STAR: A Spectral Resonance Framework for Training-Free Multi-Domain Learning with CLIP

Multi-domain learning (MDL) aims to learn a model that performs reliably across multiple domains. As a powerful vision-language foundation model, CLIP is a promising basis for MDL due to its large-scale pretraining. To better exploit CLIP's pretrained priors, MDL calls for a different design instead of existing heavy architectures and complex training strategies. In this paper, we study multi-domain CLIP features from both spectral and Gaussian geometric perspectives. Our analyses first reveal that dominant class geometry remains domain-dependent, while nuisance-sensitive variations concentrate in spectral-tail directions, providing a reliable basis for self-domain recognition. More importantly, we find that self-domain failures can be complemented by evidence from other domains, and their recoverability is strongly associated with self-domain Gaussian geometry. Based on these findings, we propose SpecTral Adaptive Resonance (STAR), a training-free framework for CLIP-based MDL. STAR first establishes a reliable self-domain base through Subspace Anchor Guidance (SAG), which preserves dominant class-domain spectral structure while suppressing nuisance-sensitive directions. On top of this base, CrOss-domain REctification (CORE) exploits complementary cross-domain spectral evidence under self-domain Gaussian compatibility to enhance self-domain predictions. Extensive experiments show that STAR consistently outperforms previous MDL baselines, and further analyses validate the effectiveness of our design choices. By building reliable self-domain anchors and selectively resonating with complementary cross-domain structures, STAR offers a new geometric perspective on CLIP-based MDL.