Visual tempo characterizes the dynamics and the temporal evolution, which helps describe
actions. Recent approaches directly perform visual tempo prediction on skeleton sequences …

Graph Convolutional Networks (GCNs) are widely used for skeleton-based action
recognition and achieved remarkable performance. Due to the locality of graph convolution …

With the aid of graph convolution neural network and transformer model, human action
recognition has achieved significant performance based on skeleton data. However, the …

Skeleton-based action recognition is an important task in computer vision. Recently, graph
convolutional networks (GCNs) have been successfully applied to this task and achieved …

Skeleton-based action recognition relies on capturing connections between joints to extract
action-specific features. Current approaches utilizing temporal convolution for inter-frame …

To effectively comprehend human actions, we have developed a Deep Neural Network
(DNN) that utilizes inner spatiotemporal non-locality to capture meaningful semantic context …

Human pose analysis has garnered significant attention within both the research community
and practical applications, owing to its expanding array of uses, including gaming, video …

The deployment of multi-stream fusion strategy on behavioral recognition from skeletal data
can extract complementary features from different information streams and improve the …

Human 3D skeleton-based action recognition has received increasing interest in recent
years. Inspired by the excellent ability of the multi-modal model, some pioneer attempts to …

The non-local attention mechanism has often been exploited in deep learning to capture
long-range dependencies (LRDs) from the same image for enhancing the performance of …