Resource-efficient Gaze Estimation via Frequency-domain Multi-task Contrastive Learning

Gaze estimation is of great importance to many scientific fields and daily applications, ranging from fundamental research in cognitive psychology to attention-aware systems. While recent advancements in deep learning have led to highly accurate gaze estimation systems, these solutions often come with high computational costs and depend on large-scale labeled gaze data for supervised learning, posing significant practical challenges. To move beyond these limitations, we present EfficientGaze, a resource-efficient framework for gaze representation learning. We introduce the frequency-domain gaze estimation, which exploits the feature extraction capability and the spectral compaction property of discrete cosine transform to substantially reduce the computational cost of gaze estimation systems for both calibration and inference. Moreover, to overcome the data labeling hurdle, we design a novel multi-task gaze-aware contrastive learning framework to learn gaze representations that are generic across subjects in an unsupervised manner. Our evaluation on two gaze estimation datasets demonstrates that EfficientGaze achieves comparable gaze estimation performance to existing supervised learning-based approaches, while enabling up to 6.80 times and 1.67 times speedup in system calibration and gaze estimation, respectively.