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.

翻译：尽管近期视线估计取得了显著成就，但无需标签实现高效且精准的个性化视线估计仍是一个实际问题，却鲜有文献涉及。为实现高效个性化，我们借鉴自然语言处理领域的最新进展，在测试时更新微量的参数——"提示"（prompts）。具体而言，该提示作为额外附加模块，不干扰原始网络结构，其参数量不足ResNet-18的1%。实验表明，提示调优方法具有极高效率：所提方法的适应速度比对比方法快10倍。然而，在无标签条件下更新提示以实现个性化视线估计并非易事。测试时需确保最小化特定无监督损失能导向视线估计误差最小化的目标。为解决这一难题，我们提出元学习提示机制，使其更新方向与目标保持一致。实验证明，即使仅使用简单的对称损失，元学习后的提示也能有效适应。此外，我们在四个跨数据集验证中展示了所提方法的显著优势。