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arXiv Computer Vision By Zhipeng Ye, Feng Jiang, Qiufeng Wang, Hao Li

LAMP: Layerwise Adaptive Mixing of Predictors for Training-Free Few-Shot Vision-Language Adaptation

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LAMP (Layerwise Adaptive Mixing of Predictors) is a training‑free framework that fuses global and regional information from multiple encoder layers for few‑shot vision‑language adaptation. It uses a single forward pass per view to generate descriptors, then applies global and support‑calibrated regional kernel predictors that independently fit label residuals. Validation data select the optimal mixture in class‑score space, preserving native feature dimensions without learned projections, and an extended variant LAMP‑F further adapts text‑anchored class prototypes for improved accuracy across eleven datasets with ViT and ResNet backbones.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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