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NanoVDR: Distilling a 2B Vision-Language Retriever into a 70M Text-Only Encoder for Visual Document Retrieval

📝 Summary:
NanoVDR improves visual document retrieval by distilling a large VLM teacher into a small 70M text-only query encoder. This decouples document indexing from query processing, achieving 50x lower latency and 32x fewer parameters with nearly identical quality.

🔹 Publication Date: Published on Mar 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12824
• PDF: https://arxiv.org/pdf/2603.12824
• Project Page: https://huggingface.co/nanovdr

🔹 Models citing this paper:
https://huggingface.co/nanovdr/NanoVDR-L
https://huggingface.co/nanovdr/NanoVDR-S-Multi
https://huggingface.co/nanovdr/NanoVDR-S

Spaces citing this paper:
https://huggingface.co/spaces/nanovdr/NanoVDR-Demo

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For more data science resources:
https://xn--r1a.website/DataScienceT

#VisualDocumentRetrieval #ModelDistillation #VLM #InformationRetrieval #DeepLearning
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