Enhancing Micro Gesture Recognition for Emotion Understanding via Context-aware Visual-Text Contrastive Learning

TL;DR

This paper introduces a novel visual-text contrastive learning approach with adaptive prompting to improve micro gesture recognition for emotion understanding, achieving state-of-the-art results and demonstrating the benefit of textual information in emotion analysis.

Contribution

It proposes a new visual-text contrastive learning method with adaptive prompting for micro gesture recognition, integrating textual data to enhance emotion understanding.

Findings

Achieves state-of-the-art performance on two datasets.

Textual information improves emotion understanding by over 6%.

The method outperforms existing single-modality approaches.

Abstract

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.