OGKD injects inter-class geometry into teacher targets for two distillation losses (GAD on global tokens, LGD on patches) and reports 1.7-2.8% average accuracy gains over prior VLM adaptation methods on 11 medical datasets.
Learning generalizable prompt for clip with class similarity knowledge
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Geometry-Aware Distillation for Prompt Tuning Biomedical Vision-Language Models
OGKD injects inter-class geometry into teacher targets for two distillation losses (GAD on global tokens, LGD on patches) and reports 1.7-2.8% average accuracy gains over prior VLM adaptation methods on 11 medical datasets.