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English(EN) Neuro-Symbolic Graph Transformers for Hierarchical Intention Recognition from Multimodal Human Activity Sequences

新的神经符号图Transformer增强了人机意图识别能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种神经符号图Transformer（NSGT），旨在提高人机协作中的分层意图识别能力。这种新架构将人类推理图（HR-G）集成到其自注意力机制中，并使用可微分的一阶逻辑（FOL）损失来强制执行多层次意图的本体一致性。在源自NTU RGB+D 120的合成数据集上进行的评估表明，NSGT实现了91.65%的Top-1动作准确率，在使用FOL损失后提高到96.67%。该模型还展示了准确率和结构一致性之间的可调权衡，在保持稳健的动作准确率的同时，将本体矛盾减少了33%。 AI

影响 增强了对人类意图的理解，以实现更复杂的人机协作。

排序理由 详细介绍新模型架构及其评估的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新的神经符号图Transformer增强了人机意图识别能力

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详细介绍新模型架构及其评估的学术论文。[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) · Farnaz Soleimani (LISSI), Abdelghani Chibani (LISSI), Yacine Amirat (LISSI), Ghazaleh Khodabandelou (LISSI) ·

    用于多模态人类活动序列分层意图识别的神经符号图Transformer

    arXiv:2610.18176v1 Announce Type: new Abstract: Proactive human-robot collaboration requires autonomous systems to infer not only immediate actions but also the underlying hierarchical intentions governing human behavior. Current deep learning approaches predominantly focus on fl…

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