From Gaze Jitter to Domain Adaptation: Generalizing Gaze Estimation by Manipulating High-Frequency Components

Abstract

Gaze, as a pivotal indicator of human emotion, plays a crucial role in various computer vision tasks. However, the accuracy of gaze estimation often significantly deteriorates when applied to unseen environments, thereby limiting its practical value. Therefore, enhancing the generalizability of gaze estimators to new domains emerges as a critical challenge. A common limitation in existing domain adaptation research is the inability to identify and leverage truly influential factors during the adaptation process. This shortcoming often results in issues such as limited accuracy and unstable adaptation. To address this issue, this article discovers a truly influential factor in the cross-domain problem, i.e., high-frequency components (HFC). This discovery stems from an analysis of gaze jitter-a frequently overlooked but impactful issue where predictions can deviate drastically even for visually similar input images. Inspired by this discovery, we propose an “embed-then-suppress" HFC manipulation strategy to adapt gaze estimation to new domains. Our method first embeds additive HFC to the input images, then performs domain adaptation by suppressing the impact of HFC. Specifically, the suppression is carried out in a contrasive manner. Each original image is paired with its HFC-embedded version, thereby enabling our method to suppress the HFC impact through contrasting the representations within the pairs. The proposed method is evaluated across four cross-domain gaze estimation tasks. The experimental results show that it not only enhances gaze estimation accuracy but also significantly reduces gaze jitter in the target domain. Compared with previous studies, our method offers higher accuracy, reduced gaze jitter, and improved adaptation stability, marking the potential for practical deployment.