12 papers
Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units
Chao Hao, Zezheng Wang, Yanhua Huang +4
This paper investigates the enhancement of reasoning capabilities in language models through token-level multi-model collaboration. Our approach selects the optimal tokens from the…
Distribution-Specific Learning for Joint Salient and Camouflaged Object Detection
Chao Hao, Zitong Yu, Xin Liu +5
Salient object detection (SOD) and camouflaged object detection (COD) are two closely related but distinct computer vision tasks. Although both are class-agnostic segmentation task…
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…
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…
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…
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…