Few-shot learning (FSL) aims at recognizing a novel object under limited training samples. A
robust feature extractor (backbone) can significantly improve the recognition performance of …

High-resolution remote sensing images are now available with the progress of remote
sensing technology. With respect to popular remote sensing tasks, such as scene …

With the rapid growth of the Internet of Things (IoT), smart systems and applications are
equipped with an increasing number of wearable sensors and mobile devices. These …

Metric-based few-shot learning categorizes unseen query instances by measuring their
distance to the categories appearing in the given support set. To facilitate distance …

Few-shot learning (FSL) models are trained on base classes that have many training
examples and evaluated on novel classes that have very few training examples. Since these …

Many real-life problem settings have classes of data with very few examples for training.
Deep learning networks do not perform well for such few-shot classes. In order to perform …

In recent years, self-supervised learning has emerged as a powerful approach to learning
visual representations without requiring extensive manual annotation. One popular …

Traditional deep learning models often struggle in few-shot learning scenarios, where
limited labeled data is available. While the Contrastive Language-Image Pre-training (CLIP) …

Adversarial images generated by generative adversarial networks are not close to any
existing benign images, and contain nonrobust features that have been identified as critical …

Few-shot learning, the capability of a machine learning model to comprehend and adapt to
new classes with limited instances, has been a critical area of research in the realm of …