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English(EN) Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval

新的SSA-ME框架增强LMM以改进跨模态检索

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员引入了一个名为显著主体感知多模态嵌入（SSA-ME）的新框架，以解决大型多模态模型中的视觉忽视和语义漂移问题。该方法侧重于主体级别的语义，而不仅仅是样本级别的目标，旨在改进模型在复杂查询中对语义相关主体的分组方式。SSA-ME利用视觉专家和显著性引导目标来更好地对齐跨模态注意力并重新校准视觉特征，从而提高多模态检索性能。 AI

影响 通过解决大型多模态模型中的语义漂移和视觉忽视问题来改进多模态检索。

排序理由 该集群描述了一篇关于大型多模态模型新颖框架的学术论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的SSA-ME框架增强LMM以改进跨模态检索

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该集群描述了一篇关于大型多模态模型新颖框架的学术论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Guosheng Zhang, Linkai Liu, Keyao Wang, Haixiao Yue, Zhiwen Tan, Xiao Tan ·

    应对大型多模态模型中的视觉忽视和语义漂移，以增强跨模态检索

    arXiv:2604.25273v1 Announce Type: new Abstract: Despite significant progress in Unified Multimodal Retrieval (UMR) powered by Large Multimodal Models (LMMs), existing embedding methods primarily focus on sample-level objectives via contrastive learning while overlooking the cruci…

  2. arXiv cs.CV TIER_1 English(EN) · Xiao Tan ·

    大型多模态模型在增强跨模态检索中的视觉忽视与语义漂移对抗

    Despite significant progress in Unified Multimodal Retrieval (UMR) powered by Large Multimodal Models (LMMs), existing embedding methods primarily focus on sample-level objectives via contrastive learning while overlooking the crucial subject-level semantics. This limitation hind…

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