UI-Zoomer: Uncertainty-Driven Adaptive Zoom-In for GUI Grounding
GUI grounding, which localizes interface elements from screenshots given natural language queries, remains challenging for small icons and dense layouts. Test-time zoom-in methods improve localization by cropping and re-running inference at higher resolution, but apply cropping uniformly across all instances with fixed crop sizes, ignoring whether the model is actually uncertain on each case. We propose \textbf{UI-Zoomer}, a training-free adaptive zoom-in framework that treats both the trigger and scale of zoom-in as a prediction uncertainty quantification problem. A confidence-aware gate fuses spatial consensus among stochastic candidates with token-level generation confidence to selecti...
| Authors | Fei Tang, Bofan Chen, Zhengxi Lu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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