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English(EN) Multi-modal Knowledge Preserving Adapter for Embedding Backward Compatibility

新型适配器实现多模态大语言模型向后兼容

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了多模态知识保持适配器（MKP-Adapter），这是一种确保多模态大语言模型（MLLMs）向后兼容的新方法，无需更新核心骨干模型。这种仅适配器的方法利用多级保持损失和焦点重加权策略，解决了在保持兼容性的同时保留新嵌入知识的挑战。实验表明，MKP-Adapter 在图像、文本、视觉文档和视频检索等各种多模态任务中实现了强大的向后兼容性，且附加延迟极小。 AI

影响 该方法可以显著降低多模态AI系统中更新嵌入模型的成本和复杂性。

排序理由 该集群包含一篇详细介绍大语言模型新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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

新型适配器实现多模态大语言模型向后兼容

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该集群包含一篇详细介绍大语言模型新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jaeseok Byun, Gukyeong Kwon, Han-Kai Hsu, Meher Gitika Karumuri, Zhikang Zhang, Hao Yang, Davide Modolo ·

    用于嵌入向后兼容的多模态知识保持适配器

    arXiv:2609.16875v1 Announce Type: new Abstract: Upgrading embedding models typically requires expensive database re-indexing, as new query embeddings are incompatible with existing database embeddings. While Backward Compatible Training (BCT) mitigates this by enforcing compatibi…

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