Few-shot image recognition based on multi-scale features prototypical network

In order to improve the model’s capability in expressing features during few-shot learning, a multi-scale features prototypical network (MS-PN) algorithm is proposed. The metric learning algorithm is employed to extract image features and project them into a feature space, thus evaluating the similarity between samples based on their relative distances within the metric space. To sufficiently extract feature information from limited sample data and mitigate the impact of constrained data volume, a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature extraction. Additionally, the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the experiment. The loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric space. Experimental evaluations are conducted on small-sample datasets mini-ImageNet and CUB200-2011. The method in this paper can achieve higher classification accuracy. Specifically, in the 5-way 1-shot experiment, classification accuracy reaches 50. 13% and 66. 79% respectively on these two datasets. Moreover, in the 5-way 5-shot experiment, accuracy of 66. 79% and 85. 91% are observed, respectively. © 2024 Inst. of Scientific and Technical Information of China. All rights reserved.