Factor-Informed Uncertainty Distillation for Gaze Estimation

2026-07-23 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

Factor-Informed Uncertainty Distillation (FIUD) is a novel teacher-student framework introduced in 2026 to enhance gaze estimation reliability, particularly in unconstrained environments. It addresses the limitations of existing uncertainty quantification methods, which are either computationally expensive (sampling-based) or implicitly infer uncertainty from pixels (single-pass heteroscedastic regression). FIUD employs a gradient-boosting teacher to predict expected gaze error from interpretable image-quality factors like illumination, sharpness, and eye visibility. A neural student then distills these signals using curriculum learning and ranking supervision into a lightweight, single-pass uncertainty head. Evaluated on ETH-XGaze, Gaze360, and MPIIFaceGaze (over 300k samples), FIUD significantly improves uncertainty, error rank correlation, and selective prediction compared to baselines, showing the largest gains in unconstrained settings like Gaze360.

Key takeaway

For Machine Learning Engineers developing real-time gaze estimation systems, FIUD offers a critical solution for improving reliability in unconstrained environments without incurring high latency. By explicitly aligning uncertainty with interpretable image-quality factors, your systems can make more informed accept/reject decisions, enhancing safety and user experience. Consider integrating this teacher-student distillation approach to achieve robust, efficient uncertainty quantification for critical applications like driver monitoring.

Key insights

FIUD improves gaze estimation reliability by distilling interpretable image-quality failure modes into a single-pass uncertainty head.

Principles

• Distill interpretable quality-to-error signals for robust uncertainty.
• Decouple interpretability cost from runtime utility for efficiency.

Method

A gradient-boosting teacher predicts gaze error from quality factors. A neural student distills this via curriculum learning and ranking supervision into a single-pass uncertainty head.

In practice

• Trigger abstention or re-capture on high uncertainty.
• Dynamically adjust UI elements like button hit-boxes.

Topics

• Gaze Estimation
• Uncertainty Quantification
• Knowledge Distillation
• Image Quality
• Real-time Systems
• Selective Prediction

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.