Cross-Layer and Cross-Sample Feature Optimization Network for Few-Shot Fine-Grained Image Classification

Abstract

Recently, a number of Few-Shot Fine-Grained Image Classification (FS-FGIC) methods have been proposed, but they primarily focus on better fine-grained feature extraction while overlooking two important issues. The first one is how to extract discriminative features for Fine-Grained Image Classification tasks while reducing trivial and non-generalizable sample level noise introduced in this procedure, to overcome the over-fitting problem under the setting of Few-Shot Learning. The second one is how to achieve satisfying feature matching between limited support and query samples with variable spatial positions and angles. To address these issues, we propose a novel Cross-layer and Cross-sample feature optimization Network for FS-FGIC, C2-Net for short. The proposed method consists of two main modules: Cross-Layer Feature Refinement (CLFR) module and Cross-Sample Feature Adjustment (CSFA) module. The CLFR module further refines the extracted features while integrating outputs from multiple layers to suppress sample-level feature noise interference. Additionally, the CSFA module addresses the feature mismatch between query and support samples through both channel activation and position matching operations. Extensive experiments have been conducted on five fine-grained benchmark datasets, and the results show that the C2-Net outperforms other state-of-the-art methods by a significant margin in most cases. Our code is available at: https://github.com/zenith0923/C2-Net.