Spatio-Temporal Self-supervision for Few-Shot Action Recognition

Few-shot action recognition aims to classify unseen action classes with limited labeled training samples. Most current works follow the metric learning technology to learn a good embedding and an appropriate comparison metric. Due to the limited labeled data, the generalization of embedding networks is limited when employing the meta-learning process with episodic tasks. In this paper, we aim to repurpose self-supervised learning to learn a more generalized few-shot embedding model. Specifically, a Spatio-Temporal Self-supervision (STS) framework for few-shot action recognition is proposed to generate self-supervision loss at the spatial and temporal levels as auxiliary losses. By this means, the proposed STS can provide a robust representation for few-shot action recognition. Furthermore, we propose a Spatio-Temporal Aggregation (STA) module that accounts for the spatial information relationship among all frames within a video sequence to achieve optimal video embedding. Experiments on several challenging few-shot action recognition benchmarks show the effectiveness of the proposed method in achieving state-of-the-art performance for few-shot action recognition.