English(EN) MoEMB: Scaling Universal Multimodal Embeddings with Efficient Mixture-of-Experts Models

MoEMB 使用混合专家模型高效扩展多模态嵌入

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员推出 MoEMB，一种利用混合专家 (MoE) 模型扩展通用多模态嵌入 (UME) 的新方法。该方法在不增加表示大小或检索工作量的情况下增强了编码器容量，解决了先前扩展技术的局限性。MoEMB 在 MMEB-V2 和 MRMR 基准测试上取得了最先进的性能，与现有方法相比，激活参数和计算量明显更少。该研究还探讨了自适应计算策略，以进一步提高基于 MoE 的嵌入模型的效率，使其更适合大规模检索和推荐系统。 AI

影响这项研究可能为检索和推荐等任务带来更高效、更强大的多模态人工智能系统。

排序理由这是一篇详细介绍新模型架构及其在基准测试上性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Xuanming Cui, Shlok Kumar Mishra, Wentao Bao, Aashu Singh, Zihao Wang, Xiangjun Fan, Jun Xiao, Ser-Nam Lim, Jianpeng Cheng · 2026-09-10 04:00

MoEMB：通过高效的混合专家模型扩展通用多模态嵌入

arXiv:2609.08663v1 Announce Type: cross Abstract: Universal multimodal embedding (UME) increasingly demands encoder's capacity for handling a broad range of tasks and modalities with increased complexity. Prior scaling methods either increase the representation size, retrieval ef…

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MoEMB：通过高效的混合专家模型扩展通用多模态嵌入

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