FAD: Frequency Adaptation and Diversion for Cross-domain Few-shot Learning

TL;DR

This paper introduces FAD, a frequency-aware framework for cross-domain few-shot learning that models and adapts spectral components separately, significantly improving generalization across diverse domains.

Contribution

FAD is the first method to explicitly incorporate frequency domain adaptation with spectral band-wise modules for CD-FSL.

Findings

FAD outperforms state-of-the-art methods on Meta-Dataset benchmark.

Frequency-aware adaptation improves robustness to domain shifts.

Spectral band-wise modules enhance feature disentanglement and transferability.

Abstract

Cross-domain few-shot learning (CD-FSL) requires models to generalize from limited labeled samples under significant distribution shifts. While recent methods enhance adaptability through lightweight task-specific modules, they operate solely in the spatial domain and overlook frequency-specific variations that are often critical for robust transfer. We observe that spatially similar images across domains can differ substantially in their spectral representations, with low and high frequencies capturing complementary semantic information at coarse and fine levels. This indicates that uniform spatial adaptation may overlook these spectral distinctions, thus constraining generalization. To address this, we introduce Frequency Adaptation and Diversion (FAD), a frequency-aware framework that explicitly models and modulates spectral components. At its core is the Frequency Diversion Adapter, which transforms intermediate features into the frequency domain using the discrete Fourier transform (DFT), partitions them into low, mid, and high-frequency bands via radial masks, and reconstructs each band using inverse DFT (IDFT). Each frequency band is then adapted using a dedicated convolutional branch with a kernel size tailored to its spectral scale, enabling targeted and disentangled adaptation across frequencies. Extensive experiments on the Meta-Dataset benchmark demonstrate that FAD consistently outperforms state-of-the-art methods on both seen and unseen domains, validating the utility of frequency-domain representations and band-wise adaptation for improving generalization in CD-FSL.