  Micro-expression recognition (MER) aims to recognize the short and subtle
facial movements from the Micro-expression (ME) video clips, which reveal real
emotions. Recent MER methods mostly only utilize special frames from ME video
clips or extract optical flow from these special frames. However, they neglect
the relationship between movements and space-time, while facial cues are hidden
within these relationships. To solve this issue, we propose the Hierarchical
Space-Time Attention (HSTA). Specifically, we first process ME video frames and
special frames or data parallelly by our cascaded Unimodal Space-Time Attention
(USTA) to establish connections between subtle facial movements and specific
facial areas. Then, we design Crossmodal Space-Time Attention (CSTA) to achieve
a higher-quality fusion for crossmodal data. Finally, we hierarchically
integrate USTA and CSTA to grasp the deeper facial cues. Our model emphasizes
temporal modeling without neglecting the processing of special data, and it
fuses the contents in different modalities while maintaining their respective
uniqueness. Extensive experiments on the four benchmarks show the effectiveness
of our proposed HSTA. Specifically, compared with the latest method on the
CASME3 dataset, it achieves about 3% score improvement in seven-category
classification.
