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OBJECT RECOGNITION METHOD AND APPARATUS BASED ON WEAKLY SUPERVISED LEARNING

机译:基于弱监督学习的目标识别方法和装置

摘要

Provided are an object recognition method and apparatus which determine an object of interest included in a recognition target image using a trained machine learning model and determine an area in which the object of interest is located in the recognition target image. The object recognition method based on weakly supervised learning, performed by an object recognition apparatus, includes extracting a plurality of feature maps from a training target image given classification results of objects of interest, generating an activation map for each of the objects of interest by accumulating the feature maps, calculating a representative value of each of the objects of interest by aggregating activation values included in a corresponding activation map, determining an error by comparing classification results determined using the representative value of each of the objects of interest with the given classification results and updating a CNN-based object recognition model by back-propagating the error.
机译:提供了一种对象识别方法和设备,该对象识别方法和设备使用训练有素的机器学习模型来确定识别目标图像中包括的感兴趣对象,并确定识别对象图像中感兴趣对象位于其中的区域。由对象识别装置执行的基于弱监督学习的对象识别方法包括:从训练目标图像中提取给定感兴趣对象的分类结果的多个特征图;通过累加生成每个感兴趣对象的激活图。所述特征图,通过聚合包括在相应激活图中的激活值来计算每个关注对象的代表值,通过将使用每个关注对象的代表值确定的分类结果与给定分类结果进行比较来确定误差并通过反向传播错误来更新基于CNN的对象识别模型。

著录项

  • 公开/公告号US2018144209A1

    专利类型

  • 公开/公告日2018-05-24

    原文格式PDF

  • 申请/专利权人 LUNIT INC.;

    申请/专利号US201615378039

  • 发明设计人 HYO EUN KIM;SANG HEUM HWANG;

    申请日2016-12-14

  • 分类号G06K9/46;G06T7/00;G06N3/08;G06N3/04;G06F19/00;

  • 国家 US

  • 入库时间 2022-08-21 13:03:47

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