Summary of Flowlearn: Evaluating Large Vision-language Models on Flowchart Understanding, by Huitong Pan et al.
FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding
by Huitong Pan, Qi Zhang, Cornelia Caragea, Eduard Dragut, Longin Jan Latecki
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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: This paper introduces the FlowLearn dataset, a comprehensive resource designed to enhance understanding of flowcharts. The dataset contains 14,858 flowcharts, including 3,858 scientific flowcharts sourced from literature and 10,000 simulated flowcharts created using a customizable script. Annotations include visual components, OCR, Mermaid code representation, and VQA question-answer pairs. Despite Large Vision-Language Models’ (LVLMs) success in various tasks, their effectiveness in decoding flowcharts has not been thoroughly investigated. The FlowLearn test set evaluates the performance of state-of-the-art LVLMs, identifying limitations and establishing a foundation for future enhancements. For instance, GPT-4V achieved high accuracy in counting nodes, while Claude excelled in OCR tasks. No single model excels in all tasks within the FlowLearn framework, highlighting opportunities for further development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper creates a special collection of flowcharts to help people understand complex ideas better. The collection has over 14,000 flowcharts, including ones from scientific articles and computer-generated ones. It also includes extra information like what words mean, what images are, and what code looks like. The goal is to see how well computer models can understand these flowcharts, which are important for sharing knowledge. Right now, the best models aren’t perfect at understanding flowcharts, so there’s room for improvement. |
Keywords
» Artificial intelligence » Claude » Gpt
