AI RESEARCH
PAND: Prompt-Aware Neighborhood Distillation for Lightweight Fine-Grained Visual Classification
arXiv CS.LG
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ArXi:2602.07768v2 Announce Type: replace-cross Distilling knowledge from large Vision-Language Models (VLMs) into lightweight networks is crucial yet challenging in Fine-Grained Visual Classification (FGVC), due to the reliance on fixed prompts and global alignment. To address this, we propose PAND (Prompt-Aware Neighborhood Distillation), a two-stage framework that decouples semantic calibration from structural transfer. First, we incorporate Prompt-Aware Semantic Calibration to generate adaptive semantic anchors. Second, we.