Factory automation has become increasingly important due to labor shortages, leading to the introduction of autonomous mobile robots for tasks such as material transportation. Markers are commonly used for robot self-localization and object identification. In the RoboCup Logistics League (RCLL), ArUco markers are employed both for robot localization and for identifying processing modules. Conventional recognition relies on OpenCV-based image processing, which detects black-and-white marker patterns. However, these methods often fail under noise, motion blur, defocus, or varying illumination conditions. Deep-learning-based recognition offers improved robustness under such conditions, but requires large amounts of annotated data. Annotation must typically be done manually, as the type and position of objects cannot be detected automatically, making dataset preparation a major bottleneck. In contrast, ArUco markers include built-in recognition modules that provide both ID and positional information, enabling automatic annotation. This paper proposes an automated annotation method for training deep-learning models on ArUco marker images. By leveraging marker detection results obtained from the ArUco module, the proposed approach eliminates the need for manual labeling. A YOLO-based model is trained using the automatically annotated dataset, and its performance is evaluated under various conditions. Experimental results demonstrate that the proposed method improves recognition performance compared with conventional image-processing techniques, particularly for images affected by blur or defocus. Automatic annotation also reduces human effort and ensures consistent labeling quality. Future work will investigate the relationship between confidence thresholds and recognition performance.
3D-POP -- An automated annotation approach to facilitate markerless 2D-3D tracking of freely moving birds with marker-based motion capture
University of Konstanz（康斯坦茨大学）；Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；Technische Universität München（慕尼黑工业大学）；Eötvös Loránd University（厄特沃什·罗兰大学）；Hungarian Academy of Sciences（匈牙利科学院）
机器学习和计算机视觉的最新进展正在彻底改变动物行为领域，使研究人员能够在没有任何标记的情况下跟踪自由移动的动物的姿势和位置。然而，用于无标记姿态跟踪的动物注释图像的大数据集，特别是从多个角度拍摄的具有准确3D注释的高分辨率图像，仍然很少。在这里，我们提出了一种方法，使用运动捕捉（mo-cap）系统，以半自动的方式获得大量的动物运动和姿势（2D和3D）的注释数据。我们的方法是新颖的，因为它提取的3D位置的形态关键点（如眼睛，喙，尾巴）在参考的位置标记附着到动物。使用这种方法，我们获得并在这里提供了一个新的数据集- 3D-POP，其中包含大约300 k注释帧（400万个实例），这些注释帧以视频的形式存在，其中包含来自3.6m x 4.2m区域中的4个不同相机视图的一到十只自由移动的鸟类。3D-POP是第一个具有2D和3D精确关键点注释以及限界框和个体身份的群集鸟类数据集，将有助于开发2D到3D无标记姿势，轨迹跟踪和鸟类识别问题的解决方案。
Multistep Automated Data Labelling Procedure (MADLaP) for Thyroid Nodules on Ultrasound: An Artificial Intelligence Approach for Automating Image Annotation
Duke University（杜克大学）；Duke University Medical Center（杜克大学医学中心）
Restoration of marker occluded hematoxylin and eosin stained whole slide histology images using generative adversarial networks
Image Data Analytics, Informatics IT, Merck \& Co., Inc., West Point, PA 19486, USA Image Data Analytics, Informatics IT, Merck \& Co., Inc., Rahway, NJ 07065, USA
CellMaster: Collaborative Cell Type Annotation in Single-Cell Analysis
Halicioglu Data Science Institute, University of California, San Diego, CA, USA（哈利奇奥格卢数据科学研究所，加州大学圣地亚哥分校）；Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, USA（电气与计算机工程系，德克萨斯A&M大学）；Department of Medicine, University of California, San Diego, CA, USA（医学系，加州大学圣地亚哥分校）；Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, USA（化学与生物化学系，加州大学圣地亚哥分校）；Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA（摩尔癌症中心，加州大学圣地亚哥分校）；Mohamed bin Zayed University of AI, Abu Dhabi, UAE（穆罕默德·本·扎耶德人工智能大学）；School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA（计算机科学学院，卡内基梅隆大学）
Whole-Slide Image Analysis under Realistic Few-Shot Annotation Protocols
Institute of Information and Communication Technologies, Electronics, and Applied Mathematics (ICTEAM), Université catholique de Louvain (UCLouvain)（天主教鲁汶大学信息与通信技术、电子与应用数学研究所）
针对现实 whole-slide 图像场景的小样本标注问题，提出 SlideCRF 模型，结合空间与生物线索优化预测，在四个数据集上宏观 F1 较现有转导方法更优，零样本预测提升显著。
Learning Visual Prompts for Guiding the Attention of Vision Transformers
School of Computation, Information and Technology（计算、信息与技术学院）；Technical University of Munich（慕尼黑工业大学）；AI Innovation, Data:Lab Munich（慕尼黑人工智能创新数据实验室）；Volkswagen AG（大众汽车集团）；Department of Engineering Science（工程科学系）；University of Oxford（牛津大学）
SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers
School of Computer Science University of St Andrews（计算机科学学院圣安德鲁大学）；School of Medicine University of St Andrews（医学学院圣安德鲁大学）；Lothian Biorepository NHS Lothian（洛锡安生物库NHS洛锡安）
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy
National Institute of Nuclear Physics（国家核物理研究所）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；University of Bologna（博洛尼亚大学）
Automating the assessment of biofouling in images using expert agreement as a gold standard
Centre of Excellence for Biosecurity Risk Analysis, The University of Melbourne（生物安全风险分析卓越中心，墨尔本大学）；Mathematics And Statistics, The University of Melbourne（墨尔本大学数学与统计学院）；Biosecurity Animal Division, Department of Agriculture, Water and the Environment（农业、水利与环境部生物安全动物司）
AWGUNET: Attention-Aided Wavelet Guided U-Net for Nuclei Segmentation in Histopathology Images
Jadavpur University（jadavpur大学）；Artificial Intelligence Research Institute, China University of Mining and Technology（中国矿业大学人工智能研究院）；Saint Petersburg Electrotechnical University "LETI"（圣彼得堡国立电工技术大学"LETI"）
Adaptive Multi-Scale Integration Unlocks Robust Cell Annotation in Histopathology Images
Department of Computer and Information Science, University of Pennsylvania（计算机与信息科学系，宾夕法尼亚大学）；Department of Bioengineering, University of Pennsylvania（生物工程系，宾夕法尼亚大学）；Department of Pathology and Laboratory Medicine, University of Pennsylvania（病理学与实验室医学系，宾夕法尼亚大学）；Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania（生物统计学、流行病学与信息学系，宾夕法尼亚大学）
Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images
Division of Computing and Data Science, Ruđer Bošković Institute（鲁德·波斯科维奇研究所计算与数据科学部）；Sitnik Ventures UG（Sitnik Ventures公司）；Department of Pathology and Cytology, Dubrava University Hospital（杜布拉瓦大学医院病理科与细胞学部）；Clinical Division for Pathology and Cytology, Clinical Hospital Center Zagreb（扎格雷布临床医院中心病理科与细胞学部）；Division of Molecular Medicine, Ruđer Bošković Institute（鲁德·波斯科维奇研究所分子医学部）
Comparison of marker-less 2D image-based methods for infant pose estimation
University Medical Center Göttingen（戈滕堡大学医学中心）；German Center for Child and Adolescent Health (DZKJ)（德国儿童与青少年健康研究中心）；Leibniz ScienceCampus Göttingen（戈滕堡莱比锡科学校园）；University of Göttingen（戈滕堡大学）；III Institute of Physics - Biophysics（物理-生物物理第三研究所）；University Hospital Heidelberg（海德堡大学医院）；Ruprecht-Karls University of Heidelberg（海德堡鲁珀特-卡尔大学）；Department of Child and Adolescent Psychiatry（儿童与青少年精神病科部门）；Medical University of Graz（格拉茨医学院）；Curtin Autism Research Group（库林自闭症研究组）；Curtin School of Allied Health（库林健康联盟学校）；Curtin University（库林大学）；Center of Neurodevelopmental Disorders (KIND)（神经发育障碍中心（KIND））；Department of Women’s and Children’s Health（妇女与儿童健康部门）；Center for Psychiatry Research（精神医学研究中心）；Karolinska Institutet（卡罗林斯卡研究所）；Region Stockholm（斯德哥尔摩地区）；Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm（斯德哥尔摩健康服务儿童与青少年精神病科，斯德哥尔摩地区）；Stockholm Health Care Services, Region Stockholm（斯德哥尔摩健康服务，斯德哥尔摩地区）
