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English(EN) SAMA: Semantic Anchor-aligned Augmentation for Unified Low-Resource Multimodal Information Extraction

SAMA框架通过语义锚点提升低资源多模态提取能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出SAMA，一个旨在解决多模态信息提取（MIE）任务（如命名实体识别、关系提取和事件提取）中数据稀缺性问题的新型框架。SAMA利用结构化语义锚点指导协同多专家多模态大语言模型（CME-MLLM）生成高质量的合成数据。该框架包含一个用于图像合成的锚点保持扩散机制和一个双约束过滤模块，以确保生成样本的保真度，无需人工验证。实验表明，SAMA在全监督和低资源场景下均显著优于现有的增强方法。 AI

影响 增强了低资源多模态AI任务的数据生成能力，有望提高各种提取应用的性能。

排序理由 这是一篇详细介绍多模态信息提取新方法的学术论文。

在 arXiv cs.CL 阅读 →

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SAMA框架通过语义锚点提升低资源多模态提取能力

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Quanjiang Guo, Chong Mu, Jiazhou Pan, Ming Jia, Ling Tian, Hui Gao, Zhao Kang ·

    SAMA：统一低资源多模态信息提取的语义锚点对齐增强

    arXiv:2606.18780v1 Announce Type: cross Abstract: Multimodal Information Extraction (MIE)-covering tasks such as Multimodal Named Entity Recognition (MNER), Relation Extraction (MRE), and Event Extraction (MEE)-is essential for understanding multimedia content but remains constra…

  2. arXiv cs.CV TIER_1 English(EN) · Zhao Kang ·

    SAMA：统一低资源多模态信息提取的语义锚点对齐增强

    Multimodal Information Extraction (MIE)-covering tasks such as Multimodal Named Entity Recognition (MNER), Relation Extraction (MRE), and Event Extraction (MEE)-is essential for understanding multimedia content but remains constrained by severe data scarcity. Although data augmen…

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