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English(EN) Conan-embedding-v3: Fusing Modality-Specific Models for Omni-Modal Embedding

Conan-embedding-v3 融合模型以实现统一的多模态嵌入

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了 Conan-embedding-v3，一个旨在为文本、图像、视频、文档和音频等多种数据模态创建统一嵌入空间的新框架。该方法涉及独立训练特定模态的模型，然后将它们的任务向量融合到一个单一主干中。解决的一个关键挑战是“投影仪漂移”，当融合具有外部编码器的模型时会发生这种情况，导致音频等特定模态的性能下降。Conan-embedding-v3 采用“投影仪恢复”和多模态排练来缓解此问题，在 MMEB 和 MAEB 等基准测试中取得了出色的性能。 AI

影响 引入了一个新颖的框架，用于将各种数据类型统一到单个嵌入空间中，从而可能改善跨模态检索和理解。

排序理由 这是一篇研究论文，详细介绍了用于多模态嵌入的新模型架构和框架。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

Conan-embedding-v3 融合模型以实现统一的多模态嵌入

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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Shiyu Li, Zhiyuan Hu, Yifan Wang, Peiming Li, Zheng Wei, Yang Tang ·

    Conan-embedding-v3：融合特定模态模型以实现全模态嵌入

    arXiv:2606.09331v1 Announce Type: cross Abstract: Omni-modal retrieval promises a single embedding space for text, image, video, document, and audio inputs, but building such a unified retriever is difficult since these modalities differ in data distribution, architecture, and op…

  2. arXiv cs.LG TIER_1 English(EN) · Yang Tang ·

    Conan-embedding-v3：融合特定模态模型以实现全模态嵌入

    Omni-modal retrieval promises a single embedding space for text, image, video, document, and audio inputs, but building such a unified retriever is difficult since these modalities differ in data distribution, architecture, and optimization dynamics. In this work, we present Cona…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Conan-embedding-v3：融合特定模态模型以实现全模态嵌入

    Omni-modal retrieval promises a single embedding space for text, image, video, document, and audio inputs, but building such a unified retriever is difficult since these modalities differ in data distribution, architecture, and optimization dynamics. In this work, we present Cona…

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