One-shot skeleton action recognition, which aims to learn a skeleton action
recognition model with a single training sample, has attracted increasing
interest due to the challenge of collecting and annotating large-scale skeleton
action data. However, most existing studies match skeleton sequences by
comparing their feature vectors directly which neglects spatial structures and
temporal orders of skeleton data. This paper presents a novel one-shot skeleton
action recognition technique that handles skeleton action recognition via
multi-scale spatial-temporal feature matching. We represent skeleton data at
multiple spatial and temporal scales and achieve optimal feature matching from
two perspectives. The first is multi-scale matching which captures the
scale-wise semantic relevance of skeleton data at multiple spatial and temporal
scales simultaneously. The second is cross-scale matching which handles
different motion magnitudes and speeds by capturing sample-wise relevance
across multiple scales. Extensive experiments over three large-scale datasets
(NTU RGB+D, NTU RGB+D 120, and PKU-MMD) show that our method achieves superior
one-shot skeleton action recognition, and it outperforms the state-of-the-art
consistently by large margins.

本文提出一种新的一次性骨架动作识别技术，通过多尺度空间和时间特征匹配处理骨架动作识别，实现了优秀、卓越的一次性骨架动作识别，并在诸多大规模数据集上，持续超越当前最先进的方法。