Read Anywhere Pointed: Layout-aware GUI Screen Reading with Tree-of-Lens Grounding
- URL: http://arxiv.org/abs/2406.19263v2
- Date: Fri, 25 Oct 2024 18:16:21 GMT
- Title: Read Anywhere Pointed: Layout-aware GUI Screen Reading with Tree-of-Lens Grounding
- Authors: Yue Fan, Lei Ding, Ching-Chen Kuo, Shan Jiang, Yang Zhao, Xinze Guan, Jie Yang, Yi Zhang, Xin Eric Wang,
- Abstract summary: We propose a Tree-of-Lens (ToL) agent, utilizing a novel ToL grounding mechanism, to address the ScreenPR task.
Based on the input point coordinate and the corresponding GUI screenshot, our ToL agent constructs a Hierarchical Layout Tree.
Based on the tree, our ToL agent not only comprehends the content of the indicated area but also articulates the layout and spatial relationships between elements.
- Score: 30.624179161014283
- License:
- Abstract: Graphical User Interfaces (GUIs) are central to our interaction with digital devices and growing efforts have been made to build models for various GUI understanding tasks. However, these efforts largely overlook an important GUI-referring task: screen reading based on user-indicated points, which we name the Screen Point-and-Read (ScreenPR) task. Currently, this task is predominantly handled by rigid accessible screen reading tools, in great need of new models driven by advancements in Multimodal Large Language Models (MLLMs). In this paper, we propose a Tree-of-Lens (ToL) agent, utilizing a novel ToL grounding mechanism, to address the ScreenPR task. Based on the input point coordinate and the corresponding GUI screenshot, our ToL agent constructs a Hierarchical Layout Tree. Based on the tree, our ToL agent not only comprehends the content of the indicated area but also articulates the layout and spatial relationships between elements. Such layout information is crucial for accurately interpreting information on the screen, distinguishing our ToL agent from other screen reading tools. We also thoroughly evaluate the ToL agent against other baselines on a newly proposed ScreenPR benchmark, which includes GUIs from mobile, web, and operating systems. Last but not least, we test the ToL agent on mobile GUI navigation tasks, demonstrating its utility in identifying incorrect actions along the path of agent execution trajectories. Code and data: https://screen-point-and-read.github.io
Related papers
- AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents [50.39555842254652]
We introduce the Android Multi-annotation EXpo (AMEX) to advance research on AI agents in mobile scenarios.
AMEX comprises over 104K high-resolution screenshots from 110 popular mobile applications, which are annotated at multiple levels.
AMEX includes three levels of annotations: GUI interactive element grounding, GUI screen and element functionality descriptions, and complex natural language instructions.
arXiv Detail & Related papers (2024-07-03T17:59:58Z) - 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) - ScreenAgent: A Vision Language Model-driven Computer Control Agent [17.11085071288194]
We build an environment for a Vision Language Model (VLM) agent to interact with a real computer screen.
Within this environment, the agent can observe screenshots and manipulate the Graphics User Interface (GUI) by outputting mouse and keyboard actions.
We construct the ScreenAgent dataset, which collects screenshots and action sequences when completing a variety of daily computer tasks.
arXiv Detail & Related papers (2024-02-09T02:33:45Z) - 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) - ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation [30.693616802332745]
This paper presents a novel benchmark, AssistGUI, to evaluate whether models are capable of manipulating the mouse and keyboard on the Windows platform in response to user-requested tasks.
We propose an advanced Actor-Critic framework, which incorporates a sophisticated GUI driven by an AI agent and adept at handling lengthy procedural tasks.
arXiv Detail & Related papers (2023-12-20T15:28:38Z) - CogAgent: A Visual Language Model for GUI Agents [61.26491779502794]
We introduce CogAgent, a visual language model (VLM) specializing in GUI understanding and navigation.
By utilizing both low-resolution and high-resolution image encoders, CogAgent supports input at a resolution of 1120*1120.
CogAgent achieves the state of the art on five text-rich and four general VQA benchmarks, including VQAv2, OK-VQA, Text-VQA, ST-VQA, ChartQA, infoVQA, DocVQA, MM-Vet, and POPE.
arXiv Detail & Related papers (2023-12-14T13:20:57Z) - From Pixels to UI Actions: Learning to Follow Instructions via Graphical
User Interfaces [66.85108822706489]
This paper focuses on creating agents that interact with the digital world using the same conceptual interface that humans commonly use.
It is possible for such agents to outperform human crowdworkers on the MiniWob++ benchmark of GUI-based instruction following tasks.
arXiv Detail & Related papers (2023-05-31T23:39:18Z)
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.