Factor-Informed Uncertainty Distillation for Gaze Estimation
Deep gaze estimation works well in controlled capture but degrades in unconstrained settings, where systems must reject unreliable predictions. Single-pass uncertainty (e.g., heteroscedastic regression) infers uncertainty from pixels without explicit input-validity cues, while sampling based methods are often too costly for real time use. We propose Factor-Informed Uncertainty Distillation (FIUD), a teacher-student framework that aligns uncertainty with interpretable image-quality failure modes. A gradient-boosting teacher predicts expected gaze error from factors such as illumination, sharpne
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- LinkedLinked via arxiv author · 85%Mohammadreza Jamalifard →
“Factor-Informed Uncertainty Distillation for Gaze Estimation”
- LinkedLinked via arxiv author · 85%Yaxiong Lei →
“Factor-Informed Uncertainty Distillation for Gaze Estimation”
- LinkedLinked via arxiv author · 85%Javier Fumanal-Idocin →
“Factor-Informed Uncertainty Distillation for Gaze Estimation”
- LinkedLinked via arxiv author · 85%Parastoo Azizinezhad →
“Factor-Informed Uncertainty Distillation for Gaze Estimation”
- LinkedLinked via arxiv author · 85%Tom Foulsham →
“Factor-Informed Uncertainty Distillation for Gaze Estimation”
- LinkedLinked via arxiv author · 85%Javier Andreu-Perez →
“Factor-Informed Uncertainty Distillation for Gaze Estimation”