Abstract
Uncertainty in gaze estimation manifests in two aspects: 1) low-quality images caused by occlusion, blurriness, inconsistent eye movements, or even non-face images; 2) incorrect labels resulting from the misalignment between the labeled and actual gaze points during the annotation process. Allowing these uncertainties to participate in training hinders the improvement of gaze estimation. To tackle these challenges, in this paper, we propose an effective solution, named Suppressing Uncertainty in Gaze Estimation (SUGE), which introduces a novel triplet-label consistency measurement to estimate and reduce the uncertainties. Specifically, for each training sample, we propose to estimate a novel ``neighboring label'' calculated by a linearly weighted projection from the neighbors to capture the similarity relationship between image features and their corresponding labels, which can be incorporated with the predicted pseudo label and ground-truth label for uncertainty estimation. By modeling such triplet-label consistency, we can measure the qualities of both images and labels, and further largely reduce the negative effects of unqualified images and wrong labels through our designed sample weighting and label correction strategies. Experimental results on the gaze estimation benchmarks indicate that our proposed SUGE achieves state-of-the-art performance.
Abstract (translated)
凝视估计中的不确定性体现在两个方面：1) 由遮挡、模糊、不一致的眼部运动，甚至是非人脸图像引起的低质量图像；2) 在标注过程中，标记的注视点与实际注视点之间的错位导致错误标签。允许这些不确定性参与训练会阻碍凝视估计算法性能的提升。为解决这些问题，在本文中我们提出了一种有效的解决方案，称为抑制凝视估计中的不确定性（SUGE），该方法引入了一种新的三重标签一致性测量方式来估算并减少不确定性。具体来说，对于每个训练样本，我们建议估算一个通过从邻居处进行线性加权投影计算得出的新型“邻近标签”，以捕捉图像特征与其对应标签之间的相似关系。此邻近标签可以与预测的伪标签和真实标签一起用于不确定性估计。通过建模这种三重标签一致性，我们可以衡量图像和标签的质量，并进一步通过我们设计的样本权重调整及标签校正策略大幅减少不合格图像和错误标签带来的负面影响。在凝视估计算法基准测试中的实验结果表明，我们提出的SUGE达到了最先进的性能水平。
URL
https://arxiv.org/abs/2412.12890
