The goal of Few-shot classification (FSL) is to identify unseen classes with very limited
samples has attracted more and more attention. Usually, it is formulated as a metric learning …

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) …

Despite significant progress in recent deep neural networks, most deep learning algorithms
rely heavily on abundant training samples. To address the issue, few-shot learning (FSL) …

This work focuses on tackling the challenging but realistic visual task of Incremental Few-
Shot Learning (IFSL), which requires a model to continually learn novel classes from only a …

Medical imaging, a cornerstone of disease diagnosis and treatment planning, faces the
hurdles of subjective interpretation and reliance on specialized expertise. Deep learning …

Abstract In recent years, Deep Neural Networks (DNNs) have shown their power over many
research fields, and related applications are entering people's daily lives with unstoppable …