Few-shot Learning (FSL) aims to learn and distinguish new categories from a scant number
of available samples, presenting a significant challenge in the realm of deep learning …

In recent years, graph neural network (GNN)-based approaches have achieved great
success in the field of few-shot learning (FSL). These FSL methods usually employ a meta …

Multi-modal attention mechanisms have been successfully used in multi-modal graph
learning for various tasks. However, existing attention-based multi-modal graph learning …

Few-shot object detection is an important but challenging task where only a few instances of
novel categories are available. The widely used approach is to pretrain a detector on base …

Few-shot Learning aims to learn and distinguish new categories with a very limited number
of available images, presenting a significant challenge in the realm of deep learning. Recent …

Few-shot learning aims to rapidly recognize unseen targets using only a limited number of
labeled samples, which is one of the core capabilities for humans. However, existing …

Few-shot Learning (FSL) aims to learn and distinguish new categories from a scant number
of available samples, presenting a significant challenge in the realm of deep learning …