Learning Feature Exploration and Selection With Handcrafted Features for Few-Shot Learning

Abstract

Interest in few-shot learning (FSL) has grown recently, but the value of feature learning, which bridges the gap between base and novel classes, remains largely understudied. The limited availability of labeled samples for each class poses a major challenge. To tackle this, we propose a simple yet effective approach called deep discriminative handcrafted feature regression (DDHFR) to explore intrinsic information and select improved discriminative features in few-shot data by mining knowledge from classical handcrafted features. To explore intrinsic information, we design several deep handcrafted feature regression (DHFR) modules and plugged them separately into different layers of the backbone to use feature engineering knowledge for feature learning optimization at different granularities. To achieve discriminative feature selection, we incorporate an auxiliary classifier (AC) into each DHFR module to enhance the acquisition of discriminative information. Furthermore, we employed self-distillation to boost ability of ACs ot be classified. Experimental results in three backbones on three datasets show that DDHFR can generally improve the performance of existing FSL methods. On average, it improves the recognition accuracy by 1.16% in two common few-shot settings.