Summary of Guicourse: From General Vision Language Models to Versatile Gui Agents, by Wentong Chen et al.
GUICourse: From General Vision Language Models to Versatile GUI Agents
by Wentong Chen, Junbo Cui, Jinyi Hu, Yujia Qin, Junjie Fang, Yue Zhao, Chongyi Wang, Jun Liu, Guirong Chen, Yupeng Huo, Yuan Yao, Yankai Lin, Zhiyuan Liu, Maosong Sun
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
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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 proposed GUICourse dataset aims to improve the performance of Vision Language Models (VLMs) in completing Graphic User Interface (GUI) navigation tasks. Current VLMs struggle with fundamental abilities like Optical Character Recognition (OCR) and grounding, as well as GUI knowledge, hindering their ability to become practical GUI agents. To address this, GUICourse consists of three datasets: GUIEnv for strengthening OCR and grounding capabilities, GUIAct and GUIChat for enriching GUI component and interaction knowledge. Experimental results demonstrate that GUI agents outperform baseline VLMs on common GUI tasks, even with a small-size agent (3.1B parameters). The ablation study analyzes the effects of different training stages on agent performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces GUICourse, a set of datasets to help Vision Language Models (VLMs) complete Graphic User Interface (GUI) navigation tasks. VLMs struggle with basic abilities like reading text and understanding context, as well as knowing how GUI elements work. To solve this, the authors created three new datasets: GUIEnv for text recognition and understanding, GUIAct and GUIChat for learning about GUI components and interactions. The results show that these agents can do tasks better than regular VLMs. This is a big step towards making computers more helpful to humans. |
Keywords
» Artificial intelligence » Grounding
