In this research, we present SLYKLatent, a novel approach for enhancing gaze estimation by addressing appearance instability challenges in datasets due to aleatoric uncertainties, covariant shifts, and test domain generalization. SLYKLatent utilizes Self-Supervised Learning for initial training with facial expression datasets, followed by refinement with a patch-based tri-branch network and an inverse explained variance-weighted training loss function. Our evaluation on benchmark datasets achieves an 8.7% improvement on Gaze360, rivals top MPIIFaceGaze results, and leads on a subset of ETH-XGaze by 13%, surpassing existing methods by significant margins. Adaptability tests on RAF-DB and Affectnet show 86.4% and 60.9% accuracies, respectively. Ablation studies confirm the effectiveness of SLYKLatent's novel components. This approach has strong potential in human-robot interaction.

我们提出了一种名为SLYKLatent的新颖方法，通过解决由于不确定性、协变量偏移和测试域泛化引起的数据集外观不稳定性挑战，来增强凝视估计。该方法利用自监督学习进行初始训练，使用基于图像块的三支网络和反向解释方差加权训练损失函数进行改进。我们在基准数据集上的评估结果在Gaze360上实现了8.7%的改进，在MPIIFaceGaze上与顶级结果不相上下，在ETH-XGaze的子集上超过现有方法的13%。RAF-DB和Affectnet上的适应性测试分别显示了86.4%和60.9%的准确率。消融研究证实了SLYKLatent的新颖组件的有效性。该方法在人机交互中具有巨大潜力。

SLYKLatent，面部特征估计的学习框架