English(EN) A Multi-Modal Framework with Cross-Subject Pseudo-Labeling and Semantic Alignment for Micro-Gesture Recognition

新框架通过多模态方法增强微手势识别能力

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-11 08:04

研究人员开发了一种新的多模态微手势识别框架，解决了低信噪比和跨主体泛化等挑战。该系统集成了骨骼关节坐标、3D热图体积和RGB视觉特征，并采用新颖的跨模态伪标签策略进行域适应。该方法取得了68.13%的竞争性F1分数，在第四届MiGA-IJCAI挑战赛中获得第四名。 AI

影响这项研究提升了微手势识别能力，有望改进人机交互和情绪检测系统。

排序理由这是一篇详细介绍微手势识别新框架的研究论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.CV TIER_1 English(EN) · Haoran Zhang, Haokun Zhang, Pengyu Liu, Yujia Zhang, Weibao Xue, Yanbin Hao · 2026-06-12 04:00

A Multi-Modal Framework with Cross-Subject Pseudo-Labeling and Semantic Alignment for Micro-Gesture Recognition

arXiv:2606.13030v1 Announce Type: new Abstract: Micro-gestures (MGs) are spontaneous and subtle body movements that frequently convey hidden human emotions. Recognizing MGs in untrimmed videos remains highly challenging due to their extremely low signal-to-noise ratio, severe lon…
arXiv cs.CV TIER_1 English(EN) · Yanbin Hao · 2026-06-11 08:04

A Multi-Modal Framework with Cross-Subject Pseudo-Labeling and Semantic Alignment for Micro-Gesture Recognition

Micro-gestures (MGs) are spontaneous and subtle body movements that frequently convey hidden human emotions. Recognizing MGs in untrimmed videos remains highly challenging due to their extremely low signal-to-noise ratio, severe long-tailed class distribution, and the inherent do…