In the recent years, a steady growth of Hand Gesture Recognition (HGR) based applications
has been observed. Thus, significant progress has been made in the field of hand detection …

Deep-learning-based super-resolution (SR) methods have been extensively studied and
have achieved significant performance with deep convolutional neural networks. However …

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 …

Graph convolutional networks (GCNs) have attracted considerable interest in skeleton-
based action recognition. Existing GCN-based models have proposed methods to learn …

With the rapid development of remote sensing data fusion technology, heterogeneous
remote sensing image (HRSI) change detection (CD) has become a frontier field. The …

Graph convolutional networks (GCN) have attracted increasing interest in action recognition
in recent years. GCN models human skeleton sequences as spatio-temporal graphs. Also …

Skeleton-based action recognition has been substantially driven by the development of
artificial intelligence technology and deep sensors. Recently, graph convolutional networks …

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 has attracted increasing interest in recent years. With the
flexibility of modeling long-range dependency of joints, the self-attention module has served …

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