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Virtual label expansion-Highlighted key features for few-shot learning

The goal of the few-shot image classification is to identify the category based on a very small number of labeled samples. Two of the key problems are the insufficient amount of labeled data and the unknown category (the inconsistency between the training category and the test category). For these two problems, we propose a new few-shot classification model VE-HKF. First, we introduce a Virtual label expansion mechanism (VE). This mechanism expands the support set data by using the unlabeled data in the query set, thus increasing the number of samples in the support set and making the extracted features more robust. Second, we introduced a Highlighted key features mechanism (HKF). The mechanism first generates a mask through operations such as class average vector and dimensionality reduction and uses this mask to shield and support some irrelevant features, to highlight the important features between samples of each category in a disguised manner, and then focus on the support set and the features of the query set samples to highlight the common features between the support set and the query set, making the extracted features more conducive to classification. On the three data sets of Mini-ImageNet, Omniglot, and Tiered-ImageNet, our model has achieved good results.