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English(EN) Hierarchical Multimodal Interaction Reasoning for Dyadic Relationship Recognition

新框架HiRe-DRR改进了二元关系识别

研究人员开发了HiRe-DRR,一个新颖的分层多模态交互推理框架,旨在提高识别个体之间社会关系的准确性。该框架将面部表情、肢体语言、音频和文本等各种线索分解为不同的表示级别,并对参与者之间的同质和异质交互进行建模。一个关键组成部分是交互混合Transformer(MoI-Former),它使用专门的专家和关系感知路由来精确识别相关的交互证据,并通过分层条件损失(HC-Loss)进行结构化、粗到细的推理。在多个基准上的实验表明,HiRe-DRR优于现有方法,分析表明,远距离关系更依赖低级交互,而亲密关系则利用更高级别的交互。 AI

影响 这项研究可能带来更细致的AI系统,能够理解个体之间复杂的社会动态和关系。

排序理由 该集群包含一篇详细介绍二元关系识别新框架和模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新框架HiRe-DRR改进了二元关系识别

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该集群包含一篇详细介绍二元关系识别新框架和模型的学术论文。[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) · Wang Tang, Fethiye Irmak Dogan, Linbo Qing, Ce Zhu, Hatice Gunes ·

    用于二元关系识别的分层多模态交互推理

    arXiv:2610.36249v1 Announce Type: new Abstract: Dyadic relationship recognition aims to identify the latent social relationship between two individuals from their multimodal interactions. Existing methods typically project heterogeneous cues into a shared representation and optim…