Abstract
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.
Abstract (translated)
为了将训练良好的凝视估计模型推广到新的目标领域，开发了跨域凝视估计（CDGE）以应用于真实世界的场景。现有的CDGE方法通常提取领域不变特征来缓解特征空间中的领域偏移问题，但这种方法的有效性已被广义标签转移（GLS）理论证明是不足的。在本文中，我们从一个新的GLS视角引入了跨域凝视估计，并通过标签和条件转移的问题建模化了跨域问题。提出了一种GLS校正框架以及一种可行的实现方案，在该方案中，我们引入了一种基于截断高斯分布的重要性重新加权策略来克服在标签转移修正中的连续性挑战。为了将重新加权的源分布嵌入到条件不变学习中，我们进一步推导出了一种概率感知的条件操作符差异估计方法。 通过使用不同的骨干模型进行标准CDGE任务的大量实验验证了所提出的方法具有跨领域的优越泛化能力和在各种模型上的适用性。
URL
https://arxiv.org/abs/2505.13043
