  Psychological studies have shown that Micro Gestures (MG) are closely linked
to human emotions. MG-based emotion understanding has attracted much attention
because it allows for emotion understanding through nonverbal body gestures
without relying on identity information (e.g., facial and electrocardiogram
data). Therefore, it is essential to recognize MG effectively for advanced
emotion understanding. However, existing Micro Gesture Recognition (MGR)
methods utilize only a single modality (e.g., RGB or skeleton) while
overlooking crucial textual information. In this letter, we propose a simple
but effective visual-text contrastive learning solution that utilizes text
information for MGR. In addition, instead of using handcrafted prompts for
visual-text contrastive learning, we propose a novel module called Adaptive
prompting to generate context-aware prompts. The experimental results show that
the proposed method achieves state-of-the-art performance on two public
datasets. Furthermore, based on an empirical study utilizing the results of MGR
for emotion understanding, we demonstrate that using the textual results of MGR
significantly improves performance by 6%+ compared to directly using video as
input.
