Evaluating and Correcting Human Annotation Bias in Dynamic Micro-Expression Recognition
School of Psychology, Shanghai Jiao Tong University（心理学系，上海交通大学）；School of Artificial Intelligence and Computer Science, Jiangnan University（人工智能与计算机科学学院，江南大学）
Existing manual labeling of micro-expressions is subject to errors in accuracy, especially in cross-cultural scenarios where deviation in labeling of key frames is more prominent. To address this issue, this paper presents a novel Global Anti-Monotonic Differential Selection Strategy (GAMDSS) architecture for enhancing the effectiveness of spatio-temporal modeling of micro-expressions through keyframe re-selection. Specifically, the method identifies Onset and Apex frames, which are characterized by significant micro-expression variation, from complete micro-expression action sequences via a dynamic frame reselection mechanism. It then uses these to determine Offset frames and construct a rich spatio-temporal dynamic representation. A two-branch structure with shared parameters is then used to efficiently extract spatio-temporal features. Extensive experiments are conducted on seven widely recognized micro-expression datasets. The results demonstrate that GAMDSS effectively reduces subjective errors caused by human factors in multicultural datasets such as SAMM and 4DME. Furthermore, quantitative analyses confirm that offset-frame annotations in multicultural datasets are more uncertain, providing theoretical justification for standardizing micro-expression annotations. These findings directly support our argument for reconsidering the validity and generalizability of dataset annotation paradigms. Notably, this design can be integrated into existing models without increasing the number of parameters, offering a new approach to enhancing micro-expression recognition performance. The source code is available on GitHub[https://github.com/Cross-Innovation-Lab/GAMDSS].
Expansion Quantization Network: An Efficient Micro-emotion Annotation and Detection Framework
School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）；Institute of Scientific and Technical Research on Archives（档案科学技术研究所）；China Electronics Engineering Design Institute Co., Ltd.（中国电子工程设计院有限公司）
Evaluating Self-Correcting Vision Agents Through Quantitative and Qualitative Metrics
The Thørväld Group（Thørväld集团）；Inria Paris-Rocquencourt（巴黎-罗克琴堡Inria）；Rajiv Gandhi University Doimukh（拉贾·甘地大学多伊穆克）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒姆研究实验室）；The Kumquat Consortium（昆夸特联盟）
Motion-guided sparse correction enables expert-quality point tracking across diverse microscopy regimes
Department of Computer Science, Old Dominion University（奥德赛大学计算机科学系）；Department of Biology, Massachusetts Institute of Technology（麻省理工学院生物学系）；The Picower Institute for Learning and Memory, Massachusetts Institute of Technology（麻省理工学院学习与记忆研究所）；Department of Physics and Technology, UiT--The Arctic University of Norway（挪威北极大学物理与技术系）；Department of Physics, University of Oslo（奥斯陆大学物理系）；School of Data Science, Old Dominion University（奥德赛大学数据科学学院）；Department of Physics, Old Dominion University（奥德赛大学物理系）
Unsupervised Semantic Segmentation in Synchrotron Computed Tomography with Self-Correcting Pseudo Labels
Data Science and Learning Division, Argonne National Laboratory（Argonne国家实验室数据科学与学习部门）；X-Ray Science Division, Argonne National Laboratory（Argonne国家实验室X射线科学部门）
Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data
European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence（佛罗伦萨大学欧洲非线性光谱实验室）；Bioretics SrL（Bioretics SrL公司）；National Institute of Optics, National Research Council (INO-CNR)（国家研究委员会国家光学研究所）；Department of Physics（物理系）
Simulating Sinogram-Domain Motion and Correcting Image-Domain Artifacts Using Deep Learning in HR-pQCT Bone Imaging
Weldon School of Biomedical Engineering, Purdue University（沃伦生物医学工程学院，普渡大学）；Department of Radiology and Imaging Sciences, Indiana University School of Medicine（放射学与影像科学系，印第安纳大学医学院）；Department of Physical Therapy, School of Health & Human Sciences, Indiana University Indianapolis（物理治疗系，健康与人类科学学院，印第安纳大学印第安纳波利斯分校）
