Existing manual labeling of micro-expressions is subject to errors in accuracy, especially in cross-cultural scenarios where deviation in labeling of key frames is more prominent. To address this issue, this paper presents a novel Global Anti-Monotonic Differential Selection Strategy (GAMDSS) architecture for enhancing the effectiveness of spatio-temporal modeling of micro-expressions through keyframe re-selection. Specifically, the method identifies Onset and Apex frames, which are characterized by significant micro-expression variation, from complete micro-expression action sequences via a dynamic frame reselection mechanism. It then uses these to determine Offset frames and construct a rich spatio-temporal dynamic representation. A two-branch structure with shared parameters is then used to efficiently extract spatio-temporal features. Extensive experiments are conducted on seven widely recognized micro-expression datasets. The results demonstrate that GAMDSS effectively reduces subjective errors caused by human factors in multicultural datasets such as SAMM and 4DME. Furthermore, quantitative analyses confirm that offset-frame annotations in multicultural datasets are more uncertain, providing theoretical justification for standardizing micro-expression annotations. These findings directly support our argument for reconsidering the validity and generalizability of dataset annotation paradigms. Notably, this design can be integrated into existing models without increasing the number of parameters, offering a new approach to enhancing micro-expression recognition performance. The source code is available on GitHub[https://github.com/Cross-Innovation-Lab/GAMDSS].

翻译：现有的微表情人工标注存在准确性误差，尤其在跨文化场景中关键帧标注偏差更为显著。为解决此问题，本文提出一种新颖的全局反单调差分选择策略（GAMDSS）架构，通过关键帧重选增强微表情时空建模的有效性。具体而言，该方法通过动态帧重选机制从完整的微表情动作序列中识别具有显著微表情变化的起始帧与峰值帧，并据此确定偏移帧以构建丰富的时空动态表征。随后采用参数共享的双分支结构高效提取时空特征。在七个广泛认可的微表情数据集上进行了大量实验。结果表明，GAMDSS能有效减少SAMM和4DME等多文化数据集中由人为因素引起的主观误差。此外，定量分析证实多文化数据集中的偏移帧标注具有更高不确定性，这为标准化微表情标注提供了理论依据。这些发现直接支持了我们关于重新审视数据集标注范式有效性与泛化性的论点。值得注意的是，该设计可在不增加参数量的情况下集成至现有模型，为提升微表情识别性能提供了新途径。源代码已发布于GitHub[https://github.com/Cross-Innovation-Lab/GAMDSS]。