Action recognition based on skeleton data has recently witnessed increasing
attention and progress. State-of-the-art approaches adopting Graph
Convolutional networks (GCNs) can effectively extract features on human
skeletons relying on the pre-defined human topology. Despite associated
progress, GCN-based methods have difficulties to generalize across domains,
especially with different human topological structures. In this context, we
introduce UNIK, a novel skeleton-based action recognition method that is not
only effective to learn spatio-temporal features on human skeleton sequences
but also able to generalize across datasets. This is achieved by learning an
optimal dependency matrix from the uniform distribution based on a multi-head
attention mechanism. Subsequently, to study the cross-domain generalizability
of skeleton-based action recognition in real-world videos, we re-evaluate
state-of-the-art approaches as well as the proposed UNIK in light of a novel
Posetics dataset. This dataset is created from Kinetics-400 videos by
estimating, refining and filtering poses. We provide an analysis on how much
performance improves on smaller benchmark datasets after pre-training on
Posetics for the action classification task. Experimental results show that the
proposed UNIK, with pre-training on Posetics, generalizes well and outperforms
state-of-the-art when transferred onto four target action classification
datasets: Toyota Smarthome, Penn Action, NTU-RGB+D 60 and NTU-RGB+D 120.

该研究介绍了一种基于骨架数据的动作识别方法 UNIK，通过优化依赖矩阵并使用多头注意机制，实现了对于跨数据集的泛化能力，实验表明在多个数据集上，该方法均表现出超越于现有技术的表现。