5 papers
AULLM++: Structured-Token-Conditioned Large Language Models for Micro-Expression Action Unit Detection
Zhishu Liu, Kaishen Yuan, Bo Zhao +2
Micro-expression Action Unit (AU) detection identifies localized AUs from subtle facial muscle activations, providing a foundation for decoding affective cues. Previous methods fac…
Navigating the Emotion Tree: Hierarchical Hyperbolic RAG for Multimodal Emotion Recognition
Zeheng Wang, Bo Zhao, Yijie Zhu +6
Multimodal emotion recognition aims to integrate text, audio, and video sources to understand human affective states. Although multimodal large language models excel at multimodal…
AffectAgent: Collaborative Multi-Agent Reasoning for Retrieval-Augmented Multimodal Emotion Recognition
Zeheng Wang, Zitong Yu, Yijie Zhu +9
LLM-based multimodal emotion recognition relies on static parametric memory and often hallucinates when interpreting nuanced affective states. In this paper, given that single-roun…
Complementarity-Supervised Spectral-Band Routing for Multimodal Emotion Recognition
Zhexian Huang, Bo Zhao, Hui Ma +5
Multimodal emotion recognition fuses cues such as text, video, and audio to understand individual emotional states. Prior methods face two main limitations: mechanically relying on…
AU-LLM: Micro-Expression Action Unit Detection via Enhanced LLM-Based Feature Fusion
Zhishu Liu, Kaishen Yuan, Bo Zhao +2
The detection of micro-expression Action Units (AUs) is a formidable challenge in affective computing, pivotal for decoding subtle, involuntary human emotions. While Large Language…