Gaze estimation methods encounter significant performance deterioration when being evaluated across different domains, because of the domain gap between the testing and training data. Existing methods try to solve this issue by reducing the deviation of data distribution, however, they ignore the existence of label deviation in the data due to the acquisition mechanism of the gaze label and the individual physiological differences. In this paper, we first point out that the influence brought by the label deviation cannot be ignored, and propose a gaze label alignment algorithm (GLA) to eliminate the label distribution deviation. Specifically, we first train the feature extractor on all domains to get domain invariant features, and then select an anchor domain to train the gaze regressor. We predict the gaze label on remaining domains and use a mapping function to align the labels. Finally, these aligned labels can be used to train gaze estimation models. Therefore, our method can be combined with any existing method. Experimental results show that our GLA method can effectively alleviate the label distribution shift, and SOTA gaze estimation methods can be further improved obviously.

翻译：视线估计方法在不同域间评估时，由于测试数据与训练数据之间存在域差异，常出现显著的性能下降。现有方法试图通过减小数据分布的偏差来解决这一问题，然而它们忽略了由于视线标签的采集机制及个体生理差异所导致的数据中标签偏差的存在。本文首先指出标签偏差带来的影响不可忽视，并提出一种视线标签对齐算法（GLA）以消除标签分布偏差。具体而言，我们首先在所有域上训练特征提取器以获得域不变特征，然后选择一个锚定域来训练视线回归器。我们预测剩余域上的视线标签，并使用映射函数对齐这些标签。最后，这些对齐后的标签可用于训练视线估计模型。因此，我们的方法可与任何现有方法结合。实验结果表明，我们的GLA方法能有效缓解标签分布偏移，并显著提升现有SOTA视线估计方法的性能。