, we introduce the Multimodal Multi-image Understanding (MMIU) benchmark, a comprehensive evaluation suite designed to assess LVLMs across a wide range of multi-image tasks. MMIU encompasses 7 types of multi-image relationships, 52 tasks, 77K images, and 11K meticulously curated multiple-choice questions, making it the most extensive benchmark of its kind. Our evaluation of 24 popular LVLMs, including both open-source and proprietary models, reveals significant challenges in multi-image comprehension, particularly in tasks involving spatial understanding. Even the most advanced models, such as GPT-4o, achieve only 55.7% accuracy on MMIU. Through multi-faceted analytical experiments, we identify key performance gaps and limitations, providing valuable insights for future model and data improvements. We aim for MMIU to advance the frontier of LVLM research and development, moving us toward achieving sophisticated multimodal multi-image user interactions.<|reference_end|>
@article{meng2024mmiu:,
    title={MMIU: Multimodal Multi-image Understanding for Evaluating Large
  Vision-Language Models},
    author={Fanqing Meng, Jin Wang, Chuanhao Li, Quanfeng Lu, Hao Tian, Jiaqi
  Liao, Xizhou Zhu, Jifeng Dai, Yu Qiao, Ping Luo, Kaipeng Zhang, Wenqi Shao},
    journal={arXiv preprint arXiv:2408.02718},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02718},
    primaryClass={cs.CV}
}