SD-LoRA: Training-Time Structural Distillation into LoRA for Few-Shot Vision-Language Adaptation

arXiv:2611.07063v1 Announce Type: cross Abstract: Few-shot adaptation of vision-language models must balance two competing goals: learning fine-grained visual distinctions from very limited supervision and preserving the efficiency that makes frozen CLIP-like models attractive for deployment. Existing lightweight adaptation methods, including prompt tuning, LoRA, and cache-based adapters, typically supervise global image embeddings and therefore underuse token-level structure. Conversely, structure-aware methods exploit local evidence but often introduce additional inference-time computation.

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