LAMP: Layerwise Adaptive Mixing of Predictors for Training-Free Few-Shot Vision-Language Adaptation
Few-shot adaptation often relies on an encoder's final global representation, leaving complementary information in intermediate layers underused. Combining these features is challenging because their dimensions can differ and their usefulness varies across tasks. We propose LAMP, Layerwise Adaptive Mixing of Predictors, a training-free framework that combines global and regional information from multiple encoder layers. A single forward pass per view supplies all descriptors. Global and support-calibrated regional kernel predictors independently fit the same label residuals. Labeled validation data select their mixture in class-score space, preserving native feature dimensions without lea...
| Authors | Zhipeng Ye, Feng Jiang, Qiufeng Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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