Despite the recent remarkable achievement in gaze estimation, efficient and
accurate personalization of gaze estimation without labels is a practical
problem but rarely touched on in the literature. To achieve efficient
personalization, we take inspiration from the recent advances in Natural
Language Processing (NLP) by updating a negligible number of parameters,
"prompts", at the test time. Specifically, the prompt is additionally attached
without perturbing original network and can contain less than 1% of a
ResNet-18's parameters. Our experiments show high efficiency of the prompt
tuning approach. The proposed one can be 10 times faster in terms of adaptation
speed than the methods compared. However, it is non-trivial to update the
prompt for personalized gaze estimation without labels. At the test time, it is
essential to ensure that the minimizing of particular unsupervised loss leads
to the goals of minimizing gaze estimation error. To address this difficulty,
we propose to meta-learn the prompt to ensure that its updates align with the
goal. Our experiments show that the meta-learned prompt can be effectively
adapted even with a simple symmetry loss. In addition, we experiment on four
cross-dataset validations to show the remarkable advantages of the proposed
method. Code is available at https://github.com/hmarkamcan/TPGaze.Comment: Accepted by AAAI 202