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English(EN) Two-Stage Multi-modal Learning for Dynamic Audio-Visual Navigation: From Physical Alignment to Scene Aggregation

新框架通过多模态学习增强动态视听导航

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了一个新的两阶段多模态学习框架，旨在改进智能体的动态视听导航（D-AVN）。第一阶段，强度校准几何编码器，将声音强度与视觉几何对齐，以实现类别无关的空间理解。第二阶段，跨模态场景聚合器，使用Transformer整合多模态特征以获得鲁棒的状态表示。在Replica和Matterport3D数据集上的实验表明，该方法取得了最先进的结果，提高了泛化能力和导航效率，尤其是在未听过的声音类别方面。 AI

影响 这项研究可能催生出更强大的具身AI智能体，它们能够利用视觉和听觉线索在复杂环境中导航，尤其是在处理动态或未听过的声源时。

排序理由 该集群包含一篇详细介绍新方法和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新框架通过多模态学习增强动态视听导航

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该集群包含一篇详细介绍新方法和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jia Li, Yinfeng Yu ·

    动态视听导航的两阶段多模态学习：从物理对齐到场景聚合

    arXiv:2610.14077v1 Announce Type: cross Abstract: Embodied Audio-Visual Navigation (AVN) requires an agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has achieved notable success with static sound sources, real-world scenarios often i…

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