23 citations · 31 across the 12 of their papers we have counts for
14 papers
Do Emotion Concepts Generalize Across Sources, Modalities, and Architectures in Vision-Language Models?
Bohao Xing, Xin Liu, Kaishen Yuan +5
Recent studies suggest that large language models encode emotion concepts as structured internal representations, but most existing work focuses on text and a single architecture.…
Insights from Visual Cognition: Understanding Human Action Dynamics with Overall Glance and Refined Gaze Transformer
Bohao Xing, Deng Li, Rong Gao +2
Recently, Transformer has made significant progress in various vision tasks. To balance computation and efficiency in video tasks, recent works heavily rely on factorized or window…
MSF-Mamba: Motion-aware State Fusion Mamba for Efficient Micro-Gesture Recognition
Deng Li, Jun Shao, Bohao Xing +4
Micro-gesture recognition (MGR) targets the identification of subtle and fine-grained human motions and requires accurate modeling of both long-range and local spatiotemporal depen…
EmotionHallucer: Evaluating Emotion Hallucinations in Multimodal Large Language Models
Bohao Xing, Xin Liu, Guoying Zhao +3
Emotion understanding is a critical yet challenging task. Recent advances in Multimodal Large Language Models (MLLMs) have significantly enhanced their capabilities in this area. H…
MER 2025: When Affective Computing Meets Large Language Models
Zheng Lian, Rui Liu, Kele Xu +15
MER2025 is the third year of our MER series of challenges, aiming to bring together researchers in the affective computing community to explore emerging trends and future direction…
FSBench: A Figure Skating Benchmark for Advancing Artistic Sports Understanding
Rong Gao, Xin Liu, Zhuozhao Hu +4
Figure skating, known as the "Art on Ice," is among the most artistic sports, challenging to understand due to its blend of technical elements (like jumps and spins) and overall ar…
DEEMO: De-identity Multimodal Emotion Recognition and Reasoning
Deng Li, Bohao Xing, Xin Liu +3
Emotion understanding is a critical yet challenging task. Most existing approaches rely heavily on identity-sensitive information, such as facial expressions and speech, which rais…
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…
DiffFAS: Face Anti-Spoofing via Generative Diffusion Models
Xinxu Ge, Xin Liu, Zitong Yu +4
Face anti-spoofing (FAS) plays a vital role in preventing face recognition (FR) systems from presentation attacks. Nowadays, FAS systems face the challenge of domain shift, impacti…
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…
MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning
Rex Liu, Xin Liu
With the exponential growth of multimedia data, leveraging multimodal sensors presents a promising approach for improving accuracy in human activity recognition. Nevertheless, accu…
Enhancing Micro Gesture Recognition for Emotion Understanding via Context-aware Visual-Text Contrastive Learning
Deng Li, Bohao Xing, Xin Liu
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 em…
Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding
Rong Gao, Xin Liu, Bohao Xing +3
In this work, we focus on a special group of human body language -- the micro-gesture (MG), which differs from the range of ordinary illustrative gestures in that they are not inte…
EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model
Deng Li, Xin Liu, Bohao Xing +4
Emotion AI is the ability of computers to understand human emotional states. Existing works have achieved promising progress, but two limitations remain to be solved: 1) Previous s…