发表机构
University of Essex（埃塞克斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究注视估计在无约束环境中的问题，提出因子感知不确定性蒸馏框架FIUD，通过梯度提升教师预测误差、神经学生提炼信号，提升了不确定性、误差等级相关性和选择性预测，在无约束环境中效果最佳。
AI 中文摘要
深度注视估计在受控捕获环境中表现良好，但在无约束环境中会退化，此时系统必须拒绝不可靠的预测。单通道不确定性（如异方差回归）从像素中推断不确定性，而无需明确的输入有效性线索，而基于采样的方法对于实时使用来说成本往往过高。我们提出了因子感知不确定性蒸馏（FIUD），这是一种师生框架，它将不确定性与可解释的图像质量失败模式对齐。梯度提升教师从诸如光照、清晰度、眼睛可见性和对称性等因素预测预期的注视误差；神经学生通过课程学习和排序监督将这些信号提炼成一个轻量级的单通道不确定性头部。在ETH-XGaze、Gaze360和MPIIFaceGaze（超过30万个样本）上，FIUD相对于确定性和基于采样的基线提高了不确定性、误差等级相关性和选择性预测，在无约束环境中增益最大。
英文摘要
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.
Journal refETRA, 2026, 11, 1-7
