Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition

TL;DR

This paper introduces a part-aware prototypical graph network that captures both global and local skeletal motion patterns for improved one-shot skeleton-based action recognition, especially for fine-grained actions.

Contribution

It proposes a dual-level representation and a class-agnostic attention mechanism to enhance transferability and discrimination in one-shot action recognition.

Findings

Effective on NTU RGB+D 120 dataset

Outperforms existing methods in fine-grained recognition

Demonstrates robustness to subtle motion differences

Abstract

In this paper, we study the problem of one-shot skeleton-based action recognition, which poses unique challenges in learning transferable representation from base classes to novel classes, particularly for fine-grained actions. Existing meta-learning frameworks typically rely on the body-level representations in spatial dimension, which limits the generalisation to capture subtle visual differences in the fine-grained label space. To overcome the above limitation, we propose a part-aware prototypical representation for one-shot skeleton-based action recognition. Our method captures skeleton motion patterns at two distinctive spatial levels, one for global contexts among all body joints, referred to as body level, and the other attends to local spatial regions of body parts, referred to as the part level. We also devise a class-agnostic attention mechanism to highlight important parts for each action class. Specifically, we develop a part-aware prototypical graph network consisting of three modules: a cascaded embedding module for our dual-level modelling, an attention-based part fusion module to fuse parts and generate part-aware prototypes, and a matching module to perform classification with the part-aware representations. We demonstrate the effectiveness of our method on two public skeleton-based action recognition datasets: NTU RGB+D 120 and NW-UCLA.