  Temporal action segmentation (TAS) aims to classify and locate actions in the
long untrimmed action sequence. With the success of deep learning, many deep
models for action segmentation have emerged. However, few-shot TAS is still a
challenging problem. This study proposes an efficient framework for the
few-shot skeleton-based TAS, including a data augmentation method and an
improved model. The data augmentation approach based on motion interpolation is
presented here to solve the problem of insufficient data, and can increase the
number of samples significantly by synthesizing action sequences. Besides, we
concatenate a Connectionist Temporal Classification (CTC) layer with a network
designed for skeleton-based TAS to obtain an optimized model. Leveraging CTC
can enhance the temporal alignment between prediction and ground truth and
further improve the segment-wise metrics of segmentation results. Extensive
experiments on both public and self-constructed datasets, including two
small-scale datasets and one large-scale dataset, show the effectiveness of two
proposed methods in improving the performance of the few-shot skeleton-based
TAS task.
