Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

Few-shot learning (FSL) is an important and topical problem in computer vision that has
motivated extensive research into numerous methods spanning from sophisticated meta …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

A common approach to transfer learning under distribution shift is to fine-tune the last few
layers of a pre-trained model, preserving learned features while also adapting to the new …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

In this paper, we look at the problem of cross-domain few-shot classification that aims to
learn a classifier from previously unseen classes and domains with few labeled samples …

Transfer-learning methods aim to improve performance in a data-scarce target domain using
a model pretrained on a data-rich source domain. A cost-efficient strategy, linear probing …

Few-shot classification consists of a training phase where a model is learned on a relatively
large dataset and an adaptation phase where the learned model is adapted to previously …

Abstract Few-Shot Learning (FSL) requires vision models to quickly adapt to brand-new
classification tasks with a shift in task distribution. Understanding the difficulties posed by …

The goal of few-shot learning is to learn the generalizability from seen to unseen data with
only a few samples. Most previous few-shot learning focus on learning generalizability …