Skeleton-based action recognition is a hotspot in image processing. A key challenge of this task lies in its dependence on large, manually labeled datasets whose acquisition is costly and time-consuming. This paper devises a novel, label-efficient method for skeleton-based action recognition using graph convolutional networks (GCNs). The contribution of the proposed method resides in learning a novel acquisition function -- scoring the most informative subsets for labeling -- as the optimum of an objective function mixing data representativity, diversity and uncertainty. We also extend this approach by learning the most informative subsets using an invertible GCN which allows mapping data from ambient to latent spaces where the inherent distribution of the data is more easily captured. Extensive experiments, conducted on two challenging skeleton-based recognition datasets, show the effectiveness and the outperformance of our label-frugal GCNs against the related work.

翻译：骨架动作识别是图像处理领域的研究热点。该任务的一个关键挑战在于其对大规模人工标注数据集的依赖，而此类数据集的获取成本高昂且耗时。本文设计了一种基于图卷积网络（GCNs）的标签高效骨架动作识别新方法。所提方法的贡献在于学习一种新颖的采集函数——通过评估最具信息量的子集进行标注——作为融合数据代表性、多样性和不确定性的目标函数的最优解。我们进一步扩展了该方法，利用可逆GCN学习最具信息量的子集，该网络允许将数据从环境空间映射到潜在空间，从而更易于捕捉数据的内在分布。在两个具有挑战性的骨架识别数据集上进行的大量实验表明，我们提出的标签高效GCN方法相较于相关工作具有显著的有效性和优越性能。