MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
- Fei Wang ,
- Xingyu Fu ,
- James Y. Huang ,
- Zekun Li ,
- Qin Liu ,
- Xiaogeng Liu ,
- Mingyu Derek Ma ,
- Nan Xu ,
- Wenxuan Zhou ,
- Kai Zhang ,
- T. Yan ,
- W. Mo ,
- Hsiang-Hui Liu ,
- Pan Lu ,
- Chunyuan Li ,
- Chaowei Xiao ,
- Kai-Wei Chang ,
- Dan Roth ,
- Sheng Zhang ,
- Hoifung Poon ,
- Muhao Chen
ArXiv | , Vol abs/2406.09411
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.