6 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…
VLA: Prior-Guided Vision-Language-Action Models via World Knowledge Variation
Yijie Zhu, Jie He, Rui Shao +4
Recent vision-language-action (VLA) models have significantly advanced robotic manipulation by unifying perception, reasoning, and control. To achieve such integration, recent stud…
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…
Period-LLM: Extending the Periodic Capability of Multimodal Large Language Model
Yuting Zhang, Hao Lu, Qingyong Hu +4
Periodic or quasi-periodic phenomena reveal intrinsic characteristics in various natural processes, such as weather patterns, movement behaviors, traffic flows, and biological sign…
FEALLM: Advancing Facial Emotion Analysis in Multimodal Large Language Models with Emotional Synergy and Reasoning
Zhuozhao Hu, Kaishen Yuan, Xin Liu +5
Facial Emotion Analysis (FEA) plays a crucial role in visual affective computing, aiming to infer a person's emotional state based on facial data. Scientifically, facial expression…
AU-TTT: Vision Test-Time Training model for Facial Action Unit Detection
Bohao Xing, Kaishen Yuan, Zitong Yu +2
Facial Action Units (AUs) detection is a cornerstone of objective facial expression analysis and a critical focus in affective computing. Despite its importance, AU detection faces…