Visual Grounding Methods for Efficient Interaction with Desktop Graphical User Interfaces

• URL: http://arxiv.org/abs/2407.01558v3
• Date: Fri, 18 Jul 2025 11:35:01 GMT
• Title: Visual Grounding Methods for Efficient Interaction with Desktop Graphical User Interfaces
• Authors: El Hassane Ettifouri, Jessica López Espejel, Laura Minkova, Tassnim Dardouri, Walid Dahhane,
• Abstract summary: Instruction Visual Grounding (IVG) is a multi-modal approach to object identification within a Graphical User Interface (GUI)<n>We propose IVGocr, which combines a Large Language Model (LLM), an object detection model, and an Optical Character Recognition (OCR) module; and IVGdirect, which uses a multimodal architecture for end-to-end grounding.<n>Our final test dataset is publicly released to support future research.
• Score: 1.3107174618549584
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Most visual grounding solutions primarily focus on realistic images. However, applications involving synthetic images, such as Graphical User Interfaces (GUIs), remain limited. This restricts the development of autonomous computer vision-powered artificial intelligence (AI) agents for automatic application interaction. Enabling AI to effectively understand and interact with GUIs is crucial to advancing automation in software testing, accessibility, and human-computer interaction. In this work, we explore Instruction Visual Grounding (IVG), a multi-modal approach to object identification within a GUI. More precisely, given a natural language instruction and a GUI screen, IVG locates the coordinates of the element on the screen where the instruction should be executed. We propose two main methods: (1) IVGocr, which combines a Large Language Model (LLM), an object detection model, and an Optical Character Recognition (OCR) module; and (2) IVGdirect, which uses a multimodal architecture for end-to-end grounding. For each method, we introduce a dedicated dataset. In addition, we propose the Central Point Validation (CPV) metric, a relaxed variant of the classical Central Proximity Score (CPS) metric. Our final test dataset is publicly released to support future research.

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