Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

In recent years, the popularity of depth sensors and 3D scanners has led to a rapid
development of 3D point clouds. Semantic segmentation of point cloud, as a key step in …

DeepFake detection aims to differentiate falsified faces from real ones. Most approaches
formulate it as a binary classification problem by solely mining the local artifacts and …

The prevalence of relation networks in computer vision is in stark contrast to underexplored
point-based methods. In this paper, we explore the possibilities of local relation operators …

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 (\emph {eg,} social …

Point clouds have attracted increasing attention. Significant progress has been made in
methods for point cloud analysis, which often requires costly human annotation as …

Learning powerful discriminative features is the key for remote sensing scene classification.
Most existing approaches based on convolutional neural network (CNN) have achieved …

Personalized recommendation is ubiquitous, playing an important role in many online
services. Substantial research has been dedicated to learning vector representations of …

Data augmentation is key to improving the generalization ability of deep learning models.
Mixup is a simple and widely-used data augmentation technique that has proven effective in …