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

NanoVDR将2B VLM蒸馏成69M文本编码器,用于更快的视觉文档检索

研究人员开发了NanoVDR,这是一种新颖的视觉文档检索方法,可显著降低计算成本。通过将一个大型的2B参数视觉-语言模型(VLM)蒸馏成一个参数量小得多的69M的纯文本编码器,NanoVDR实现了教师模型95.1%的质量,同时大幅降低了延迟和GPU需求。该方法将文档和查询的编码路径解耦,认识到查询通常是更简单的文本字符串,需要更少的复杂处理。 AI

影响 通过显著减小模型尺寸和计算需求,实现更快、更高效的视觉文档检索。

排序理由 这是一篇研究论文,详细介绍了一种提高视觉文档检索模型效率的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

NanoVDR将2B VLM蒸馏成69M文本编码器,用于更快的视觉文档检索

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这是一篇研究论文,详细介绍了一种提高视觉文档检索模型效率的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhuchenyang Liu, Yao Zhang, Yu Xiao ·

    NanoVDR:将20亿参数的视觉语言检索器提炼为7000万参数的纯文本编码器,用于视觉文档检索

    arXiv:2603.12824v2 Announce Type: replace-cross Abstract: Vision-Language Model (VLM) based retrievers have advanced visual document retrieval (VDR) to impressive quality. They require the same multi-billion parameter encoder for both document indexing and query encoding, incurri…