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 …

Point clouds provide a flexible geometric representation suitable for countless applications
in computer graphics; they also comprise the raw output of most 3D data acquisition devices …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

Geometric deep learning is an umbrella term for emerging techniques attempting to
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …

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

Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

Deep learning has achieved a remarkable performance breakthrough in several fields, most
notably in speech recognition, natural language processing, and computer vision. In …

We propose an end-to-end deep learning architecture that produces a 3D shape in
triangular mesh from a single color image. Limited by the nature of deep neural network …

We introduce a method for learning to generate the surface of 3D shapes. Our approach
represents a 3D shape as a collection of parametric surface elements and, in contrast to …