Online class-incremental continual learning is a specific task of continual learning. It aims to
continuously learn new classes from data stream and the samples of data stream are seen …

Humans have long been recorded in a variety of forms since antiquity. For example,
sculptures and paintings were the primary media for depicting human beings before the …

Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in
autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) …

Test time adaptation (TTA) aims to adapt deep neural networks when receiving out of
distribution test domain samples. In this setting, the model can only access online unlabeled …

Abstract Domain generalization (DG) aims to learn a robust model from source domains that
generalize well on unseen target domains. Recent studies focus on generating novel …

Abstract Domain generalization studies the problem of training a model with samples from
several domains (or distributions) and then testing the model with samples from a new …

LiDAR semantic segmentation (LSS) is a critical task in autonomous driving and has
achieved promising progress. However prior LSS methods are conventionally investigated …

Anomaly detection (AD) is a crucial machine learning task that aims to learn patterns from a
set of normal training samples to identify abnormal samples in test data. Most existing AD …

Domain generalization (DG) aims at solving distribution shift problems in various scenes.
Existing approaches are based on Convolution Neural Networks (CNNs) or Vision …

In the past decade, deep neural networks have achieved significant progress in point cloud
learning. However, collecting large-scale precisely-annotated point clouds is extremely …