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.
Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing
Hokkaido University（北海道大学）；Graduate School of Information Science and Technology, Hokkaido University（北海道大学信息科学与技术研究院）；Faculty of Information Science and Technology, Hokkaido University（北海道大学信息科学与技术学院）
Meta-LoRA: Meta-Learning LoRA Components for Domain-Aware ID Personalization
Pixery Labs İstanbul, Türkiye（Pixery实验室伊斯坦布尔，土耳其）；METU Dept. of Computer Engineering（METU计算机工程系）；METU Dept. of Electrical and Elec. Engineering（METU电气与电子工程系）
The Visual Experience Dataset: Over 200 Recorded Hours of Integrated Eye Movement, Odometry, and Egocentric Video
Barnard College, Columbia University（哥伦比亚大学巴纳德学院）；Bates College（贝茨学院）；North Dakota State University（北达科他州立大学）；University of Nevada, Reno（内华达大学里诺分校）；Magic Leap；Technical University of Munich（慕尼黑工业大学）；Unmanned Ground Systems（无人地面系统公司）；Smith-Kettlewell Eye Research Institute（史密斯-凯特威尔眼科研究所）
