WinSpot: GUI Grounding Benchmark with Multimodal Large Language Models
Graphical User Interface (GUI) automation relies on accurate GUI grounding.However, obtaining large-scale, high-quality labeled data remains a key challenge, particularly in desktop environments like Windows Operating System (OS).Existing datasets primarily focus on structured web-based elements, leaving a gap in real-world GUI interaction data for non-web applications.To address this, we introduce a new framework that leverages LLMs to generate large-scale GUI grounding data, enabling automated and scalable labeling across diverse interfaces.To ensure high accuracy and reliability, we manually validated and refined 5,000 GUI coordinate-instruction pairs, creating WinSpot-the first benchmark specifically designed for GUI grounding tasks in Windows environments.WinSpot provides a high-quality dataset for training and evaluating visual GUI agents, establishing a foundation for future research in GUI automation across diverse and unstructured desktop environments 1 .
Authors
- Dan Zhao (ORCID: https://orcid.org/0000-0002-3988-5040)
- Colby Banbury
- Kazuhito Koishida (ORCID: https://orcid.org/0000-0002-3111-5375)
- Hui Zheng (ORCID: https://orcid.org/0000-0001-6224-3620)
- Tianyi Chen (ORCID: https://orcid.org/0000-0003-3477-1439)
- Yinheng Li (ORCID: https://orcid.org/0009-0008-1813-8135)
Institutions
- Microsoft (United States) (US)
- Microsoft Research (United Kingdom) (GB)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.18653/v1/2025.acl-short.85
- Primary Topic
- Speech and dialogue systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00