Benefiting from the advanced human visual system, humans naturally classify activities and predict motions in a short time. However, most existing computer vision studies consider those two tasks separately, resulting in an insufficient understanding of human actions. Moreover, the effects of view variations remain challenging for most existing skeleton-based methods, and the existing graph operators cannot fully explore multiscale relationship. In this article, a versatile graph-based model (Vers-GNN) is proposed to deal with those two tasks simultaneously. First, a skeleton representation self-regulated scheme is proposed. It is among the first trials that successfully integrate the idea of view adaptation into a graph-based human activity analysis system. Next, several novel graph operators are proposed to model the positional relationships and learn the abstract dynamics between different human joints and parts. Finally, a practical multitask learning framework and a multiobjective self-supervised learning scheme are proposed to promote both the tasks. The comparative experimental results show that Vers-GNN outperforms the recent state-of-the-art methods for both the tasks, with the to date highest recognition accuracies on the datasets of NTU RGB + D (CV: 97.2%), UWA3D (88.7%), and CMU (1000 ms: 1.13).

中文翻译：

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通向直观人类活动理解的多功能图神经网络。

得益于先进的人类视觉系统，人类自然地对活动进行分类并在短时间内预测动作。然而，大多数现有的计算机视觉研究将这两项任务分开考虑，导致对人类行为的理解不足。此外，对于大多数现有的基于骨架的方法来说，视图变化的影响仍然具有挑战性，并且现有的图运算符无法充分探索多尺度关系。在本文中，提出了一种通用的基于图的模型（Vers-GNN）来同时处理这两个任务。首先，提出了骨架表示自调节方案。它是成功地将视图适应的想法集成到基于图形的人类活动分析系统中的首批试验之一。下一个，提出了几种新颖的图运算符来模拟位置关系并学习不同人体关节和部位之间的抽象动力学。最后，提出了实用的多任务学习框架和多目标自监督学习方案来促进这两项任务。对比实验结果表明，Vers-GNN 在这两项任务上均优于近期最先进的方法，在 NTU RGB + D（CV：97.2%）、UWA3D（88.7 %）和 CMU（1000 毫秒：1.13）。