Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Few-shot image classification is a challenging problem that aims to achieve the human level
of recognition based only on a small number of training images. One main solution to few …

The goal of few-shot learning (FSL) is to recognize a set of novel classes with only few
labeled samples by exploiting a large set of abundant base class samples. Adopting a meta …

Recent work has shown that label-efficient few-shot learning through self-supervision can
achieve promising medical image segmentation results. However, few-shot segmentation …

Most recent few-shot learning (FSL) approaches are based on episodic training whereby
each episode samples few training instances (shots) per class to imitate the test condition …

Distance-based classification is frequently used in transductive few-shot learning (FSL).
However, due to the high-dimensionality of image representations, FSL classifiers are prone …

Few-shot learning aims to recognize novel classes from only a few labeled training
examples. Aligning semantically relevant local regions has shown promise in effectively …

State-of-the-art deep learning systems (eg, ImageNet image classification) typically require
very large training sets to achieve high accuracies. Therefore, one of the grand challenges is …

Android malware is a major cyber threat to the popular Android platform which may
influence millions of end users. To battle against Android malware, a large number of …

Abstract Few-Shot Learning (FSL) is a challenging task, ie, how to recognize novel classes
with few examples? Pre-training based methods effectively tackle the problem by pre …