Test Time Prompt Tuning for Domain Adaptive Gaze Estimation

Although current gaze estimation methods achieve promising results in within-domain evaluations, they suffer from significant degradation when tested on real-world scenarios due to the presence of distribution shift. As collecting labeled samples entails significant costs, some methods leverage unsupervised domain adaptation (UDA) techniques to solve this problem. However, they need the source domain data during training, which may not be accessible due to privacy concerns, and tune all parameters, which may not be practical to conduct on edge devices at the test time due to computational constraints. Therefore, it is desirable to design an efficient and accurate gaze estimation method without source domain data. To achieve this goal, we propose a test time prompt tuning framework for efficient source-free domain adaptive gaze estimation. Specifically, we only learn a negligible number of parameters as prompts to adjust the gaze feature extracted from the final layer of the gaze encoder without perturbing original network. To learn meaningful prompts, an unsupervised learning loss is designed which aligns the feature distribution of target domain with the source domain and makes the estimator predict confident labels on the target domain data. The proposed method is 2.9 times faster in terms of adaptation speed than the most recent efficient method with only 5% of its updating parameters. Extensive experiments on four cross-dataset validations demonstrate the effectiveness of the proposed method.