Abstract Graph Convolutional Networks (GCNs) have been widely used to model the high-
order dynamic dependencies for skeleton-based action recognition. Most existing …

Human action recognition has been applied in many fields, such as video surveillance and
human computer interaction, where it helps to improve performance. Numerous reviews of …

In recent years, great achievements have been made in graph convolutional network (GCN)
for non-Euclidean spatial data feature extraction, especially the skeleton-based feature …

Graph-based reasoning over skeleton data has emerged as a promising approach for
human action recognition. However, the application of prior graph-based methods, which …

Geometric Deep Learning techniques have become a transformative force in the field of
Computer-Aided Design (CAD), and have the potential to revolutionize how designers and …

Humans learn movements naturally, but it takes a lot of time and training to achieve expert
performance in motor skills. In this review, we show how modern technologies can support …

Pedestrian trajectory prediction in first-person view is an important support for achieving fully
automated driving in cities. However, existing pedestrian trajectory prediction methods still …

Multivariate time series classification is widely applicable to finance, healthcare, and
meteorology; therefore, it is a valuable data-mining task. However, existing methods rely …

Deep Learning models have become dominant in tackling financial time-series analysis
problems, overturning conventional machine learning and statistical methods. Most often, a …

Deep neural networks have been widely used for feature learning in facial expression
recognition systems. However, small datasets and large intra-class variability can lead to …