Self-similarity feature based few-shot learning via hierarchical relation network

Few-shot learning aims to recognize new visual concepts with a small number of labeled samples. The hierarchical structure based on inter-class labels performs well in many few-shot learning models. However, intra-class features are similar and difficult to distinguish, which is important for mining the correlation and independence between intra-class features in the scene of sparse data. In this paper, we propose a few-shot learning model with a self-similarity feature representation by a hierarchical relation network, which considers inter-class labels and intra-class features to guide few-shot learning. First, we introduce a self-similarity feature representation module as the intermediate feature transform in the neural network. Unlike the traditional model, it extracts specific feature information from intra-class features. Second, we leverage the inter-class label hierarchical structure as important auxiliary information to establish a hierarchical relation network metric module. The module uses coarse-grained information to guide fine-grained classification, which effectively alleviates the problem of insufficient data. Experimental results show that our model improves the classification accuracy, reaching 58.68% on the tieredImageNet dataset.