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 …

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

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 …

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

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

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 …

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 …

Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide
applications in social networks, e-commerce, and cybersecurity. Recent deep learning …