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Deep neural networks have demonstrated superior performance onappearance-based gaze estimation tasks. However, due to variations in person,illuminations, and background, performance degrades dramatically when applyingthe model to a new domain. In this paper, we discover an interesting gazejitter phenomenon in cross-domain gaze estimation, i.e., the gaze predictionsof two similar images can be severely deviated in target domain. This isclosely related to cross-domain gaze estimation tasks, but surprisingly, it hasnot been noticed yet previously. Therefore, we innovatively propose to utilizethe gaze jitter to analyze and optimize the gaze domain adaptation task. Wefind that the high-frequency component (HFC) is an important factor that leadsto jitter. Based on this discovery, we add high-frequency components to inputimages using the adversarial attack and employ contrastive learning toencourage the model to obtain similar representations between original andperturbed data, which reduces the impacts of HFC. We evaluate the proposedmethod on four cross-domain gaze estimation tasks, and experimental resultsdemonstrate that it significantly reduces the gaze jitter and improves the gazeestimation performance in target domains.

Jitter Does Matter: Adapting Gaze Estimation to New Domains