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Few-shot action recognition aims to recognise unseen actions given a few examples. Thus, this letter proposes a model named meta relation network (Meta RN) to address such problem. This model contains two parts: a MetaNet and a relation network. Relation network is utilised to extract video features and classify actions. A second-order pooling followed by power normalization is used for feature enhancement, and target videos are finally classified by exploring nonlinear distance relations. The MetaNet module is designed to model different task distributions and generate task-adaptive parameters for the embedding layer of the relation network in different tasks. Experimental results on two public action recognition datasets demonstrate that the network achieves higher accuracies than several state-of-the-art approaches.

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