Enhancing accuracy of uncertainty estimation in appearance-based gaze tracking with probabilistic evaluation and calibration

ArXi:2501.14894v4 Announce Type: replace Accurately knowing uncertainties in appearance-based gaze tracking is critical for ensuring reliable downstream applications. Due to the lack of individual uncertainty labels, current uncertainty-aware approaches adopt probabilistic models to acquire uncertainties by following distributions in the