Active Inference for Micro-Gesture Recognition: EFE-Guided Temporal Sampling and Adaptive Learning

TL;DR

This paper introduces an active inference framework for micro-gesture recognition that dynamically selects informative segments and adapts to noise, improving accuracy in challenging low-resource and noisy environments.

Contribution

It proposes EFE-guided temporal sampling and uncertainty-aware adaptive learning, enhancing micro-gesture recognition under adverse conditions.

Findings

Improved recognition accuracy across multiple backbones.

Effective noise mitigation via sample weighting.

Validation on SMG dataset confirms robustness.

Abstract

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.