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Italiano(IT) RegRet: Enhancing Region-Level Retrieval in Large Multimodal Models

RegRet框架增强大型多模态模型中的区域级检索

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出RegRet，一个旨在改进大型多模态模型（LMMs）中区域级检索的新框架。该方法侧重于增强图像内详细区域特征的捕获，同时保持整体的全局检索性能。RegRet利用区域感知编码器和多阶段训练流程，包括本地化字幕和区域对比学习。为了解决现有基准测试的局限性，还开发了REGMB基准，该基准包含跨四个任务的22.5万个对比对。 AI

影响 这项研究通过使LMMs能够更精确地理解区域级别，有可能提高基于图像的搜索和RAG系统的准确性和粒度。

排序理由 该集群描述了一篇关于多模态模型新框架和基准测试的最新研究论文。

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RegRet框架增强大型多模态模型中的区域级检索

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报道来源 [2]

  1. arXiv cs.AI TIER_1 Italiano(IT) · Xun Liang, Honghui Yang, Weihang Pan, Ruisi Zhao, Boyuan Pan, Yao Hu, Wenxiao Wang, Binbin Lin, Deng Cai ·

    RegRet：增强大型多模态模型中的区域级检索

    arXiv:2609.16847v1 Announce Type: cross Abstract: Region-level retrieval aims to align user-specified image regions with relevant regions or textual descriptions, playing a crucial role in realworld applications such as e-commerce product search and RAG. Although recent Large Mul…

  2. arXiv cs.IR (Information Retrieval) TIER_1 Italiano(IT) · Deng Cai ·

    RegRet：增强大型多模态模型中的区域级检索

    Region-level retrieval aims to align user-specified image regions with relevant regions or textual descriptions, playing a crucial role in realworld applications such as e-commerce product search and RAG. Although recent Large Multimodal Models (LMMs) have made significant stride…

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