Action-Attending Graphic Neural Network

TL;DR

This paper introduces an end-to-end graph neural network that models skeletons as attribute graphs, employs spectral filtering and attention mechanisms to focus on salient joints, and uses recurrent encoding for improved human action recognition.

Contribution

The proposed A$^2$GNN integrates spectral graph filtering, action-attending layers, and recurrent encoding, offering a novel end-to-end framework for skeleton-based action recognition.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively identifies salient joints for action analysis.

Demonstrates robustness across multiple datasets.

Abstract

The motion analysis of human skeletons is crucial for human action recognition, which is one of the most active topics in computer vision. In this paper, we propose a fully end-to-end action-attending graphic neural network (A$^2$GNN) for skeleton-based action recognition, in which each irregular skeleton is structured as an undirected attribute graph. To extract high-level semantic representation from skeletons, we perform the local spectral graph filtering on the constructed attribute graphs like the standard image convolution operation. Considering not all joints are informative for action analysis, we design an action-attending layer to detect those salient action units (AUs) by adaptively weighting skeletal joints. Herein the filtering responses are parameterized into a weighting function irrelevant to the order of input nodes. To further encode continuous motion variations, the deep features learnt from skeletal graphs are gathered along consecutive temporal slices and then fed into a recurrent gated network. Finally, the spectral graph filtering, action-attending and recurrent temporal encoding are integrated together to jointly train for the sake of robust action recognition as well as the intelligibility of human actions. To evaluate our A$^2$GNN, we conduct extensive experiments on four benchmark skeleton-based action datasets, including the large-scale challenging NTU RGB+D dataset. The experimental results demonstrate that our network achieves the state-of-the-art performances.