Computer Science > Computer Vision and Pattern Recognition
[Submitted on 13 Jun 2024 (v1), last revised 2 Jul 2024 (this version, v2)]
Title:MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
View PDF HTML (experimental)Abstract: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.
Submission history
From: Fei Wang [view email][v1] Thu, 13 Jun 2024 17:59:52 UTC (19,224 KB)
[v2] Tue, 2 Jul 2024 01:56:14 UTC (19,224 KB)
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