  In this work, we focus on a special group of human body language -- the
micro-gesture (MG), which differs from the range of ordinary illustrative
gestures in that they are not intentional behaviors performed to convey
information to others, but rather unintentional behaviors driven by inner
feelings. This characteristic introduces two novel challenges regarding
micro-gestures that are worth rethinking. The first is whether strategies
designed for other action recognition are entirely applicable to
micro-gestures. The second is whether micro-gestures, as supplementary data,
can provide additional insights for emotional understanding. In recognizing
micro-gestures, we explored various augmentation strategies that take into
account the subtle spatial and brief temporal characteristics of
micro-gestures, often accompanied by repetitiveness, to determine more suitable
augmentation methods. Considering the significance of temporal domain
information for micro-gestures, we introduce a simple and efficient
plug-and-play spatiotemporal balancing fusion method. We not only studied our
method on the considered micro-gesture dataset but also conducted experiments
on mainstream action datasets. The results show that our approach performs well
in micro-gesture recognition and on other datasets, achieving state-of-the-art
performance compared to previous micro-gesture recognition methods. For
emotional understanding based on micro-gestures, we construct complex emotional
reasoning scenarios. Our evaluation, conducted with large language models,
shows that micro-gestures play a significant and positive role in enhancing
comprehensive emotional understanding. The scenarios we developed can be
extended to other micro-gesture-based tasks such as deception detection and
interviews. We confirm that our new insights contribute to advancing research
in micro-gesture and emotional artificial intelligence.
