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 …

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 …

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

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 …

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

Remote sensing image object detection with deep neural networks has been highly
successful, but it heavily relies on a large number of labeled samples for optimal …

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 …

Nowadays, the focus on few-shot object detection (FSOD) is fueled by limited remote
sensing data availability. In view of various challenges posed by remote sensing images …

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 …

Presently, the task of few-shot object detection (FSOD) in remote sensing images (RSIs) has
become a focal point of attention. Numerous few-shot detectors, particularly those based on …