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机译:通过最佳BoW进行实例注释以实现弱监督对象定位
College of Internet of Things Engineering, Hohai University, Changzhou, China;
Institute for Information and System Sciences and Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, China;
School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia;
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China;
Samsung Research Center, Beijing, China;
Videos; Image segmentation; Birds; Context; Optimization; Search problems; Labeling;
机译:基于单实例注释的弱监督对象检测的渐进学习框架
机译:具有多重折叠多实例学习的弱监督对象定位
机译:具有多实例学习和包拆分功能的弱监督大型对象本地化
机译:学习注释一致的实例弱监督实例分割
机译:用于生成上下文信息的实例分段标签的弱监督框架
机译:使用光学相干断层扫描图像对年龄相关的黄斑变性进行弱监督病变定位
机译:多重监督多实例学习的弱监督对象定位
机译:弱监督的判别本地化和分类:联合学习过程