Summary of Improved Gui Grounding Via Iterative Narrowing, by Anthony Nguyen
Improved GUI Grounding via Iterative Narrowing
by Anthony Nguyen
First submitted to arxiv on: 18 Nov 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 The paper introduces a novel visual prompting framework for enhancing the capabilities of Vision-Language Model (VLM) agents in Graphical User Interface (GUI) grounding. It builds upon previous work that fine-tuned general VLMs, such as GPT-4V, specifically for zero-shot GUI grounding. The proposed method employs an iterative narrowing mechanism to improve performance both for general and fine-tuned models. The framework is evaluated on a comprehensive benchmark comprising various UI platforms, with the code provided to reproduce the results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves how computers understand graphical user interfaces (like websites or apps) using artificial intelligence. Current AI systems are good at doing tasks like answering questions or generating text, but they struggle to understand GUIs. Researchers have been working on making these systems better at understanding GUIs by fine-tuning them for specific tasks. This paper presents a new way to make AI systems even better at understanding GUIs by using visual prompts that help them narrow down what they’re looking at. The method was tested on many different types of UI platforms and showed promising results. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Grounding » Language model » Prompting » Zero shot
