Semantic segmentation has been continuously investigated in the last ten years, and majority of the established technologies are based on supervised models. In recent years, image-level weakly supervised semantic segmentation (WSSS), including single- and multi-stage process, has attracted large attention due to data labeling efficiency. In this paper, we propose to embed affinity learning of multi-stage approaches in a single-stage model. To be specific, we introduce an adaptive affinity loss to thoroughly learn the local pairwise affinity. As such, a deep neural network is used to deliver comprehensive semantic information in the training phase, whilst improving the performance of the final prediction module. On the other hand, considering the existence of errors in the pseudo labels, we propose a novel label reassign loss to mitigate over-fitting. Extensive experiments are conducted on the PASCAL VOC 2012 dataset to evaluate the effectiveness of our proposed approach that outperforms other standard single-stage methods and achieves comparable performance against several multi-stage methods.
Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation
Academy for Engineering & Technology, Fudan University（复旦大学工程与技术学院）；Digital Medical Research Center, School of Basic Medical Science, Fudan University（复旦大学基础医学院数字医学研究中心）；Shandong Computer Science Center（山东省计算机科学中心）
Inter- and Intra-image Refinement for Few Shot Segmentation
Department of Biomedical Engineering, Peking University（北京大学生物医学工程系）；Institute of Medical Technology, Peking University Health Science Center, Peking University（北京大学医学部医学技术研究所）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）
Robust Seed Mask Generation for Interactive Image Segmentation
Friedrich-Alexander University Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡大学）；Siemens Healthcare GmbH（西门子医疗有限公司）；Erlangen Graduate School in Advanced Optical Technologies (SAOT)（埃尔朗根先进光学技术研究生院）
ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentation
Southern University of Science and Technology（南方科技大学）；University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Hierarchical Temporal Context Learning for Camera-based Semantic Scene Completion
Shanghai Jiao Tong University（上海交通大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（宁波工程学院宁波数字孪生研究院）；The University of Adelaide（阿德莱德大学）；PhiGent Robotics（智臻机器人（北京）有限公司）
MICCAI STSR 2025 Challenge: Semi-Supervised Teeth and Pulp Segmentation and CBCT-IOS Registration
Innovation Center for Electronic Design Automation Technology（电子设计自动化技术创新中心）；School of Cyberspace（网络空间学院）；Hangzhou Dianzi University（杭州电子科技大学）；Hangzhou Geriatric Stomatology Hospital（杭州老年牙科医院）；Shenzhen University（深圳大学）；Queen Mary University of London（伦敦女王大学）；Shandong University（山东大学）；School of Automation and Intelligent Sensing（自动化与智能感知学院）；King’s College London（伦敦国王学院）；College of Information Engineering（信息工程学院）；University of Leicester（莱斯特大学）
SGDFuse: SAM-Guided Diffusion Model for High-Fidelity Infrared and Visible Image Fusion
School of Computer Science（计算机科学学院）；Business University, Yantai, China（烟台商学院）；College of Computer and Data Science（计算机与数据科学学院）；Fuzhou University, Fuzhou, China（福州大学）；School of Computing and Mathematical Sciences（计算与数学科学学院）；University of Leicester, Leicester, United Kingdom（莱斯特大学）；School of Computing and Artificial Intelligence（计算与人工智能学院）；Director of the Rapid-Rich Object Search Laboratory（快速丰富对象搜索实验室主任）；NTU-PKU Joint Research Institute, Nanyang Technological University, Singapore（NTU-PKU联合研究机构，新加坡国立大学）
QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA；Department of Electrical；Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA；Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA；Institute for Informatics, Data Science \& Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
MICCAI STS 2024 Challenge: Semi-Supervised Instance-Level Tooth Segmentation in Panoramic X-ray and CBCT Images
Innovation Center for Electronic Design Automation Technology（电子设计自动化技术创新中心）；School of Cyberspace（网络空间学院）；Hangzhou Dianzi University（杭州电子科技大学）；Hangzhou Geriatric Stomatology Hospital（杭州老年牙科医院）；Shenzhen University（深圳大学）；Queen Mary University of London（伦敦大学Queen Mary分校）；Shandong University（山东大学）；Xidian University（西安电子科技大学）；Shanghai Jiao Tong University（上海交通大学）；Harbin Institute of Technology（哈尔滨工业大学）；The University of Hong Kong（香港大学）；Chinese Academy of Sciences（中国科学院）；Yunnan Provincial Stomatology Hospital（云南省牙科医院）；Shanghai MediWorks Precision Instruments Co., Ltd（上海 MediWorks 精密仪器有限公司）；University of Leicester（莱斯特大学）
