Aiming to generalize the well-trained gaze estimation model to new target domains, Cross-domain Gaze Estimation (CDGE) is developed for real-world application scenarios. Existing CDGE methods typically extract the domain-invariant features to mitigate domain shift in feature space, which is proved insufficient by Generalized Label Shift (GLS) theory. In this paper, we introduce a novel GLS perspective to CDGE and modelize the cross-domain problem by label and conditional shift problem. A GLS correction framework is presented and a feasible realization is proposed, in which a importance reweighting strategy based on truncated Gaussian distribution is introduced to overcome the continuity challenges in label shift correction. To embed the reweighted source distribution to conditional invariant learning, we further derive a probability-aware estimation of conditional operator discrepancy. Extensive experiments on standard CDGE tasks with different backbone models validate the superior generalization capability across domain and applicability on various models of proposed method.

翻译：为将训练良好的视线估计模型泛化至新的目标域，跨域视线估计（CDGE）被开发用于实际应用场景。现有CDGE方法通常通过提取域不变特征来缓解特征空间的域偏移，但广义标签偏移（GLS）理论证明此方法存在不足。本文提出一种新颖的GLS视角来研究CDGE，将跨域问题建模为标签偏移与条件偏移问题。我们提出一个GLS校正框架并给出可行实现方案，其中引入基于截断高斯分布的重要性重加权策略以解决标签偏移校正中的连续性挑战。为将重加权后的源分布嵌入条件不变性学习，我们进一步推导出条件算子差异的概率感知估计方法。在不同骨干模型的标准CDGE任务上进行的大量实验验证了所提方法在跨域泛化能力上的优越性及其对多种模型的适用性。