DeskVision: Large Scale Desktop Region Captioning for Advanced GUI Agents

• URL: http://arxiv.org/abs/2503.11170v1
• Date: Fri, 14 Mar 2025 08:16:02 GMT
• Title: DeskVision: Large Scale Desktop Region Captioning for Advanced GUI Agents
• Authors: Yibin Xu, Liang Yang, Hao Chen, Hua Wang, Zhi Chen, Yaohua Tang,
• Abstract summary: We propose an automated GUI data generation pipeline, AutoCaptioner, which generates data with rich descriptions while minimizing human effort.<n>We create a novel large-scale desktop GUI dataset, DeskVision, which reflects daily usage and covers diverse systems and UI elements.<n>We train a new GUI understanding model, GUIExplorer, which achieves state-of-the-art (SOTA) performance in understanding/grounding visual elements.
• Score: 17.20455408001344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The limitation of graphical user interface (GUI) data has been a significant barrier to the development of GUI agents today, especially for the desktop / computer use scenarios. To address this, we propose an automated GUI data generation pipeline, AutoCaptioner, which generates data with rich descriptions while minimizing human effort. Using AutoCaptioner, we created a novel large-scale desktop GUI dataset, DeskVision, along with the largest desktop test benchmark, DeskVision-Eval, which reflects daily usage and covers diverse systems and UI elements, each with rich descriptions. With DeskVision, we train a new GUI understanding model, GUIExplorer. Results show that GUIExplorer achieves state-of-the-art (SOTA) performance in understanding/grounding visual elements without the need for complex architectural designs. We further validated the effectiveness of the DeskVision dataset through ablation studies on various large visual language models (LVLMs). We believe that AutoCaptioner and DeskVision will significantly advance the development of GUI agents, and will open-source them for the community.

Related papers

• SpiritSight Agent: Advanced GUI Agent with One Look [7.470506991479107]
  An ideal Graphical User Interface (GUI) agent is expected to achieve high accuracy, low latency, and compatibility. Recent vision-based approaches have shown promise by leveraging advanced Vision Language Models (VLMs) We propose $textbfSpiritSight$, a vision-based, end-to-end GUI agent that excels in GUI navigation tasks.
  arXiv  Detail & Related papers  (2025-03-05T05:30:22Z)
• Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction [69.57190742976091]
  We introduce Aguvis, a unified vision-based framework for autonomous GUI agents.<n>Our approach leverages image-based observations, and grounding instructions in natural language to visual elements.<n>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)
• 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)
• OS-ATLAS: A Foundation Action Model for Generalist GUI Agents [55.37173845836839]
  OS-Atlas is a foundational GUI action model that excels at GUI grounding and OOD agentic tasks. We are releasing the largest open-source cross-platform GUI grounding corpus to date, which contains over 13 million GUI elements.
  arXiv  Detail & Related papers  (2024-10-30T17:10:19Z)
• GUICourse: From General Vision Language Models to Versatile GUI Agents [75.5150601913659]
  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.
  arXiv  Detail & Related papers  (2024-06-17T08:30:55Z)
• 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)
• VideoGUI: A Benchmark for GUI Automation from Instructional Videos [78.97292966276706]
  VideoGUI is a novel multi-modal benchmark designed to evaluate GUI assistants on visual-centric GUI tasks. Sourced from high-quality web instructional videos, our benchmark focuses on tasks involving professional and novel software. Our evaluation reveals that even the SoTA large multimodal model GPT4o performs poorly on visual-centric GUI tasks.
  arXiv  Detail & Related papers  (2024-06-14T17:59:08Z)
• 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.