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IGNN: An improved graph neufral network with integrated attention and pre-message-passing for few-shot image classification

Fig 2

IGNN model workflow diagram.

and represent feature embedding functions. Gray circles represent query set sample nodes, while nodes in other colors represent support set sample nodes. The model first extracts image features from the support set and the query set using the feature embedding functions, and these features are then constructed as nodes in a graph. Next, through the pre-message-passing mechanism, the features of the support set and query set images are associated, and the feature representations of each node are updated. Finally, with the help of the message-passing mechanism, the labels of the query set are predicted, and the predicted labels are compared with the true labels to compute the loss value. The images in this figure are similar to but not identical to the original ones and are therefore for illustrative purposes only.

Fig 2

doi: https://doi.org/10.1371/journal.pone.0348057.g002