面向微手势识别的主动推理：EFE 引导的时序采样与自适应学习
Active Inference for Micro-Gesture Recognition: EFE-Guided Temporal Sampling and Adaptive Learning
把期望自由能（EFE）当作时序片段选择的主动采样准则，并用预测不确定性给样本加权，这套「不确定性驱动的片段选择+加权」可直接迁移到细粒度动作识别、微表情、以及低质量视频中的关键帧/关键片段选取。
原文依据 actively selects the most discriminative temporal segments under EFE guidance
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论微手势是由无意识的神经与情绪活动触发的细微、瞬态动作，在人机交互与临床监测中具有很大潜力。然而，其幅度低、持续时间短、被试间差异大，使现有深度模型在低样本、噪声和跨被试条件下容易性能退化。本文提出一个基于主动推理的微手势识别框架，其核心是期望自由能（EFE）引导的时序采样与不确定性感知的自适应学习。模型在 EFE 引导下主动选择最具判别力的时序片段，从而实现动态观察并最大化信息增益。同时，由预测不确定性驱动的样本加权缓解了标签噪声与分布偏移的影响。在 SMG 数据集上的实验证明了所提方法的有效性，在多个主流骨干网络上均取得一致提升。消融研究确认，EFE 引导的观察与自适应学习机制对性能提升都至关重要。本工作为低资源、噪声条件下的时序行为建模提供了一种可解释且可扩展的范式，可广泛应用于可穿戴传感、人机交互与临床情绪监测。
英文原文摘要 1342 字符
Micro-gestures are subtle and transient movements triggered by unconscious neural and emotional activities, holding great potential for human-computer interaction and clinical monitoring. However, their low amplitude, short duration, and strong inter-subject variability make existing deep models prone to degradation under low-sample, noisy, and cross-subject conditions. This paper presents an active inference-based framework for micro-gesture recognition, featuring Expected Free Energy (EFE)-guided temporal sampling and uncertainty-aware adaptive learning. The model actively selects the most discriminative temporal segments under EFE guidance, enabling dynamic observation and information gain maximization. Meanwhile, sample weighting driven by predictive uncertainty mitigates the effects of label noise and distribution shift. Experiments on the SMG dataset demonstrate the effectiveness of the proposed method, achieving consistent improvements across multiple mainstream backbones. Ablation studies confirm that both the EFE-guided observation and the adaptive learning mechanism are crucial to the performance gains. This work offers an interpretable and scalable paradigm for temporal behavior modeling under low-resource and noisy conditions, with broad applicability to wearable sensing, HCI, and clinical emotion monitoring.