activity
20232026
most citedMulti-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection

1 citations · 2 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024

EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning

Bohao Xing, Zitong Yu, Xin Liu +6

Facial expression recognition (FER) is an important research topic in emotional artificial intelligence. In recent decades, researchers have made remarkable progress. However, curr…

cs.CV2024

GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

Hao Lu, Xuesong Niu, Jiyao Wang +12

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in l…

cs.CV2024★ 1 cited

AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit Detectors

Kaishen Yuan, Zitong Yu, Xin Liu +3

Facial Action Units (AU) is a vital concept in the realm of affective computing, and AU detection has always been a hot research topic. Existing methods suffer from overfitting iss…

cs.CV2023★ 1 cited

Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection

Xin Liu, Kaishen Yuan, Xuesong Niu +4

Facial Action Unit (AU) detection is a crucial task in affective computing and social robotics as it helps to identify emotions expressed through facial expressions. Anatomically,…