Summary of Edge: Enhanced Grounded Gui Understanding with Enriched Multi-granularity Synthetic Data, by Xuetian Chen et al.
EDGE: Enhanced Grounded GUI Understanding with Enriched Multi-Granularity Synthetic Data
by Xuetian Chen, Hangcheng Li, Jiaqing Liang, Sihang Jiang, Deqing Yang
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 research paper proposes a data-driven approach to enhance the understanding and interacting capabilities of large vision-language models (LVLMs) on graphical user interfaces (GUIs). Unlike LLM-based methods that rely on structured texts, LVLM approaches can visually perceive and interact with screens directly. The authors develop EDGE, a framework for generating high-quality training data from webpages across the Web. They demonstrate superior webpage understanding capabilities through evaluation results on various GUI and agent benchmarks. This approach reduces manual annotations, enabling researchers to harness public resources for advancing their work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand and interact with screens like we do! It’s about making computers smarter at looking at pictures and text together. Right now, these “large vision-language models” are only good at understanding written text, but they can’t see or touch things on the screen. The researchers want to change that by teaching them how to look at web pages and understand what’s going on. They made a special tool called EDGE that helps create lots of training data from all over the internet. This makes computers better at understanding screens, which is helpful for many areas, like making robots or virtual assistants. |
