面向开放世界的手势识别
Towards Open-World Gesture Recognition
AI总结：
该研究针对真实世界手势识别中数据分布随时间变化的问题，定义了开放世界手势识别任务，提出用设计工程方法离线分析参数、对比持续学习方法，并给出腕戴式开放世界手势识别的设计准则。
摘要：
为用户提供精准的手势交互界面（例如基于腕戴设备的手势识别）是混合现实领域的一项关键挑战。然而，手势识别中的静态机器学习流程假设训练数据与测试数据来自相同的基础分布。遗憾的是，在涉及手势识别的真实世界应用中（比如基于腕戴设备的手势识别），数据分布可能会随时间发生变化。\n我们将这种让识别模型适配出现新数据模式的新任务的问题，定义为开放世界手势识别（open-world gesture recognition，OWGR）。我们提出采用持续学习方法，使机器学习模型能够适配新任务，同时不会降低在已学习任务上的性能。但围绕何时以及如何训练、部署识别模型的问题进行参数探索，需要开展耗费大量资源的用户研究，可能并不具备实用性。\n为应对这一挑战，我们提出一种设计工程方法，通过系统地检验各类参数并对比不同的持续学习方法，可在已收集的大规模数据集上开展离线分析。最后，我们提出了设计准则，以推动开放世界腕戴式手势识别流程的开发。
英文摘要：
Providing users with accurate gestural interfaces, such as gesture recognition based on wrist-worn devices, is a key challenge in mixed reality. However, static machine learning processes in gesture recognition assume that training and test data come from the same underlying distribution. Unfortunately, in real-world applications involving gesture recognition, such as gesture recognition based on wrist-worn devices, the data distribution may change over time. We formulate this problem of adapting recognition models to new tasks, where new data patterns emerge, as open-world gesture recognition (OWGR). We propose the use of continual learning to enable machine learning models to be adaptive to new tasks without degrading performance on previously learned tasks. However, the process of exploring parameters for questions around when, and how, to train and deploy recognition models requires resource-intensive user studies may be impractical. To address this challenge, we propose a design engineering approach that enables offline analysis on a collected large-scale dataset by systematically examining various parameters and comparing different continual learning methods. Finally, we provide design guidelines to enhance the development of an open-world wrist-worn gesture recognition process.
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