Graph convolutional networks (GCNs) are widely adopted in skeleton-based action
recognition due to their powerful ability to model data topology. We argue that the …

GraphSAGE is a widely-used graph neural network for classification, which generates node
embeddings in two steps: sampling and aggregation. In this paper, we introduce causal …

With the development of artificial intelligence and human-computer interaction technology,
gesture has been widely used in intelligent vehicles, human-computer interaction, virtual …

Estimating the 3D pose of a hand from a 2D image is a well-studied problem and a
requirement for several real-life applications such as virtual reality, augmented reality, and …

Due to the significant computational challenge of training large-scale graph neural networks
(GNNs), various sparse learning techniques have been exploited to reduce memory and …

Human Action Recognition plays a driving engine of many human-computer interaction
applications. Most current researches focus on improving the model generalization by …

Dynamic hand gesture recognition has evolved as a prominent topic of computer vision
research due to its vast applications in human–computer interaction, robotics, and other …

Hand gesture recognition can benefit from directly processing 3D point cloud sequences,
which carry rich geometric information and enable the learning of expressive spatio …

With the development of cost-effective depth sensors, skeleton-based dynamic hand gesture
recognition has made significant progress. Existing methods mostly utilize a single model to …

Recognizing traffic command gestures with high accuracy and quick response at a low
computational cost is a requisite for driver assistance or autonomous driving. However, it …