GUICourse: From General Vision Language Models to Versatile GUI Agents
On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models
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Abstract (arXiv)
Utilizing Graphic User Interface (GUI) for human-computer interaction is essential for accessing a wide range of digital tools. Recent advancements in Vision Language Models (VLMs) highlight the compelling potential to develop versatile agents to help humans finish GUI navigation tasks. However, current VLMs are challenged in terms of fundamental abilities (OCR and grounding) and GUI knowledge (the functions and control methods of GUI elements), preventing them from becoming practical GUI agents. To solve these challenges, we contribute GUICourse, a suite of datasets to train visual-based GUI agents from general VLMs. First, we introduce the GUIEnv dataset to strengthen the OCR and grounding capabilities of VLMs. Then, we introduce the GUIAct and GUIChat datasets to enrich their knowledge of GUI components and interactions. Experiments demonstrate that our GUI agents have better performance on common GUI tasks than their baseline VLMs. Even the small-size GUI agent (with 3.1B parameters) can still work well on single-step and multi-step GUI tasks. Finally, we analyze the different varieties in the training stage of this agent by ablation study. Our source codes and datasets are released at https://github.com/yiye3/GUICourse.
Key facts. Cited by 114 papers in the map (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 50 of its 72 references that are in the map. First posted on arXiv 17 Jun 2024.
Builds on
50 of 72 references.
Closest ancestors · 3
Same direction, newest first
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- 2023CogAgent: A Visual Language Model for GUI Agents14 Dec
- 2023GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation13 Nov
Other references · 47
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- 2023From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfacescited by 68
- 2023LASER: LLM Agent with State-Space Exploration for Web Navigationcited by 66
- 2022A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibilitycited by 61
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- 2021Grounding Open-Domain Instructions to Automate Web Support Taskscited by 43
- 2024Exploring Perceptual Limitation of Multimodal Large Language Modelscited by 25
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Citing papers per year: 2024: 15; 2025: 63; 2026: 36.
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