Feature Mixture on Pre-Trained Model for Few-Shot Learning

Few-shot learning (FSL) aims at recognizing a novel object under limited training samples. A robust feature extractor (backbone) can significantly improve the recognition performance of the FSL model. However, training an effective backbone is a challenging issue since 1) designing and validating structures of backbones are time-consuming and expensive processes, and 2) a backbone trained on the known (base) categories is more inclined to focus on the textures of the objects it learns, which is hard to describe the novel samples. To solve these problems, we propose a feature mixture operation on the pre-trained (fixed) features: 1) We replace a part of the values of the feature map from a novel category with the content of other feature maps to increase the generalizability and diversity of training samples, which avoids retraining a complex backbone with high computational costs. 2) We use the similarities between the features to constrain the mixture operation, which helps the classifier focus on the representations of the novel object where these representations are hidden in the features from the pre-trained backbone with biased training. Experimental studies on five benchmark datasets in both inductive and transductive settings demonstrate the effectiveness of our feature mixture (FM). Specifically, compared with the baseline on the Mini-ImageNet dataset, it achieves 3.8% and 4.2% accuracy improvements for 1 and 5 training samples, respectively. Additionally, the proposed mixture operation can be used to improve other existing FSL methods based on backbone training.