Spatial frequency information fusion network for few-shot learning
To address overfitting and poor generalization caused by data scarcity in few-shot image classification, this paper proposes SFIFNet—a novel network that explicitly fuses frequency-domain and spatial-domain information during data preprocessing for the first time. Methodologically, SFIFNet leverages frequency transforms (e.g., DCT or FFT) to extract global texture and structural priors, integrates them with multi-scale spatial features via a lightweight deep neural architecture, and further enhances robustness through conventional data augmentation. Its key contribution lies in breaking the prevailing reliance on spatial-domain representations alone, systematically exploiting discriminative and complementary features encoded in the frequency domain. Extensive experiments on standard few-shot benchmarks—including Mini-ImageNet and CUB—demonstrate that SFIFNet achieves significant improvements in classification accuracy (average gain of +2.3%) and superior cross-domain generalization capability.