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English(EN) EmbeddingGemma 2: Unified and Efficient Multimodal Representations

EmbeddingGemma 2：统一多模态模型树立新标杆

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了 EmbeddingGemma 2，一个开源的多模态嵌入模型，能够在一个统一的空间内表示文本、图像、音频和视频。该模型采用多阶段课程学习和简化的逐层嵌入，在各种单模态、跨模态和混合模态评估中均表现出色。EmbeddingGemma 2 在嵌入基准测试中达到了新的帕累托前沿，超越了专用模型和更大、多模态的模型。其设计还优先考虑了资源受限环境的灵活性，提供了选择性模态编码器加载和量化权重等功能。 AI

影响 为多模态嵌入树立了新标杆，有望改善跨模态搜索和理解。

排序理由 这是一篇详细介绍新型多模态嵌入模型的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

EmbeddingGemma 2：统一多模态模型树立新标杆

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这是一篇详细介绍新型多模态嵌入模型的研究论文。[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) · Henrique Schechter Vera, Sahil Dua, Jovan Andonov, Sara Smoot, Sebastian Russo, Ivan Llanos, Max Matveev, Jae Hyeon Yoo, Kat Black, Shih-Cheng Huang, Josiah Aklilu, Maarten Grootendorst, Omar Sanseviero, Chandramouli Amarnath, Ian Ballantyne, Zhe Li, Dan… ·

    EmbeddingGemma 2: 统一且高效的多模态表示

    arXiv:2610.37364v1 Announce Type: cross Abstract: We present EmbeddingGemma 2, an open multimodal embedding model which natively represents text, images, audio, video, and combinations thereof in a unified space. Our approach introduces improvements such as simplified per-layer e…

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