提出新视角解决跨域眼神估计泛化难题
A Generalized Label Shift Perspective for Cross-Domain Gaze Estimation
- 从标签分布偏移角度建模跨域问题
- 引入截断高斯重加权策略提升校正效果
- 适配多种主干网络，通用性强
为使训练好的眼神估计模型泛化到新目标域，跨域眼神估计（CDGE）被用于真实场景。现有方法通常提取域不变特征以缓解特征空间中的域偏移，但根据广义标签偏移（GLS）理论，这被证明是不足的。本文提出一种新的GLS视角来理解CDGE，将跨域问题建模为标签与条件分布偏移。提出一个GLS校正框架，并设计一种基于截断高斯分布的重要度重加权策略，以应对标签偏移校正中的连续性挑战。为进一步将重加权源分布嵌入条件不变学习，推导出概率感知的条件算子差异估计。在标准CDGE任务上，使用不同主干模型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对多种模型的适用性。
原文摘要 · Abstract (English)
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.
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