With the escalated demand of human-machine interfaces for intelligent
systems, development of gaze controlled system have become a necessity. Gaze,
being the non-intrusive form of human interaction, is one of the best suited
approach. Appearance based deep learning models are the most widely used for
gaze estimation. But the performance of these models is entirely influenced by
the size of labeled gaze dataset and in effect affects generalization in
performance. This paper aims to develop a semi-supervised contrastive learning
framework for estimation of gaze direction. With a small labeled gaze dataset,
the framework is able to find a generalized solution even for unseen face
images. In this paper, we have proposed a new contrastive loss paradigm that
maximizes the similarity agreement between similar images and at the same time
reduces the redundancy in embedding representations. Our contrastive regression
framework shows good performance in comparison to several state of the art
contrastive learning techniques used for gaze estimation.

通过使用半监督对比学习框架，本文提出了一种新的对比损失范式，从而实现了基于注视方向的估计，该框架在使用少量带有标签的注视数据集时，可以找到泛化解决方案，即使对于未见过的人脸图像也能有良好的性能表现，与其他用于注视估计的最先进对比学习技术相比，我们的对比回归框架表现良好。