EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning
Fraunhofer IKS（弗劳恩霍夫智能认知系统研究所）；University of Freiburg（弗赖堡大学）；Federal University of Rio Grande（里奥格兰德联邦大学）；University of Tech. Nuremberg（纽伦堡工业大学）；TU München（慕尼黑工业大学）
Monish R. Nallapareddy, Kshitij Sirohi, Paulo L. J. Drews-Jr, Wolfram Burgard, Chih-Hong Cheng, Abhinav Valada
Uncertainty estimation is crucial in safety-critical settings such as automated driving as it provides valuable information for several downstream tasks including high-level decision making and path planning. In this work, we propose EvCenterNet, a novel uncertainty-aware 2D object detection framework using evidential learning to directly estimate both classification and regression uncertainties. To employ evidential learning for object detection, we devise a combination of evidential and focal loss functions for the sparse heatmap inputs. We introduce class-balanced weighting for regression and heatmap prediction to tackle the class imbalance encountered by evidential learning. Moreover, we propose a learning scheme to actively utilize the predicted heatmap uncertainties to improve the detection performance by focusing on the most uncertain points. We train our model on the KITTI dataset and evaluate it on challenging out-of-distribution datasets including BDD100K and nuImages. Our experiments demonstrate that our approach improves the precision and minimizes the execution time loss in relation to the base model.
Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation
Key Laboratory of High Confidence Software Technologies (Peking University)（高可信软件技术重点实验室（北京大学））；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation
Key Laboratory of High Confidence Software Technologies (Peking University)（高可信软件技术重点实验室（北京大学））；School of Computer Science, Peking University（北京大学计算机学院）
FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentation
School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Information Science and Engineering, Wuhan University of Science and Technology（武汉科技大学信息科学与工程学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Mutual Evidential Deep Learning for Medical Image Segmentation
Key Laboratory of High Confidence Software Technologies (MOE) School of Computer Science Peking University（高可信软件技术重点实验室（MOE）计算机科学学院，北京大学）；College of Computer and Information Science School of Software Southwest University（计算机与信息科学学院 软件学院 西南大学）；Department of Computer and Information Science University of Macau（计算机与信息科学系 澳门大学）
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation
Gryphon Center for Artificial Intelligence and Theoretical Sciences, Singapore University of Pennsylvania, Perelman School of Medicine, Department of Radiation Oncology（格雷芬人工智能与理论科学中心，新加坡大学匹兹堡大学，佩尔曼医学学院，放射肿瘤学系）；Georgia Institute of Technology（佐治亚理工学院）；University of Pennsylvania, Perelman School of Medicine, Department of Radiation Oncology（宾夕法尼亚大学，佩尔曼医学学院，放射肿瘤学系）
Uncertainty-Supervised Interpretable and Robust Evidential Segmentation
School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院）；Institute of Science and Technology for Brain-inspired intelligence, Fudan University, Shanghai, China（复旦大学脑启发式智能技术研究所）；Nuffield Department of Population Health, University of Oxford, Oxford, UK（牛津大学人口健康牛津部门）
Medical Image Segmentation with Belief Function Theory and Deep Learning
University of Technology of Compiègne（贡比涅技术大学）；Heudiasyc Laboratory（欧洲计算机科学与自动化研究院）；University of Paris-Saclay（巴黎萨克雷大学）；University of Artois（阿图瓦大学）；ENSMM School（国立高等机械与微技术学院）；University of Rouen（鲁昂大学）
Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty
National Key Laboratory of Fundamental Science on Synthetic Vision, the College of Computer Science, Sichuan University（国家基础科学合成视觉重点实验室、计算机科学学院、四川大学）；Department of Radiology, West China Hospital, Sichuan University（放射科、西昌医院、四川大学）；Saw Swee Hock School of Public Health, National University of Singapore（Saw Swee Hock 公共卫生学院、新加坡国立大学）；Department of Mathematics, Sichuan University（数学系、四川大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所（IHPC）、科学、技术与研究局（A*STAR））；Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore（创新与精准眼健康中心、 Yong Loo Lin 医学院、新加坡国立大学）；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡眼研究所、新加坡国家眼科中心）
Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework
Ford Werke GmbH（福特汽车公司（Ford Werke GmbH））；Institute for Automotive Engineering, RWTH Aachen University（亚琛工业大学汽车工程研究所（Institute for Automotive Engineering, RWTH Aachen University））
Evidential Perfusion Physics-Informed Neural Networks with Residual Uncertainty Quantification
Cancer Biology, Seoul National University College of Medicine, Seoul, Korea（首尔国立大学医学院癌症生物学系）；Radiology, Seoul National University Hospital, Seoul, Korea（首尔国立大学医院放射学）；Bioengineering, Seoul National University, Seoul, Korea（首尔国立大学生物工程系）；Biomedical Sciences, Seoul National University, Seoul, Korea（首尔国立大学生物医学科学）；Radiology, Seoul National University College of Medicine, Seoul, Korea（首尔国立大学医学院放射学）；Healthcare AI Research Institute, Seoul National University Hospital, Seoul, Korea（首尔国立大学医院健康人工智能研究院）
TMF-RSE: Tri-Modal Fusion with Regional Semantics and Evidential Uncertainty for Lung Severity Scoring
Institute of Applied Sciences and Intelligent Systems (ISASI), CNR（应用科学与智能系统研究所（ISASI），CNR）；Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU)（巴斯克国家大学计算机科学与人工智能系）；University of Salento（萨莱nton大学）
DEFNet: Multitasks-based Deep Evidential Fusion Network for Blind Image Quality Assessment
Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education（高可信软件技术重点实验室（北京大学）教育部）；School of Computer Science, Peking University（计算机学院（北京大学））
DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
National University of Singapore；National University of Singapore Guangzhou Research Translation；Innovation Institute；Imperial College London；Peking Union Medical College Hospital, Chinese Academy of Medical Sciences \& Peking Union Medical College；Hunan University；Institute of High Performance Computing, Agency for Science, Technology；Research (A STAR)
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；US Army Research Laboratory（美国陆军研究实验室）；University of Oslo（奥斯陆大学）；Virginia Tech（弗吉尼亚理工大学）；University of the District of Columbia（哥伦比亚特区大学）
