To solve the problem that self-supervised information contained in images is easily ignored during me-ta-training,which leads to inadequate feature extraction and poor model generalization ability,a small sample im-age classification model combining self-supervised comparison and meta-migration is proposed.Firstly,in the pre-training stage,a self-supervised global and local joint comparison loss is proposed to train the feature encoder,so that it can learn richer transferable prior knowledge.Secondly,in the meta-training stage,to solve the problem that samples are prone to deviation in the feature space,a prototype center metric network is proposed to optimize the feature space and make the feature distribution of similar samples more compact.Finally,the cosine classifier based on the cosine similarity metric is used to calculate the similarity between the center of each category and the query set image.The classification effect is compared with the current mainstream model on the Mini-ImageNet and Tiered-ImageNet datasets.The results show that:Compared with the baseline,the accuracy of the proposed model is improved by 3.14%and 4.09%in the 5-way 1-shot and 5-way 5-shot tasks of the Mini-ImageNet dataset,respectively,over the two tasks of the Tiered-ImageNet dataset,is improved by 2.98%and 3.73%,respectively.
维普
万方数据