# Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification
## 1 Introduction
In contrast to some other sophisticated GNN techniques, EGNNs are made to deliver good performance while requiring less computing complexity. The introduction of edge update techniques, which allows for more precise capturing of node interactions during information propagation, is a significant breakthrough. Because of this, EGNNs can sometimes find a compromise between representational power and computing efficiency. The research on using hierarchical residual structures with EGNNs for few-shot picture classification is covered in this section of the paper. The performance of the model can be improved by incorporating residual structures within the hierarchical structure between nodes when dealing with few-shot picture classification problems, particularly when employing Edge-Update Graph Neural Networks (EGNNs). This is seen as introducing residual structures into the EGNN node update hierarchy. The new node features are specifically coupled residually with the old node features during the updating procedure of each node. This helps the model better capture correlations and patterns between images in few-shot circumstances by ensuring that the previous node properties are not completely lost during information transmission. Multiple node update topologies might introduce residual connections. For instance, the node features from the first layer may still be related to the updated node results while updating nodes in the second layer. These residual cross-hierarchy linkages boost information transfer between levels, which strengthens the model's capacity for learning. Different residual connection techniques can be tested in experiments to see which one performs best for few-shot picture classification problems. Additionally, multiple optimizers, loss functions, and data augmentation for graph data may be combined to create a comprehensive picture classification model that performs better. In conclusion, incorporating residual structures within the EGNN's node update hierarchy is a viable method for jobs requiring single-shot picture categorization. utilizing the advantages of both strategies. We can sum up our main contributions as follows:  
* The experimental findings are analyzed and the incorporation of residual structures into EGNNs for few-shot picture categorization is emphasized. It has been shown through practical tests that adding residual structures into EGNNs has produced noticeably better outcomes in few-shot image classification tasks. This demonstrates how well the suggested strategy works to improve model performance. This improvement has been experimentally proven in practice as well as theoretically. * On the mini-ImageNet dataset, comparative tests were performed utilizing two distinct residual architectures. These experiments revealed that residual connections with convolutional structures should yield greater improvements on smaller support sets, whereas linear residual structures contribute more significantly on larger support sets. * A custom dataset, CBAC (Car Brand Appearance Classification), was created. These experiments showed that residual connections with convolutional structures should yield greater improvements on smaller support sets, whereas linear residual structures contribute more significantly on larger support sets. On this dataset, comparative studies showed observable performance improvements that were confirmed by experiments.