Task-Oriented Local Feature Rectification Network for Few-Shot Image Classification

Few-shot image classification aims to classify unlabeled samples when only a small number of labeled samples are available for each class. Recently, local feature-based few-shot learning methods have made significant progress. However, existing methods often treat all local descriptors equally, without considering the importance of each local descriptor in different tasks. Therefore, the few-shot learning model is easily disturbed by class-irrelevant features, which results in a decrease in accuracy. To address this issue, we propose a task-oriented local feature rectification network (TLFRNet) with two feature rectification modules (support rectification module and query rectification module). The former module uses the relationship between each local descriptor and prototypes within the support set to rectify the support features. The latter module uses a CNN to rectify the similarity tensors between the query and support local features and then models the importance of the query local features. Through these two modules, our model can effectively reduce the intra-class variation of class-relevant features, thus obtaining more accurate image-to-class similarity for classification. Extensive experiments on five datasets show that TLFRNet achieves more superior classification performance than the related methods.