Unsupervised domain adaptation (UDA) aims to adapt a model trained on the source
domain (eg synthetic data) to the target domain (eg real-world data) without requiring further …

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 …

Abstract Domain generalization aims to learn a generalizable model from aknown'source
domain for variousunknown'target domains. It has been studied widely by domain …

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 …

Abstract Knowledge transfer from synthetic to real data has been widely studied to mitigate
data annotation constraints in various computer vision tasks such as semantic segmentation …

Unsupervised domain adaptive object detection aims to adapt detectors from a labelled
source domain to an unlabelled target domain. Most existing works take a two-stage strategy …

Deep learning-based 3D object detection has achieved unprecedented success with the
advent of large-scale autonomous driving datasets. However, drastic performance …

The rise of deep neural networks has led to several breakthroughs for semantic
segmentation. In spite of this, a model trained on source domain often fails to work properly …

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 …