Abstract: To address the issues of poor generalization performance in few-shot learning models based on prototype networks and inaccurate class prototypes obtained from a small number of samples,a novel few-shot learning method is proposed in this paper.Firstly,a symmetric convolutional block network(SCB-Net) consisting of bidirectional convolutional block attention modules and residual blocks is used to adaptively learn the features at different depths of the image,so as to extract a more representative representation of the category features and effectively improve the generalization ability of the model.Secondly,an inverse Euclidean label propagation prototype calibration algorithm(IELP-PC) is introduced.It employs pseudo-labeling to augment the support set samples and subsequently calibrates the class prototypes using inverse Euclidean distance weighting for the support set samples,thereby improving the model’s classification accuracy.Experiment results on two commonly used datasets mini-ImageNet and tiered-ImageNet demonstrate the effectiveness of the proposed method.Compared with the baseline model,the proposed method improves the 5-way 1-shot accuracy by 6.44% and 7.83%,and the 5-way 5-shot accuracy by 2.68% and 2.02%,respectively.

Key words: Prototype network, Few-shot learning, Symmetric convolutional block network, Prototype calibration, Inverse Euclidean distance