Few-shot classification aims to adapt classifiers trained on base classes to novel classes
with a few shots. However, the limited amount of training data is often inadequate to …

Few-shot classification (FSC) is a challenging task due to limitation in accessing training
data. Recent methods often employ highly complex networks to obtain high-quality features …

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 …

Few-shot learning is a challenging task that aims to train a classifier with very limited training
samples. Most existing few-shot learning methods mainly focus on mining knowledge from …

Most existing few-shot image classification methods employ global pooling to aggregate
class-relevant local features in a data-drive manner. Due to the difficulty and inaccuracy in …

Building learning systems possessing adaptive flexibility to different tasks is critical and
challenging. In this article, we propose a novel and general meta-learning framework, called …

Automatic pill recognition (APR) systems are crucial for enhancing hospital efficiency,
assisting visually impaired individuals, and preventing cross-infection. However, most …

Learning in hyperbolic spaces has gained increasing attention due to the superior capability
of modeling hierarchical structures. Existing hyperbolic learning methods use a fixed …