Summary of Proreason: Multi-modal Proactive Reasoning with Decoupled Eyesight and Wisdom, by Jingqi Zhou et al.
ProReason: Multi-Modal Proactive Reasoning with Decoupled Eyesight and Wisdom
by Jingqi Zhou, Sheng Wang, Jingwei Dong, Lei Li, Jiahui Gao, Lingpeng Kong, Chuan Wu
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper addresses the issue that large vision-language models (LVLMs) often prioritize language knowledge over image information in visual reasoning tasks. The authors identify drawbacks in existing solutions and propose a novel framework called ProReason to tackle this problem. ProReason decomposes the visual reasoning process into two stages: visual perception and textual reasoning. It features multi-run proactive perception and decoupled vision-reasoning capabilities, allowing for seamless integration of large language models (LLMs) to compensate for LVLMs’ reasoning deficits. The authors demonstrate that ProReason outperforms existing frameworks on various benchmarks for both open-source and closed-source models, with a performance improvement of up to 15% on the MMMU benchmark when assisted by LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding pictures. Right now, these computers are really good at understanding words, but they struggle to understand what’s going on in pictures. The authors of this paper want to change that. They came up with a new way of doing things called ProReason. It’s like having two separate brains: one for looking at the picture and one for figuring out what it means. This helps computers work better together and makes them smarter. The authors tested their idea on lots of different pictures and found that it worked really well. |
