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 …

Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

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 …

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Heterogeneity across clients in federated learning (FL) usually hinders the optimization
convergence and generalization performance when the aggregation of clients' knowledge …

Point cloud videos exhibit irregularities and lack of order along the spatial dimension where
points emerge inconsistently across different frames. To capture the dynamics in point cloud …

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 dataset generalization is a challenging variant of the well-studied few-shot
classification problem where a diverse training set of several datasets is given, for the …

Point cloud sequences are irregular and unordered in the spatial dimension while exhibiting
regularities and order in the temporal dimension. Therefore, existing grid based …