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。
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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…
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…
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…