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 domain shift encountered in cross-subject evaluation scenarios. In this paper, we propose a comprehensive multi-modal framework for Track 1 of the 4th MiGA-IJCAI Challenge. To capture fine-grained representations, we design a saliency-guided multi-modal extraction pipeline integrating 68-keypoint skeleton joint coordinates, 3D heatmap volumes, and high-resolution RGB visual features. We introduce a gentle square-root smoothed weighting mechanism paired with an Orthogonal Semantic Embedding Loss to protect tail classes without compromising overall recognition capabilities. More importantly, to bridge the cross-subject generalization gap, we propose a Cross-Modal Pseudo-Labeling (CMPL) strategy for unsupervised domain adaptation, which significantly boosts single-modal robustness. A temperature-scaled soft-voting mechanism is finally utilized to alleviate overconfidence during late fusion. Extensive experiments demonstrate that our framework achieves a competitive F1-score of 68.13\%, securing the 4th place.

翻译：微手势（MGs）是自发且细微的身体动作，常能传递隐藏的人类情感。在未裁剪视频中识别微手势极具挑战性，原因在于其极低的信噪比、严重的长尾类别分布以及跨被试评估场景中固有的领域偏移问题。本文针对第四届MiGA-IJCAI挑战赛赛道一，提出了一种全面的多模态框架。为捕捉细粒度表征，我们设计了一种显著性引导的多模态提取流程，融合了68关键点骨骼关节坐标、3D热力图体积和高分辨率RGB视觉特征。我们引入了一种温和的平方根平滑加权机制，配合正交语义嵌入损失函数，在不损害整体识别能力的前提下保护尾部类别。更为关键的是，为弥合跨被试泛化差距，我们提出了一种跨模态伪标签（CMPL）策略实现无监督域适应，显著提升了单模态鲁棒性。最终采用温度缩放软投票机制缓解后融合阶段的过度自信问题。大量实验表明，我们的框架以68.13%的F1分数取得竞争力表现，位列第四名。