Beyond Modality Level Competition: Frequency-Resolved Spectral Regulation for Multimodal Learning

Modality competition is a persistent challenge in multimodal learning, where dominant modalities can suppress the effective learning of weaker ones. Existing methods typically assess and mitigate competition at the modality-level, overlooking heterogeneous competitive states within latent representations. By examining latent representations from a frequency-domain perspective, we find that modality preference varies substantially across spectral regions and can even reverse across frequency bands during optimization, exposing competitive heterogeneity that modality-level assessment can obscure and uniform regulation cannot effectively address. Motivated by this observation, we propose SpecGuide, a frequency-resolved method that mitigates modality competition at the spectral level. Specifically, SpecGuide derives band-wise preference from class-conditioned spectral discriminative margins, uses the resulting preference maps to guide adaptive spectral shaping, and encourages frequency-wise complementarity across modality-specific spectral responses. Extensive experiments on six benchmarks demonstrate that SpecGuide consistently achieves state-of-the-art performance, delivers gains across different backbones, and remains robust to missing and noise-corrupted modalities.