Abstract
Egocentric video gaze estimation requires models to capture individual gaze patterns while adapting to diverse user data. Our approach leverages a transformer-based architecture, integrating it into a PFL framework where only the most significant parameters, those exhibiting the highest rate of change during training, are selected and frozen for personalization in client models. Through extensive experimentation on the EGTEA Gaze+ and Ego4D datasets, we demonstrate that FedCPF significantly outperforms previously reported federated learning methods, achieving superior recall, precision, and F1-score. These results confirm the effectiveness of our comprehensive parameters freezing strategy in enhancing model personalization, making FedCPF a promising approach for tasks requiring both adaptability and accuracy in federated learning settings.
Abstract (translated)
基于自我的视频注视估计要求模型能够捕捉个体的注视模式，并适应多样化的用户数据。我们的方法采用了一种基于Transformer架构的方法，将其整合到一个PFL（个性化联邦学习）框架中，在此框架中，仅选择在训练过程中变化最大的重要参数进行冻结和个性化处理，以应用于客户端模型。通过在EGTEA Gaze+ 和 Ego4D 数据集上进行广泛的实验，我们展示了FedCPF方法显著优于之前报道的联邦学习方法，在召回率、精确度和F1分数方面均表现出色。这些结果确认了我们的全面参数冻结策略能够有效增强模型个性化能力，并使FedCPF成为在需要高度适应性和准确性的联邦学习任务中极具前景的方法。
URL
https://arxiv.org/abs/2502.18123
