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.

利用外观为基础的凝视估计具有很大潜力，通过使用单个通用摄像头作为输入设备，但是在很大程度上取决于大规模且经过良好注释的凝视数据集的可用性，这种数据集是稀缺且昂贵的。为了解决这个挑战，我们提出了一种基于对比学习的框架 ConGaze，该框架利用未标记的面部图像以无监督的方式跨主体学习通用的凝视感知表示。我们的实验证明 ConGaze 在三个公共凝视估计数据集上胜过现有的无监督学习解决方案 6.7％至 22.5％；并且在跨数据集评估中相对于基于监督学习的模型，取得了 15.1％至 24.6％的改进。