MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
<|reference_start|>MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding: We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MuirBench consists of 12 diverse multi-image tasks (e.g., scene understanding, ordering) that involve 10 categories of multi-image relations (e.g., multiview, temporal relations). Comprising 11,264 images and 2,600 multiple-choice questions, MuirBench is created in a pairwise manner, where each standard instance is paired with an unanswerable variant that has minimal semantic differences, in order for a reliable assessment. Evaluated upon 20 recent multi-modal LLMs, our results reveal that even the best-performing models like GPT-4o and Gemini Pro find it challenging to solve MuirBench, achieving 68.0% and 49.3% in accuracy. Open-source multimodal LLMs trained on single images can hardly generalize to multi-image questions, hovering below 33.3% in accuracy. These results highlight the importance of MuirBench in encouraging the community to develop multimodal LLMs that can look beyond a single image, suggesting potential pathways for future improvements.<|reference_end|>
@article{wang2024muirbench:,
    title={MuirBench: A Comprehensive Benchmark for Robust Multi-image
  Understanding},
    author={Fei Wang, Xingyu Fu, James Y. Huang, Zekun Li, Qin Liu, Xiaogeng Liu,
  Mingyu Derek Ma, Nan Xu, Wenxuan Zhou, Kai Zhang, Tianyi Lorena Yan, Wenjie
  Jacky Mo, Hsiang-Hui Liu, Pan Lu, Chunyuan Li, Chaowei Xiao, Kai-Wei Chang,
  Dan Roth, Sheng Zhang, Hoifung Poon, Muhao Chen},
    journal={arXiv preprint arXiv:2406.09411},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09411},
    primaryClass={cs.CV cs.AI cs.CL}
}