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English(EN) MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control

新的MMEmb-R1框架通过自适应推理增强多模态嵌入

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的MMEmb-R1框架，旨在通过自适应地整合推理能力来增强多模态嵌入。该方法通过仅在有益时选择性地应用思维链推理来解决MLLM中的挑战，从而防止不必要的计算和潜在的语义模糊。在MMEB-V2基准上的实验表明，MMEmb-R1使用4B参数模型取得了最先进的性能，显著降低了推理开销和推理延迟。 AI

影响 该框架通过优化推理过程，有望带来更高效、更有效的多模态AI系统。

排序理由 该集群包含一篇详细介绍新模型和基准结果的研究论文。[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 →

新的MMEmb-R1框架通过自适应推理增强多模态嵌入

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该集群包含一篇详细介绍新模型和基准结果的研究论文。[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) · Yuchi Wang, Dingkang Yang, Haiyang Yu, Weikang Bian, Jiefeng Long, Xiao Liang, Chao Feng, Hongsheng Li ·

    MMEmb-R1：具有对感知选择和自适应控制的增强推理多模态嵌入

    arXiv:2604.06156v2 Announce Type: replace-cross Abstract: MLLMs have been successfully applied to multimodal embedding tasks, yet their generative reasoning capabilities remain underutilized. Directly incorporating chain-of-thought reasoning into embedding learning introduces two…

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