Interpreting and explaining the behavior of deep neural networks is critical for many tasks.
Explainable AI provides a way to address this challenge, mostly by providing per-pixel …

In recent years, few-shot learning has received increasing attention in the Internet of Things
areas. Few-shot learning aims to distinguish unseen classes with a few labeled samples …

Few-shot learning (FSL) is a challenging task that aims to train a classifier to recognize
novel categories, where only a few annotated examples are available in each category …

Few-shot visual recognition refers to recognize novel visual concepts from a few labeled
instances. Many few-shot visual recognition methods adopt the metric-based meta-learning …

In recent years, deep learning models, which possess powerful feature extraction abilities,
have achieved remarkable success in the classification of hyperspectral images (HSIs) …

Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with
few examples. Recently, lots of methods have been proposed from the perspective of meta …

Few-shot learning (FSL) approaches, mostly neural network-based, assume that pre-trained
knowledge can be obtained from base (seen) classes and transferred to novel (unseen) …

As a subset of machine learning, meta-learning, or learning to learn, aims at improving the
model's capabilities by employing prior knowledge and experience. A meta-learning …

Learning to recognize novel concepts from just a few image samples is very challenging as
the learned model is easily overfitted on the few data and results in poor generalizability …

Ship target recognition is essential for maritime transportation, commercial trade, maritime
security, and monitoring illegal activity. The majority of previous ship target recognition …