Micro-expressions serve as essential cues for understanding individuals'
genuine emotional states. Recognizing micro-expressions attracts increasing
research attention due to its various applications in fields such as business
negotiation and psychotherapy. However, the intricate and transient nature of
micro-expressions poses a significant challenge to their accurate recognition.
Most existing works either neglect temporal dependencies or suffer from
redundancy issues in clip-level recognition. In this work, we propose a novel
framework for micro-expression recognition, named the Adaptive Temporal Motion
Guided Graph Convolution Network (ATM-GCN). Our framework excels at capturing
temporal dependencies between frames across the entire clip, thereby enhancing
micro-expression recognition at the clip level. Specifically, the integration
of Adaptive Temporal Motion layers empowers our method to aggregate global and
local motion features inherent in micro-expressions. Experimental results
demonstrate that ATM-GCN not only surpasses existing state-of-the-art methods,
particularly on the Composite dataset, but also achieves superior performance
on the latest micro-expression dataset CAS(ME)$^3$.

提出了一种名为自适应时间运动引导图卷积网络（ATM-GCN）的微表情识别框架，该框架在整个剪辑中捕获帧之间的时间依赖关系，从而增强了剪辑级别的微表情识别能力。实验证明，ATM-GCN 不仅在 Composite 数据集上超过现有的最先进方法，而且在最新的微表情数据集 CAS (ME)$^3$ 上也取得了卓越的性能。