Focus Like a Human: Efficient GUI Grounding via Coarse-to-Fine Visual Attention and Parallel Verification

摘要

Building upon powerful Large Visual Language Models, recent GUI agents have revolutionized autonomous GUI interaction. Given the high information density and structural complexity of GUI layouts, a critical challenge lies in accurately identifying where to focus, i.e., precise GUI grounding. To ensure grounding accuracy, prior approaches predominantly rely on processing inputs at native high resolutions. However, this paradigm inevitably introduces excessive visual redundancy, resulting in increased computational cost and dispersed attention. In our preliminary analysis, we observe that while high-resolution inputs are indispensable for recognizing fine-grained visual details, they paradoxically impair the model’s ability to attend to the correct regions. In contrast, low-resolution inputs, which suppress high-frequency details while preserving global layout structure through downsampling, produce significantly more robust and coherent attention patterns that better guide the model toward relevant regions. Interestingly, this observation mirrors the human visual system, which first leverages low-resolution peripheral vision to identify salient areas before conducting a detailed examination via high-resolution foveal focus. Motivated by this insight, we propose a coarse-to-fine GUI grounding framework termed FastFocus. Our approach first exploits the stability of low-resolution attention to efficiently generate candidate grounding regions. These candidates are then evaluated by a Parallel Verification module, which selectively zooms into high-resolution views to resolve fine details and filter out false positives. Experiments on ScreenSpot-Pro demonstrate state-of-the-art performance, validating that a hierarchical integration of low-resolution guidance and high-resolution verification constitutes an effective and robust paradigm for GUI grounding.