CSN: Component-Supervised Network for Few-Shot Classification

The few-shot classification (FSC) task has been a hot research topic in recent years. It aims to address the classification problem with insufficient labeled data on a cross-category basis. Typically, researchers pre-train a feature extractor with base data, then use it to extract the features of novel data and recognize them. Notably, the novel set only has a few annotated samples and has entirely different categories from the base set, which leads to that the pre-trained feature extractor can not adapt to the novel data flawlessly. We dub this problem as Feature-Extractor-Maladaptive (FEM) problem. Starting from the root cause of this problem, this paper presents a new scheme, Component-Supervised Network (CSN), to improve the performance of FSC. We believe that although the categories of base and novel sets are different, the composition of the sample's components is similar. For example, both cat and dog contain leg and head components. Actually, such entity components are intra-class stable. They have fine cross-category versatility and new category generalization. Therefore, we refer to WordNet, a dictionary commonly used in natural language processing, to collect component information of samples and construct a component-based auxiliary task to improve the adaptability of the feature extractor. We conduct experiments on two benchmark datasets (mini-ImageNet and tiered-ImageNet), the improvements of $0.9\%$-$5.8\%$ compared with state-of-the-arts have evaluated the efficiency of our CSN.