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.

翻译：本研究提出SLYKLatent，一种通过解决数据集中由偶然不确定性、协变量偏移和测试域泛化导致的外观稳定性问题来提升视线估计的新方法。SLYKLatent利用自监督学习进行面部表情数据集的初始训练，随后通过基于补丁的三分支网络和基于逆解释方差加权训练损失函数进行优化。在基准数据集上的评估显示，该方法在Gaze360上取得8.7%的提升，与MPIIFaceGaze顶尖结果相当，并在ETH-XGaze子集上以13%的优势领先现有方法，显著超越已有技术。在RAF-DB和Affectnet上的适应性测试分别达到86.4%和60.9%的准确率。消融实验证实了SLYKLatent创新组件的有效性。该方法在人机交互领域具有广阔应用潜力。