Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …

Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms
that can continually learn new concepts from a few data points, without forgetting knowledge …

Continually learning new classes from fresh data without forgetting previous knowledge of
old classes is a very challenging research problem. Moreover, it is imperative that such …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline
patient diagnosis and management has become more pressing than ever. As one of the …

The ability to incrementally learn new classes is crucial to the development of real-world
artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Real world data often have a long-tailed and open-ended distribution. A practical
recognition system must classify among majority and minority classes, generalize from a few …

New classes arise frequently in our ever-changing world, eg, emerging topics in social
media and new types of products in e-commerce. A model should recognize new classes …