MSSFFE-Net: a fine-grained object recognition network based on multi-scale spatial-frequency domain feature enhancement

Abstract

The challenge of few-shot fine-grained image classification lies in the scarcity of labeled training samples, which hinders models from learning subtle discriminative patterns. Moreover, most existing methods rely heavily on spatial-domain convolutional operations while overlooking structured spectral information in the frequency domain, leading to suboptimal representations. To address this limitation, we propose a Multi-Scale Spatial-Frequency Feature Enhancement Network. In the spatial domain, a dual-branch architecture is designed to model both local fine-grained details and long-range contextual dependencies. In the frequency domain, discriminative information is enhanced through multi-scale frequency decomposition and an amplitude-based attention mechanism. The spatial and frequency features are then adaptively fused into a unified representation that integrates complementary information from both domains. Extensive experiments on three public benchmarks, namely CUB-200-2011, Stanford-Dogs, and Stanford-Cars, demonstrate the effectiveness of the proposed framework. The proposed method consistently outperforms state-of-the-art approaches under both 1-shot and 5-shot settings, showing strong discriminative ability and robust generalization performance. The code is available at https://github.com/WW-YM/MSSFFF-Net/tree/master .