Data pretaining to Chapter 2 of the PhD dissertation: "Resource-efficient Gaze Estimation"

This dataset accompanies the paper "Resource-efficient Gaze Estimation via Frequency-domain Multi-task Contrastive Learning" (Chapter 2 of the PhD dissertation). This research proposes a resource-efficient framework for gaze representation learning to improve data efficiency and computational efficiency. 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 contrastive learning-based framework for unsupervised gaze representation learning. This dataset contains codes to reproduce the results.