We introduce Fluid Annotation, an intuitive human-machine collaboration interface for annotating the class label and outline of every object and background region in an image. Fluid annotation is based on three principles: (I) Strong Machine-Learning aid. We start from the output of a strong neural network model, which the annotator can edit by correcting the labels of existing regions, adding new regions to cover missing objects, and removing incorrect regions. The edit operations are also assisted by the model. (II) Full image annotation in a single pass. As opposed to performing a series of small annotation tasks in isolation, we propose a unified interface for full image annotation in a single pass. (III) Empower the annotator. We empower the annotator to choose what to annotate and in which order. This enables concentrating on what the machine does not already know, i.e. putting human effort only on the errors it made. This helps using the annotation budget effectively. Through extensive experiments on the COCO+Stuff dataset, we demonstrate that Fluid Annotation leads to accurate annotations very efficiently, taking three times less annotation time than the popular LabelMe interface.
Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation
Zhejiang Sci-Tech University（浙江理工大学）；Lishui University（丽水学院）；the Second Affiliated Hospital of Zhejiang University, College of Medicine（浙江大学医学院附属第二医院）；Communication University of Zhejiang（浙江传媒学院）；Hangzhou Dianzi University（杭州电子科技大学）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；Suzhou Research Institute of Shandong University（山东大学苏州研究院）
DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model
West China Biomedical Big Data Center（西昌生物医学大数据中心）；West China Hospital（西昌医院）；Sichuan University（四川大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Human and AI collaboration for pulmonary nodule segmentation
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（数学科学国家重点实验室，数学与系统科学研究院，中国科学院）；PET/CT Center, The First Affiliated Hospital of Guangzhou Medical University（广州医科大学第一附属医院PET/CT中心）；Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University（广州医科大学第一附属医院胸外科与肿瘤科）；Department of Mathematical Science, Tsinghua University（清华大学数学科学系）；Yau Mathematical Sciences Center, Tsinghua University（清华大学尤金数学科学中心）；China State Key Laboratory of Respiratory Disease & National Clinical Research Centre for Respiratory Disease, Guangzhou, China（中国呼吸疾病国家重点实验室及呼吸疾病临床研究中心，广州，中国）；School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）；National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University（呼吸医学国家中心、呼吸疾病临床研究中心、广州呼吸健康研究院、广州医科大学第一附属医院）
MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan)（计算能力网络与信息安全部教育部重点实验室，山东计算机科学中心（济南国家超级计算机中心））；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东科学院））；Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science（山东省计算能力互联网与服务计算重点实验室，山东省计算机科学基础研究中心）
Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images
Nagoya University Graduate School of Medicine（名古屋大学医学研究生院）；Graduate School of Informatics, Nagoya University（名古屋大学信息研究生院）；School of Information Science, Aichi Institute of Technology（爱知工业大学信息学院）；Graduate School of Medicine, University of Tokyo（东京大学医学研究生院）；Department of Radiology, Aichi Medical University（爱知医科大学放射科）；Information Technology Center, Nagoya University（名古屋大学信息技术中心）；Research Center for Medical Bigdata, National Institute of Informatics（日本信息学研究所医疗大数据研究中心）
Camouflage-aware Image-Text Retrieval via Expert Collaboration
College of Computer Science, Sichuan University（四川大学计算机科学学院）；National Key Lab of Fundamental Science on Synthetic Vision, Sichuan University（四川大学视觉合成图形图像技术国家级重点实验室）
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Lausanne University Hospital（洛桑大学医院）；University of Lausanne（洛桑大学）；Technical University of Munich（慕尼黑工业大学）；Brain Research Institute, University of Zurich（苏黎世大学脑研究所）；University of Debrecen（德布勒森大学）
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；King's College London（伦敦国王学院）；TECNALIA, Basque Research and Technology Alliance (BRTA)（TECNALIA，巴斯克研究与技术联盟（BRTA））；University of the Basque Country UPV/EHU（巴斯克大学 UPV/EHU）
