Abstract: Few-shot learning can learn the characteristics of various samples from fewer samples,but due to the problem of low data,that is,the number of samples is small,how to more accurately extract the important feature information in the image,and how to better learn from the image.The characteristics of the target object and the more accurate judgment of the similarity between the unlabeled samples and the support set category become the key.A few-shot learning method MGFAN based on multi-graph feature aggregation is proposed.Specifically,the model expands the original image through various data enhancement me-thods,and then uses a self-attention module to obtain important feature information between the original image and different expanded images,so as to obtain more accurate features vector about the image.Secondly,the self-supervised learning task of predicting different augmentation methods of images is introduced into the model as an auxiliary task to promote the feature learning ability of the model.Finally,multiple distance functions are used to calculate the similarity between samples more accurately.Experiments in 3 standard datasets miniImageNet,tieredImageNet and Stanford Dogs using 5-way 1-shot and 5-way 5-shot experimental settings show that the MGFAN method can significantly improve the classification performance of the classifier.

Key words: Few-shot learning, Deep learning, Self-supervised learning, Feature aggregation, Data augmentation, Self-attention