Deep learning has been highly successful in computer vision with large amounts of labeled
data, but struggles with limited labeled training data. To address this, Few-shot learning …

Abstract Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that tackles
few-shot learning across different domains. It aims at transferring prior knowledge learned …

The vanilla Few-shot Learning (FSL) learns to build a classifier for a new concept from one
or very few target examples, with the general assumption that source and target classes are …

Given sufficient training data on the source domain, cross-domain few-shot learning (CD-
FSL) aims at recognizing new classes with a small number of labeled examples on the …

The challenge of cross-domain few-shot learning (CD-FSL) stems from the substantial
distribution disparities between target and source domain images, necessitating a model …

Cross-domain few-shot Learning (CDFSL) is proposed to first pre-train deep models on a
source domain dataset where sufficient data is available, and then generalize models to …

Metric learning methods are widely used in few-shot learning due to their simplicity and
effectiveness. Most existing methods directly predict query labels by comparing the similarity …

Existing cross-domain few-shot learning (CDFSL) methods, which develop source-domain
training strategies to enhance model transferability, face challenges with large-scale pre …

Abstract Cross-Domain Few-Shot Learning (CD-FSL) addresses the Few-Shot Learning with
a domain gap between source and target domains, which facilitates the transfer of …

Few-Shot transfer learning has become a major focus of research as it allows recognition of
new classes with limited labeled data. While it is assumed that train and test data have the …