GUICourse: From General Vision Language Models to Versatile GUI Agents

• URL: http://arxiv.org/abs/2406.11317v1
• Date: Mon, 17 Jun 2024 08:30:55 GMT
• Title: GUICourse: From General Vision Language Models to Versatile GUI Agents
• Authors: 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,
• Abstract summary: We contribute GUICourse, a suite of datasets to train visual-based GUI agents. 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.
• Score: 75.5150601913659
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• GUI-Bee: Align GUI Action Grounding to Novel Environments via Autonomous Exploration [56.58744345634623]
  We propose GUI-Bee, an MLLM-based autonomous agent, to collect high-quality, environment-specific data through exploration. We also introduce NovelScreenSpot, a benchmark for testing how well the data can help align GUI action grounding models to novel environments.
  arXiv  Detail & Related papers  (2025-01-23T18:16:21Z)
• Falcon-UI: Understanding GUI Before Following User Instructions [57.67308498231232]
  We introduce an instruction-free GUI navigation dataset, termed Insight-UI dataset, to enhance model comprehension of GUI environments. Insight-UI dataset is automatically generated from the Common Crawl corpus, simulating various platforms. We develop the GUI agent model Falcon-UI, which is initially pretrained on Insight-UI dataset and subsequently fine-tuned on Android and Web GUI datasets.
  arXiv  Detail & Related papers  (2024-12-12T15:29:36Z)
• Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction [69.57190742976091]
  We introduce Aguvis, a unified vision-based framework for autonomous GUI agents. Our approach leverages image-based observations, and grounding instructions in natural language to visual elements. To address the limitations of previous work, we integrate explicit planning and reasoning within the model.
  arXiv  Detail & Related papers  (2024-12-05T18:58:26Z)
• Ponder & Press: Advancing Visual GUI Agent towards General Computer Control [13.39115823642937]
  Ponder & Press is a divide-and-conquer framework for general computer control using only visual input. Our agent offers a versatile, human-like interaction paradigm applicable to a wide range of applications.
  arXiv  Detail & Related papers  (2024-12-02T08:35:31Z)
• ShowUI: One Vision-Language-Action Model for GUI Visual Agent [80.50062396585004]
  Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. We develop a vision-language-action model in digital world, namely ShowUI, which features the following innovations. ShowUI, a lightweight 2B model using 256K data, achieves a strong 75.1% accuracy in zero-shot screenshot grounding.
  arXiv  Detail & Related papers  (2024-11-26T14:29:47Z)
• Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents [20.08996257335876]
  We advocate a human-like embodiment for GUI agents that perceive the environment entirely visually and directly take pixel-level operations on the GUI. We collect the largest dataset for GUI visual grounding so far, containing 10M GUI elements and their referring expressions over 1.3M screenshots. We show that a simple recipe, which includes web-based synthetic data and slight adaptation of the LLaVA architecture, is surprisingly effective for training such visual grounding models.
  arXiv  Detail & Related papers  (2024-10-07T17:47:50Z)
• GUI-WORLD: A Dataset for GUI-oriented Multimodal LLM-based Agents [73.9254861755974]
  This paper introduces a new dataset, called GUI-World, which features meticulously crafted Human-MLLM annotations. We evaluate the capabilities of current state-of-the-art MLLMs, including ImageLLMs and VideoLLMs, in understanding various types of GUI content.
  arXiv  Detail & Related papers  (2024-06-16T06:56:53Z)
• SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents [17.43878828389188]
  We propose a novel visual Graphical User Interface (GUI) agent, SeeClick, which only relies on screenshots for task automation. To tackle this challenge, we propose to enhance SeeClick with GUI grounding pre-training and devise a method to automate the curation of GUI grounding data. We have also created ScreenSpot, the first realistic GUI grounding benchmark that encompasses mobile, desktop, and web environments.
  arXiv  Detail & Related papers  (2024-01-17T08:10:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.