Automatic image annotation (AIA) raises tremendous challenges to machine learning as it requires modeling of data that are both ambiguous in input and output, e.g., images containing multiple objects and labeled with multiple semantic tags. Even more challenging is that the number of candidate tags is usually huge (as large as the vocabulary size) yet each image is only related to a few of them. This paper presents a hybrid generative-discriminative classifier to simultaneously address the extreme data-ambiguity and overfitting-vulnerability issues in tasks such as AIA. Particularly: (1) an Exponential-Multinomial Mixture (EMM) model is established to capture both the input and output ambiguity and in the meanwhile to encourage prediction sparsity; and (2) the prediction ability of the EMM model is explicitly maximized through discriminative learning that integrates variational inference of graphical models and the pairwise formulation of ordinal regression. Experiments show that our approach achieves both superior annotation performance and better tag scalability.
Semi-Automatic Data Annotation guided by Feature Space Projection
Institute of Computing, University of Campinas（坎皮纳斯大学计算研究所）；Department of Information and Computing Sciences, Faculty of Science, Utrecht University（乌得勒支大学理学院信息与计算科学系）
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonance imaging tumor annotations
Department of Health Research（健康研究系）；SINTEF Digital（SINTEF数字）；Department of Physics（物理系）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学（NTNU））；Department of Circulation and Medical Imaging（循环与医学成像系）；St. Olavs hospital（圣奥拉夫医院）；Department of Neuromedicine and Movement Science（神经医学与运动科学系）
Annotation-efficient deep learning for automatic medical image segmentation
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Peng Cheng Laboratory（鹏城实验室）；Pazhou Laboratory（琶洲实验室）；Guangdong General Hospital, Guangdong Academy of Medical Sciences（广东省人民医院（广东省医学科学院））；Renmin Hospital of Wuhan University（武汉大学人民医院）；School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University（北京大学深圳研究生院电子与计算机工程学院）；Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院脑认知与脑疾病研究所）；ETS Montreal（蒙特利尔高等技术学院）
A Distributed Approach towards Discriminative Distance Metric Learning
University of Technology, Sydney（悉尼科技大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；IBM；Microsoft Research Asia（微软亚洲研究院）
A-SelecT: Automatic Timestep Selection for Diffusion Transformer Representation Learning
University of Missouri–Kansas City（密苏里大学堪萨斯城分校）；U. S. Naval Research Laboratory（美国海军研究实验室）；Lamar University（拉马尔大学）；University of Florida（佛罗里达大学）；University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Meta AI；Rochester Institute of Technology（罗切斯特理工学院）
