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English(EN) jina-embeddings-v5-omni: Geometry-preserving Embeddings via Locked Aligned Towers

GELATO架构实现高效多模态嵌入

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新的多模态嵌入模型架构GELATO（Geometry-preserving Embeddings via Locked Aligned Towers，通过锁定的对齐塔实现几何保持的嵌入）。GELATO通过整合冻结的图像和音频编码器来扩展现有的文本嵌入模型，仅训练连接组件。这种方法实现了高效训练，并保持了原始文本嵌入的质量，同时在与更大的多模态模型相比时取得了有竞争力的性能。 AI

影响 能够更高效地训练多模态模型，可能降低复杂AI应用的入门门槛。

排序理由 该集群包含一篇详细介绍新模型架构及其评估的arXiv论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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GELATO架构实现高效多模态嵌入

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该集群包含一篇详细介绍新模型架构及其评估的arXiv论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Florian H\"onicke, Michael G\"unther, Andreas Koukounas, Mohammad Kalim Akram, Saba Sturua, Han Xiao ·

    jina-embeddings-v5-omni: 通过锁定对齐塔实现几何保持嵌入

    arXiv:2605.08384v4 Announce Type: replace Abstract: In this work, we introduce GELATO (Geometry-preserving Embeddings via Locked Aligned TOwers), a novel approach to multimodal embedding models. We build on the VLM-style architecture, in which non-text encoders are adapted to pro…

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