In skeleton-based action recognition, graph convolutional networks (GCNs), which model human body skeletons using graphical components such as nodes and connections, have achieved remarkable performance recently. However, current state-of-the-art methods for skeleton-based action recognition usually work on the assumption that the completely observed skeletons will be provided. This may be problematic to apply this assumption in real scenarios since there is always a possibility that captured skeletons are incomplete or noisy. In this work, we propose a skeleton-based action recognition method which is robust to noise information of given skeleton features. The key insight of our approach is to train a model by maximizing the mutual information between normal and noisy skeletons using a predictive coding manner. We have conducted comprehensive experiments about skeleton-based action recognition with defected skeletons using NTU-RGB+D and Kinetics-Skeleton datasets. The experimental results demonstrate that our approach achieves outstanding performance when skeleton samples are noised compared with existing state-of-the-art methods.

中文翻译：

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用于噪声鲁棒的基于骨架的动作识别的预测编码图卷积网络

在基于骨骼的动作识别中，使用节点和连接等图形组件对人体骨骼进行建模的图卷积网络（GCN）最近取得了显着的性能。然而，当前最先进的基于骨架的动作识别方法通常假设将提供完全观察到的骨架。在实际场景中应用这个假设可能有问题，因为捕获的骨架总是有可能不完整或有噪声。在这项工作中，我们提出了一种基于骨架的动作识别方法，该方法对给定骨架特征的噪声信息具有鲁棒性。我们方法的关键见解是通过使用预测编码方式最大化正常和嘈杂骨架之间的互信息来训练模型。我们使用 NTU-RGB+D 和 Kinetics-Skeleton 数据集对有缺陷的骨骼进行了基于骨骼的动作识别的综合实验。实验结果表明，与现有的最先进方法相比，我们的方法在对骨架样本进行噪声处理时取得了出色的性能。