Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …

LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously,
capture precise geometry of the surrounding environment and are crucial to many …

Instance contrast for unsupervised representation learning has achieved great success in
recent years. In this work, we explore the idea of instance contrastive learning in …

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) …

Video analysis tasks such as action recognition have received increasing research interest
with growing applications in fields such as smart healthcare, thanks to the introduction of …

Though unsupervised domain adaptation (UDA) has achieved very impressive progress
recently, it remains a great challenge due to missing target annotations and the rich …

Unsupervised domain adaptation (UDA) involves a supervised loss in a labeled source
domain and an unsupervised loss in an unlabeled target domain, which often faces more …

Semi-supervised semantic segmentation learns from small amounts of labelled images and
large amounts of unlabelled images, which has witnessed impressive progress with the …

Unsupervised domain adaptive video action recognition aims to recognize actions of a
target domain using a model trained with only out-of-domain (source) annotations. The …

The defect diagnosis of modern infrastructures is crucial to public safety. In this work, we
propose an unsupervised domain adaptive crack recognition framework. To fulfill the …