标题： CLIP-MG：利用骨骼姿态特征和RGB数据引导语义注意力以在iMiGUE数据集上进行微手势识别 显示英文标题

标题： CLIP-MG: Guiding Semantic Attention with Skeletal Pose Features and RGB Data for Micro-Gesture Recognition on the iMiGUE Dataset

摘要： 微手势识别由于手势细微、不由自主且运动幅度低，在情感计算中是一项具有挑战性的任务。 本文介绍了一种基于姿态引导的语义感知CLIP架构，即用于微手势识别的CLIP（CLIP-MG），该模型是对iMiGUE数据集上的微手势分类量身定制的修改版CLIP模型。 CLIP-MG通过姿态引导的语义查询生成和门控多模态融合机制，将人体姿态（骨架）信息集成到基于CLIP的识别管道中。 所提出的模型达到了61.82%的Top-1准确率。 这些结果既展示了我们方法的潜力，也表明了在完全适应CLIP等视觉-语言模型以进行微手势识别时仍存在的困难。 显示英文摘要

摘要： Micro-gesture recognition is a challenging task in affective computing due to the subtle, involuntary nature of the gestures and their low movement amplitude. In this paper, we introduce a Pose-Guided Semantics-Aware CLIP-based architecture, or CLIP for Micro-Gesture recognition (CLIP-MG), a modified CLIP model tailored for micro-gesture classification on the iMiGUE dataset. CLIP-MG integrates human pose (skeleton) information into the CLIP-based recognition pipeline through pose-guided semantic query generation and a gated multi-modal fusion mechanism. The proposed model achieves a Top-1 accuracy of 61.82%. These results demonstrate both the potential of our approach and the remaining difficulty in fully adapting vision-language models like CLIP for micro-gesture recognition.