Summary of Benchmarking Multi-image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-hop Reasoning, by Bingchen Zhao et al.
Benchmarking Multi-Image Understanding in Vision and Language Models: Perception, Knowledge, Reasoning, and Multi-Hop Reasoning
by Bingchen Zhao, Yongshuo Zong, Letian Zhang, Timothy Hospedales
First submitted to arxiv on: 18 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 Medium Difficulty Summary: The paper introduces Multi-Image Relational Benchmark (MIRB) to evaluate visual language models’ (VLMs) ability to analyze and reason across multiple images. MIRB is designed with four categories: perception, visual world knowledge, reasoning, and multi-hop reasoning. A comprehensive evaluation of various open-source and closed-source VLMs shows that while open-source models approach GPT-4V’s performance in single-image tasks, there’s a significant gap in multi-image reasoning tasks. Even state-of-the-art GPT-4V struggles with MIRB, highlighting the need for further research. The paper’s contribution of MIRB can serve as a testbed for developing next-generation multi-modal models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper creates a new way to test how well computers can understand multiple images together. Right now, we have tests that focus on one image at a time, but this doesn’t help us understand how the computer will work with many images. The researchers created a new set of challenges called MIRB (Multi-Image Relational Benchmark) that includes tasks like recognizing objects and understanding relationships between different pictures. They tested many different computer models and found that even some of the best ones struggle to do well on these multi-image tasks. This means we need to keep working on improving how computers understand images. |
Keywords
» Artificial intelligence » Gpt » Multi modal
