Summary of Visiongraph: Leveraging Large Multimodal Models For Graph Theory Problems in Visual Context, by Yunxin Li et al.
VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
by Yunxin Li, Baotian Hu, Haoyuan Shi, Wei Wang, Longyue Wang, Min Zhang
First submitted to arxiv on: 8 May 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 Large Multimodal Models (LMMs) have demonstrated remarkable success in visual understanding and mathematical reasoning. However, a new challenge emerges with multimodal graph theory problems that require accurate graphical structure understanding and multi-step reasoning. To tackle this, we introduce VisionGraph, a benchmark comprising eight complex graph problem tasks, including connectivity and shortest path problems. Our Description-Program-Reasoning (DPR) chain enhances logical accuracy by generating graphical structure descriptions and employing algorithm-aware multi-step reasoning. Notably, GPT-4V outperforms Gemini Pro in multi-step graph reasoning, while LMMs exhibit inferior perception accuracy for graphical structures, affecting problem-solving performance. DPR significantly improves LMMs’ multi-step graph reasoning capabilities, with the GPT-4V (DPR) agent achieving state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using big computer models to solve visual math problems. These models are good at understanding pictures and doing simple math, but they struggle with more complex problems that involve understanding graphical structures. To help them get better, we created a new set of test problems called VisionGraph. We also developed a special way for the models to understand these problems by breaking them down into smaller steps and using algorithms to solve them. The results show that one of these models, GPT-4V, is much better than others at solving visual math problems. This is important because it can help us make progress in fields like biology, transportation, and robotics. |
Keywords
» Artificial intelligence » Gemini » Gpt
