The use of high-spatial-resolution remote sensing image technology on mobile and
embedded equipment is an important and effective way for emergency rescue and …

The rapid development of deep learning provides great convenience for production and life.
However, the massive labels required for training models limits further development. Few …

Despite its success in image synthesis, we observe that diffusion probabilistic models
(DPMs) often lack contextual reasoning ability to learn the relations among object parts in an …

Recently few-shot segmentation (FSS) has been extensively developed. Most previous
works strive to achieve generalization through the meta-learning framework derived from …

Despite the progress made by few-shot segmentation (FSS) in low-data regimes, the
generalization capability of most previous works could be fragile when countering hard …

Conventional deep CNN-based segmentation approaches have achieved satisfactory
performance in recent years, however, they are essentially Big Data-driven technologies …

Visual attention has become a popular and widely used component for image recognition.
Although various attention-based methods have been proposed and achieved relatively …

In recent years, few-shot segmentation (FSS) has received widespread attention from
scholars by virtue of its superiority in low-data regimes. Most existing research focuses on …

Few-shot image classification has attracted extensive attention, which aims to recognize
unseen classes given only a few labeled samples. Due to the large intraclass variances and …

Recently, due to the excellent representation ability of convolutional neural networks
(CNNs), object detection in remote sensing images has undergone remarkable …