A Novel Adaptive Graph Neural Network Based on Meta‐Learning for Cross‐Domain Few‐Shot Learning

ABSTRACT In recent years, Graph Neural Network (GNN)‐based Few‐Shot Learning (FSL) methods have achieved significant performance. Such methods typically assume that base and novel classes share the same underlying distribution, which limits their practical applicability. Cross‐domain FSL has emerged as a promising direction to tackle real‐world distribution shifts. However, existing methods still suffer from significant domain shift, leading to severe degradation in generalization performance. To address this challenge, we introduce an enhanced Adversarial Feature Augmentation (AFA) module that, unlike prior methods which align only global feature distributions, explicitly accounts for class decision boundaries. Additionally, we propose a novel Meta‐Learning Adaptive Graph Neural Network (MLA‐GNN) framework to alleviate the limitations of scenario training in cross‐domain problems. This model consists of two key components: a meta network that adaptively generates task‐level parameters and a target network that receives the generated parameters, namely, the Task‐specific Graph Neural Network. The meta network encodes task contextual information and generates specific parameters for the target network, enabling the GNN to flexibly adjust classification boundaries across different tasks, thereby maintaining decision effectiveness on data from different domains. We train our model on the MiniImageNet dataset and test it on five few‐shot datasets. Extensive experiments demonstrate the superiority of the proposed method in cross‐domain few‐shot classification tasks.