Object detection has achieved a huge breakthrough with deep neural networks and massive
annotated data. However, current detection methods cannot be directly transferred to the …

With the abomination of time-consuming or even impractical large-scale labeling, few-shot
object detection (FSOD) based on natural scenes has attracted extensive attention …

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 …

Few-shot object detection (FSOD) aims to detect novel targets with only a few instances of
the associated samples. Although combinations of distillation techniques and meta-learning …

Once only a few-shot annotated samples are available the performance of learning-based
object detection would be heavily dropped. Many few-shot object detection (FSOD) methods …

Railway intrusion detection plays an important role in the railway intelligent transportation
system, assisting the safe operation of trains. The existing deep-learning-based object …

Recently, few-shot learning based on meta-learning has shown great potential in
hyperspectral image classification (HSIC) due to its excellent adaptability to limited training …

Few-shot image generation as an important yet challenging visual task still suffers from the
trade-off between generation quality and diversity. According to the principle of feature …

Object detection is a critical field in computer vision focusing on accurately identifying and
locating specific objects in images or videos. Traditional methods for object detection rely on …

Few-shot object detection (FSOD) aims to address the challenge of requiring a substantial
number of annotations for training in conventional object detection, which is very labor …