From Subtle Hints to Grand Expressions - Mastering Fine-grained Emotions with Dynamic Multimodal Analysis

Multimodal Emotion Analysis (MEA) plays a crucial role in extracting and understanding emotional insights from diverse data sources, including text, video, and audio. However, existing methods may overlook the key issue that multimodal components exhibit asynchronism temporally and they obtain insufficient representation of fine-grained emotional expressions. In light of this, we propose a unified emotion reasoning model, EmoChat, which enhances multimodal emotion analysis by dynamically generating emotion-related tokens and fine-grained expression information through facial action modeling. To incorporate expression semantics, we design the AU Agent, a lightweight facial expression extractor, to provide LLMs with fine-grained facial knowledge for reasoning. In addition, we propose the Correlation Aggregator to alleviate the correlation differences between acoustic features and textual content. Therefore, our method decouples both the audio and vision modalities, allowing for efficient token-level emotion cues mining in misaligned multimodal input, while maintaining semantic consistency across different languages. Experiments on public benchmark datasets have demonstrated the superiority of our proposed EmoChat over the state-of-the-art methods.