Few-shot learning aims to recognize novel queries with limited support samples by learning
from base knowledge. Recent progress in this setting assumes that the base knowledge and …

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 …

Abstract Cross-domain Few-shot Learning (CD-FSL) aims to address the challenges of FSL
where significant domain gaps exist between source and target image datasets. Unlike …

Adaptive gradient methods, eg, ADAM, have achieved tremendous success in data-driven
machine learning, especially deep learning. Employing adaptive learning rates according to …

Few-shot action recognition (FSAR) aims to learn a model capable of identifying novel
actions in videos using only a few examples. In assuming the base dataset seen during …

Cross-Domain Few-Shot Learning (CD-FSL) aims to empower machines with the capability
to rapidly acquire new concepts across domains using an extremely limited number of …

Large-scale models, abundant data, and dense computation are the pivotal pillars of deep
neural networks. The present-day deep learning models have made significant strides in …

Few-Shot transfer learning uses limited labeled data to recognise the new classes. Current
researches assume that train and test data have the same data distribution. But this setting is …