Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

A graph structure is a powerful mathematical abstraction, which can not only represent
information about individuals but also capture the interactions between individuals for …

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 …

The graph convolutional network (GCN) has recently achieved promising performance of 3D
human pose estimation (HPE) by modeling the relationship among body parts. However …

Methods of computational pathology applied to the analysis of whole-slide images (WSIs) do
not typically consider histopathological features from the tumour microenvironment. Here …

Scene graphs are powerful representations that parse images into their abstract semantic
elements, ie, objects and their interactions, which facilitates visual comprehension and …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Co-occurrent visual patterns suggest that pixel relation modeling facilitates dense prediction
tasks, which inspires the development of numerous context modeling paradigms,\emph {eg} …

We present a method that achieves state-of-the-art results on challenging (few-shot) layout-
to-image generation tasks by accurately modeling textures, structures and relationships …

Temporal action localization, which requires a machine to recognize the location as well as
the category of action instances in videos, has long been researched in computer vision …