  Appearance-based gaze estimation has shown great promise in many applications
by using a single general-purpose camera as the input device. However, its
success is highly depending on the availability of large-scale well-annotated
gaze datasets, which are sparse and expensive to collect. To alleviate this
challenge we propose ConGaze, a contrastive learning-based framework that
leverages unlabeled facial images to learn generic gaze-aware representations
across subjects in an unsupervised way. Specifically, we introduce the
gaze-specific data augmentation to preserve the gaze-semantic features and
maintain the gaze consistency, which are proven to be crucial for effective
contrastive gaze representation learning. Moreover, we devise a novel
subject-conditional projection module that encourages a share feature extractor
to learn gaze-aware and generic representations. Our experiments on three
public gaze estimation datasets show that ConGaze outperforms existing
unsupervised learning solutions by 6.7% to 22.5%; and achieves 15.1% to 24.6%
improvement over its supervised learning-based counterpart in cross-dataset
evaluations.
