A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation

Recognizing human actions can help in numerous ways, such as health monitoring, intelligent surveillance, virtual reality and human-computer interaction. A quick and accurate detection algorithm is required for daily real-time detection. This paper first proposes to generate a lightweight graph neural network by self-distillation for human action recognition tasks. The lightweight graph neural network was evaluated on the NTU-RGB+D dataset. The results demonstrate that, with competitive accuracy, the heavyweight graph neural network can be compressed by up to 80%. Furthermore, the learned representations have denser clusters, estimated by the Davies-Bouldin index, the Dunn index and silhouette coefficients. The ideal input data and algorithm capacity are also discussed.