标题： EgoAdapt：自适应多感官蒸馏和策略学习用于高效的自我感知 显示英文标题

标题： EgoAdapt: Adaptive Multisensory Distillation and Policy Learning for Efficient Egocentric Perception

摘要： 现代感知模型，特别是那些为多感官自我中心任务设计的模型，已经取得了显著的性能，但通常伴随着巨大的计算成本。 这些高需求对现实世界的部署提出了挑战，尤其是在资源受限的环境中。 在本文中，我们引入了EgoAdapt，一种框架，通过自适应地执行跨模态蒸馏和策略学习，以实现不同自我中心感知任务的高效推理，包括自我中心动作识别、主动说话者定位和行为预测。 我们提出的策略模块可以适应特定任务的动作空间，使其具有广泛的适用性。 在三个具有挑战性的自我中心数据集EPIC-Kitchens、EasyCom和Aria Everyday Activities上的实验结果表明，我们的方法显著提高了效率，将GMACs减少了高达89.09%，参数减少了高达82.02%，能耗减少了高达9.6倍，同时在性能上与相应的最先进模型相当，并在许多情况下超过了它们。 显示英文摘要

摘要： Modern perception models, particularly those designed for multisensory egocentric tasks, have achieved remarkable performance but often come with substantial computational costs. These high demands pose challenges for real-world deployment, especially in resource-constrained environments. In this paper, we introduce EgoAdapt, a framework that adaptively performs cross-modal distillation and policy learning to enable efficient inference across different egocentric perception tasks, including egocentric action recognition, active speaker localization, and behavior anticipation. Our proposed policy module is adaptable to task-specific action spaces, making it broadly applicable. Experimental results on three challenging egocentric datasets EPIC-Kitchens, EasyCom, and Aria Everyday Activities demonstrate that our method significantly enhances efficiency, reducing GMACs by up to 89.09%, parameters up to 82.02%, and energy up to 9.6x, while still on-par and in many cases outperforming, the performance of corresponding state-of-the-art models.