AutoGUI: Scaling GUI Grounding with Automatic Functionality Annotations from LLMs

• URL: http://arxiv.org/abs/2502.01977v1
• Date: Tue, 04 Feb 2025 03:39:59 GMT
• Title: AutoGUI: Scaling GUI Grounding with Automatic Functionality Annotations from LLMs
• Authors: Hongxin Li, Jingfan Chen, Jingran Su, Yuntao Chen, Qing Li, Zhaoxiang Zhang,
• Abstract summary: We propose the methodname pipeline for automatically annotating UI elements with detailed functionality descriptions at scale. Specifically, we leverage large language models (LLMs) to infer element functionality by comparing the UI content changes before and after simulated interactions with specific UI elements. We construct an methodname-704k dataset using the proposed pipeline, featuring multi-resolution, multi-device screenshots, diverse data domains, and detailed functionality annotations that have never been provided by previous datasets.
• Score: 54.58905728115257
• License:
• Abstract: User interface understanding with vision-language models has received much attention due to its potential for enabling next-generation software automation. However, existing UI datasets either only provide large-scale context-free element annotations or contextualized functional descriptions for elements at a much smaller scale. In this work, we propose the \methodname{} pipeline for automatically annotating UI elements with detailed functionality descriptions at scale. Specifically, we leverage large language models (LLMs) to infer element functionality by comparing the UI content changes before and after simulated interactions with specific UI elements. To improve annotation quality, we propose LLM-aided rejection and verification, eliminating invalid and incorrect annotations without human labor. We construct an \methodname{}-704k dataset using the proposed pipeline, featuring multi-resolution, multi-device screenshots, diverse data domains, and detailed functionality annotations that have never been provided by previous datasets. Human evaluation shows that the AutoGUI pipeline achieves annotation correctness comparable to trained human annotators. Extensive experimental results show that our \methodname{}-704k dataset remarkably enhances VLM's UI grounding capabilities, exhibits significant scaling effects, and outperforms existing web pre-training data types. We envision AutoGUI as a scalable pipeline for generating massive data to build GUI-oriented VLMs. AutoGUI dataset can be viewed at this anonymous URL: https://autogui-project.github.io/.

Related papers

• UI-TARS: Pioneering Automated GUI Interaction with Native Agents [58.18100825673032]
  This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions. In the OSWorld benchmark, UI-TARS achieves scores of 24.6 with 50 steps and 22.7 with 15 steps, outperforming Claude (22.0 and 14.9 respectively)
  arXiv  Detail & Related papers  (2025-01-21T17:48:10Z)
• 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)
• 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)
• 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)
• Tell Me What's Next: Textual Foresight for Generic UI Representations [65.10591722192609]
  We propose Textual Foresight, a novel pretraining objective for learning UI screen representations. Textual Foresight generates global text descriptions of future UI states given a current UI and local action taken. We train with our newly constructed mobile app dataset, OpenApp, which results in the first public dataset for app UI representation learning.
  arXiv  Detail & Related papers  (2024-06-12T02:43:19Z)
• ILuvUI: Instruction-tuned LangUage-Vision modeling of UIs from Machine Conversations [13.939350184164017]
  Multimodal Vision-Language Models (VLMs) enable powerful applications from their fused understanding of images and language. We adapt a recipe for generating paired text-image training data for VLMs to the UI domain by combining existing pixel-based methods with a Large Language Model (LLM) We generate a dataset of 335K conversational examples paired with UIs that cover Q&A, UI descriptions, and planning, and use it to fine-tune a conversational VLM for UI tasks.
  arXiv  Detail & Related papers  (2023-10-07T16:32:34Z)
• Towards Better Semantic Understanding of Mobile Interfaces [7.756895821262432]
  We release a human-annotated dataset with approximately 500k unique annotations aimed at increasing the understanding of the functionality of UI elements. This dataset augments images and view hierarchies from RICO, a large dataset of mobile UIs. We also release models using image-only and multimodal inputs; we experiment with various architectures and study the benefits of using multimodal inputs on the new dataset.
  arXiv  Detail & Related papers  (2022-10-06T03:48:54Z)
• UIBert: Learning Generic Multimodal Representations for UI Understanding [12.931540149350633]
  We introduce a transformer-based joint image-text model trained through novel pre-training tasks on large-scale unlabeled UI data. Our key intuition is that the heterogeneous features in a UI are self-aligned, i.e., the image and text features of UI components, are predictive of each other. We propose five pretraining tasks utilizing this self-alignment among different features of a UI component and across various components in the same UI. We evaluate our method on nine real-world downstream UI tasks where UIBert outperforms strong multimodal baselines by up to 9.26% accuracy.
  arXiv  Detail & Related papers  (2021-07-29T03:51:36Z)
• ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces [12.52699475631247]
  We introduce a new pre-trained UI representation model called ActionBert. Our methodology is designed to leverage visual, linguistic and domain-specific features in user interaction traces to pre-train generic feature representations of UIs and their components. Experiments show that the proposed ActionBert model outperforms multi-modal baselines across all downstream tasks by up to 15.5%.
  arXiv  Detail & Related papers  (2020-12-22T20:49:52Z)

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.