Summary of Muirbench: a Comprehensive Benchmark For Robust Multi-image Understanding, by Fei Wang et al.
MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
by 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
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces MuirBench, a comprehensive benchmark for evaluating the robustness of multimodal language models (LLMs) in understanding multiple images. The benchmark consists of 12 diverse multi-image tasks, such as scene understanding and ordering, which involve various types of relations between images. The dataset contains 11,264 images and 2,600 questions, with each standard instance paired with an unanswerable variant having minimal semantic differences. The results show that even top-performing models like GPT-4o and Gemini Pro struggle to achieve high accuracy on MuirBench, highlighting the importance of developing multimodal LLMs that can generalize beyond single images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new benchmark for testing how well artificial intelligence (AI) language models can understand multiple images. These AI models are called Large Language Models (LLMs), and they’re really good at understanding what’s in one image. But the researchers wanted to see if these LLMs could also understand how different images relate to each other. They made a dataset with 12 tasks that test this ability, like figuring out what’s happening in a series of pictures or putting events in order. The results show that even the best AI models have trouble with some of these tasks. |
Keywords
» Artificial intelligence » Gemini » Gpt » Scene understanding
