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Read Anywhere Pointed: Layout-aware GUI Screen Reading with Tree-of-Lens Grounding

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arxiv 2406.19263 v2 pith:FAZDVGYQ submitted 2024-06-27 cs.CL cs.CV

classification cs.CLcs.CV
keywords agentscreenreadingtasklayoutmodelsscreenprefforts
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 1 Pith paper

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  1. TRISHUL: Towards Region Identification and Screen Hierarchy Understanding for Large VLM based GUI Agents

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A training-free agentic framework, combining SAM-based region parsing with spatially enhanced element descriptions, improves LVLM action grounding and GUI referring on multiple benchmarks.

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