Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

The knowledge graph (KG) that represents structural relations among entities has become
an increasingly important research field for knowledge-driven artificial intelligence. In this …

Recent years have witnessed the resurgence of knowledge engineering which is featured
by the fast growth of knowledge graphs. However, most of existing knowledge graphs are …

Modeling the dynamics into graph neural networks (GNNs) contributes to the understanding
of evolution in dynamic graphs, which helps optimize temporal-spatial representations for …

Human behavior recognition has always been a hot spot for research in computer vision.
With the wide application of behavior recognition in virtual reality and short video in recent …

Sign language recognition is one of the most challenging applications in machine learning
and human-computer interaction. Many researchers have developed classification models …

Graph neural networks (GNN) have emerged as a new state-of-the-art for learning
knowledge graph representations. Although they have shown impressive performance in …

Image enhancement has been widely applied to medical science, industry, agriculture, and
military, for which it is very important to preserve brightness and structures. In order to …

Abstract Objects that occupy a small portion of an image or a frame contain fewer pixels and
contains less information. This makes small object detection a challenging task in computer …

Abstract Knowledge graph embedding (KGE) aims to complete link prediction tasks
effectively by learning the representation of entity and relation. Recently, deep neural …