Synergistic Spotting and Recognition of Micro-Expression via Temporal State Transition
School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；School of Intelligence Science and Technology, University of Science and Technology Beijing（北京科技大学智能科学与技术学院）
Micro-expressions are involuntary facial movements that cannot be consciously controlled, conveying subtle cues with substantial real-world applications. The analysis of micro-expressions generally involves two main tasks: spotting micro-expression intervals in long videos and recognizing the emotions associated with these intervals. Previous deep learning methods have primarily relied on classification networks utilizing sliding windows. However, fixed window sizes and window-level hard classification introduce numerous constraints. Additionally, these methods have not fully exploited the potential of complementary pathways for spotting and recognition. In this paper, we present a novel temporal state transition architecture grounded in the state space model, which replaces conventional window-level classification with video-level regression. Furthermore, by leveraging the inherent connections between spotting and recognition tasks, we propose a synergistic strategy that enhances overall analysis performance. Extensive experiments demonstrate that our method achieves state-of-the-art performance. The codes and pre-trained models are available at https://github.com/zizheng-guo/ME-TST.
Facial Micro-Expression Spotting and Recognition using Time Contrasted Feature with Visual Memory
Kalyani Government Engineering College（卡利亚尼政府工程学院）；Nanyang Technological University（南洋理工大学）；Cognizant Technology Solutions（高知特科技解决方案公司）；Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）
Spotting Micro-Expressions on Long Videos Sequences
CentraleSupélec（中央理工-高等电力学院）；CNRS（法国国家科学研究中心）；IETR（雷恩电子与电信研究所）；Institute of Psychology, Chinese Academy of Sciences（中国科学院心理研究所）；Manchester Metropolitan University（曼彻斯特城市大学）
Word length-aware text spotting: Enhancing detection and recognition in dense text image
Wuhan Institute of Technology（武汉工程大学）；Hubei Key Laboratory of Intelligent Robot（湖北省智能机器人重点实验室）；Hubei University of Arts and Science（湖北文理学院）；Wuhan University（武汉大学）
MELDAE: A Framework for Micro-Expression Spotting, Detection, and Automatic Evaluation in In-the-Wild Conversational Scenes
College of Computer Science, National University of Defense Technology, China（国防科技大学计算机科学学院）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, China（华南理工大学智能工程学院）；College of Computer Science and Technology, Zhejiang University, China（浙江大学计算机科学与技术学院）；College of Computer Science and Software Engineering, Shenzhen University, China（深圳大学计算机科学与软件工程学院）
Spotting Macro- and Micro-expression Intervals in Long Video Sequences
Institute of Psychology, Chinese Academy of Sciences（中国科学院心理研究所）；University of the Chinese Academy of Sciences（中国科学院大学）；Manchester Metropolitan University（曼彻斯特城市大学）
MEGC2025: Micro-Expression Grand Challenge on Spot Then Recognize and Visual Question Answering
Department of Computing and Mathematics, Manchester Metropolitan University（计算与数学系，曼彻斯特 Metropolitan 大学）；State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences（认知科学与心理健康国家重点实验室，心理学研究所，中国科学院）；Department of Psychology, University of the Chinese Academy of Sciences（心理学系，中国科学院大学）；National Taiwan University（台湾大学）；Zhejiang University（浙江大学）；University of Oulu（奥卢大学）；Harbin Institute of Technology（哈尔滨工业大学）
MEGC2026: Micro-Expression Grand Challenge on Visual Question Answering
Department of Computing and Mathematics, Manchester Metropolitan University（曼彻斯特 Metropolitan 大学计算与数学系）；State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, CAS & Department of Psychology, University of the Chinese Academy of Sciences（中国科学院心理研究所认知科学与心理健康国家重点实验室及中国科学院大学心理学系）；School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia（赫瑞-沃森大学马来西亚分校数学与计算机科学学院）
