FCGNN: Fuzzy Cognitive Graph Neural Networks With Concept Evolution for Few-Shot Learning

Graph neural networks (GNNs) have recently emerged as a promising approach for solving few-shot learning (FSL) problems, enabling generalization to new categories by establishing associations among limited samples. However, most previous methods focus on local interactions within a layer or between successive layers, overlooking the continuous evolution of node states across deeper layers. Especially in FSL scenarios with scarce data, this oversight leads to excessive compression or loss of node features in deep networks. To address this challenge, we propose a Fuzzy Cognitive Graph Neural Network (FCGNN), which models the evolution of node fuzzy state representation across layers by integrating principles from fuzzy cognitive maps. First, we construct a fuzzy cognitive graph (FCG) based on Gaussian similarity to enhance the information flow within the layer. FCG establishes fuzzy relationships between nodes to enhance the connections between different samples within the layer. Then, we design a fuzzy concept evolution (FCE) module based on dual fuzzy state representation, incorporating the fuzzy activation state to illustrate the continuous changes in node states. FCE combines explicit and implicit fuzzy state representation to achieve the dynamic evolution of node concepts between layers. Finally, we map the node states after multiple graph iterations to the category space for FSL image classification. In experiments on four benchmark datasets, FCGNN achieves superior classification performance and outperforms several baseline methods by up to 2.73% on average accuracy.