Despite the commercial abundance of UAVs aerial data acquisition remains challenging
and the existing Asia and North America-centric open-source UAV datasets are small-scale …

This paper presents the findings and results of the third iteration of the Multi-modal Aerial
View Object Classification (MAVOC) challenge in a detailed and comprehensive manner …

Deep neural networks tend to reciprocate the bias of their training dataset. In object
detection, the bias exists in the form of various imbalances such as class, background …

The emergence of unmanned aerial vehicles (UAVs) raised multiple concerns, given their
potential for malicious misuse in unlawful acts Vision-based counter-UAV applications offer …

Multi-scale infrared unmanned aerial vehicle (UAV) targets (IRUTs) detection under dynamic
scenarios remains a challenging task due to weak target features, varying shapes and …

Arbitrary-oriented vehicle detection via aerial imagery is essential in remote sensing and
computer vision, with various applications in traffic management, disaster monitoring, smart …

Detecting objects in large-scale drone-view images is notoriously challenging due to their
uneven distribution and scale variation caused by photoing angles. Common approaches …

In this paper, we introduce the first Challenge on Multimodal Aerial View Object
Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This …

Vehicle detection in aerial imagery is a challenging task due to small object sizes, high
object density and partial occlusions. While past research mostly focused on improving …

In recent years, deep learning based computer vision technology has progressed rapidly
thanks to the significant increases in computing power and high-quality datasets. In this …