SpecPL: Disentangling Spectral Granularity for Prompt Learning
Existing prompt learning for VLMs exhibits a modality asymmetry, predominantly optimizing text tokens while still relying on frozen visual encoder as holistic extractor and neglecting the spectral granularity essential for fine-grained discrimination. To bridge this, we introduce Disentangling Spectral Granularity for Prompt Learning (SpecPL), which approaches prompt learning from a novel spectral perspective via Counterfactual Granule Supervision. Specifically, we leverage a frozen VAE to decompose visual signals into semantic low-frequency bands and granular high-frequency details. A frozen Visual Semantic Bank anchors text representations to universal low-frequency invariants, mitigati...
| Authors | Jingtao Zhou, Xirui Kang, Feiyang Huang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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