Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

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 …

Recent advances in deep learning have led to the development of accurate and efficient
models for various computer vision applications such as classification, segmentation, and …

Most object detection methods for autonomous driving usually assume a onsistent feature
distribution between training and testing data, which is not always the case when weathers …

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

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

Convolutional neural network (CNN)-based autonomous driving object detection algorithms
have excellent detection results on conventional datasets, but the detector performance can …

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 …