Multispectral imaging and deep learning have emerged as powerful tools supporting diverse
use cases from autonomous vehicles to agriculture, infrastructure monitoring and …

Object detection (OD) is an essential and fundamental task in computer vision (CV) and
satellite image processing. Existing deep learning methods have achieved impressive …

Object detection in remote-sensing images (RSIs) relies on a large amount of labeled data
for training. However, the increasing number of new categories and class imbalance make …

In recent years, few-shot object detection (FSOD) in remote sensing images has attracted
increasing attention. Numerous studies address the challenges posed by both intra-class …

The emergence of few-shot object detection provides a new approach to address the
challenge of poor generalization ability due to data scarcity. Currently, extensive research …

The rapid development of Earth observation technology has promoted the continuous
accumulation of images in the field of remote sensing. However, a large number of remote …

Recently, deep learning-based object detection techniques have arisen alongside time-
consuming training and data collection challenges. Although few-shot learning techniques …

Few-shot object detection (FSOD) in remote-sensing images (RSIs), which aims to detect
never-seen objects with few training samples, has attracted wide attention. Some recent …

Due to the continuous development of few-shot learning, there have been notable
advancements in methods for few-shot object detection in recent years. However, most …

Few-shot object detection (FSOD) has garnered widespread attention in recent years, which
makes it possible to learn novel classes with only a handful of labeled samples. Due to the …