Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of
safety assurance. However objects encountered on the road exhibit a long-tailed distribution …

Current sparsely-supervised object detection methods largely depend on high threshold
settings to derive high-quality pseudo labels from detector predictions. However hard …

Object detection (OD), a crucial vision task, remains challenged by the lack of large training
datasets with precise object localization labels. In this work, we propose ALWOD, a new …

In the realm of object detection training models with limited unlabelled data from target
domains presents significant challenges. This study focuses on the critical issue of …

Semi-supervised 3D object detection is a promising yet under-explored direction to reduce
data annotation costs, especially for cluttered indoor scenes. A few prior works, such as …

The accurate detection of relevant vehicles, pedestrians, and other targets on the road plays
a crucial role in ensuring the safety of autonomous driving. In recent years, object detectors …

Object detection on aerial images captured by Unmanned Aerial Vehicles (UAVs) has a
wide range of applications. Due to the variations in illumination, weather conditions and …

Oriented object detection has witnessed significant progress in recent years. However the
impressive performance of oriented object detectors is at the huge cost of labor-intensive …

Abstract Semi-Supervised Object Detection (SSOD) has emerged as a potent framework that
leverages unlabeled data to reduce annotation costs while enhancing model performance …

Aerial images present significant challenges to label-driven supervised learning, in
particular, the annotation of substantial small-sized objects is a highly laborious process. To …