  Skeleton-based action recognition has attracted lots of research attention.
Recently, to build an accurate skeleton-based action recognizer, a variety of
works have been proposed. Among them, some works use large model architectures
as backbones of their recognizers to boost the skeleton data representation
capability, while some other works pre-train their recognizers on external data
to enrich the knowledge. In this work, we observe that large language models
which have been extensively used in various natural language processing tasks
generally hold both large model architectures and rich implicit knowledge.
Motivated by this, we propose a novel LLM-AR framework, in which we investigate
treating the Large Language Model as an Action Recognizer. In our framework, we
propose a linguistic projection process to project each input action signal
(i.e., each skeleton sequence) into its ``sentence format'' (i.e., an ``action
sentence''). Moreover, we also incorporate our framework with several designs
to further facilitate this linguistic projection process. Extensive experiments
demonstrate the efficacy of our proposed framework.
