Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Graph neural networks are powerful architectures for structured datasets. However, current
methods struggle to represent long-range dependencies. Scaling the depth or width of …

Most of the recent deep learning-based 3D human pose and mesh estimation methods
regress the pose and shape parameters of human mesh models, such as SMPL and MANO …

Graph convolutional network (GCN) has been successfully applied to many graph-based
applications; however, training a large-scale GCN remains challenging. Current SGD-based …

Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …

Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

Deep learning has been shown to be successful in a number of domains, ranging from
acoustics, images, to natural language processing. However, applying deep learning to the …

Graph convolutional network (GCN) has achieved great success in single hand
reconstruction task, while interacting two-hand reconstruction by GCN remains unexplored …