  Deep neural networks have demonstrated superior performance on
appearance-based gaze estimation tasks. However, due to variations in person,
illuminations, and background, performance degrades dramatically when applying
the model to a new domain. In this paper, we discover an interesting gaze
jitter phenomenon in cross-domain gaze estimation, i.e., the gaze predictions
of two similar images can be severely deviated in target domain. This is
closely related to cross-domain gaze estimation tasks, but surprisingly, it has
not been noticed yet previously. Therefore, we innovatively propose to utilize
the gaze jitter to analyze and optimize the gaze domain adaptation task. We
find that the high-frequency component (HFC) is an important factor that leads
to jitter. Based on this discovery, we add high-frequency components to input
images using the adversarial attack and employ contrastive learning to
encourage the model to obtain similar representations between original and
perturbed data, which reduces the impacts of HFC. We evaluate the proposed
method on four cross-domain gaze estimation tasks, and experimental results
demonstrate that it significantly reduces the gaze jitter and improves the gaze
estimation performance in target domains.
