PAND: Prompt-Aware Neighborhood Distillation for Lightweight Fine-Grained Visual Classification
arXiv:2602.07768v3 Announce Type: replace-cross Abstract: 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 introduce a neighborhood-aware structural distillation st
The proliferation of very large AI models necessitates the development of efficient techniques to distill their knowledge into lighter, more deployable networks, balancing performance with computational cost.
This research addresses a critical challenge in deploying powerful AI models in real-world applications by enabling resource-efficient fine-grained visual classification, which is crucial for edge devices and specialized tasks.
The ability to more effectively transfer knowledge from large vision-language models to smaller networks means broader applicability and lower operational costs for high-precision visual recognition tasks.
- · AI developers
- · Edge AI providers
- · Specialized CV applications
- · Companies reliant on only large, inefficient models
Improved performance and efficiency in fine-grained visual classification tasks.
Accelerated deployment of advanced visual AI in resource-constrained environments like robotics and mobile devices.
Enhanced automation and QA processes across various industries due to more accurate and accessible visual inspection systems.
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Read at arXiv cs.LG