Factor-Informed Uncertainty Distillation for Gaze Estimation

• URL: http://arxiv.org/abs/2607.20072v1
• Date: Wed, 22 Jul 2026 12:20:40 GMT
• Title: Factor-Informed Uncertainty Distillation for Gaze Estimation
• Abstract summary: We propose Factor-Informed Uncertainty Distillation (FIUD) to align uncertainty with interpretable image-quality failure modes.<n>A gradient-boosting teacher predicts expected gaze error from factors such as illumination, sharpness, eye visibility and symmetry.<n>A neural student distills these signals via curriculum learning and ranking supervision into a lightweight single-pass uncertainty head.
• Score: 3.7508723681775606
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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, sharpness, eye visibility and symmetry; a neural student distills these signals via curriculum learning and ranking supervision into a lightweight single-pass uncertainty head. Across ETH-XGaze, Gaze360, and MPIIFaceGaze (>300k samples), FIUD improves uncertainty, error rank correlation and selective prediction versus deterministic and sampling-based baselines, with the largest gains in unconstrained settings.

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