对于弱监督对象定位的背景意识分类激活地图
IEEE transactions on pattern analysis and machine intelligence
|August 29, 2023
概括
弱监督对象定位 (WSOL) 方法经常与背景噪声作斗争. 本研究引入了背景感知分类激活地图 (B-CAM),通过在训练过程中明确考虑背景特征来提高对象定位的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 弱监督对象本地化 (WSOL) 使用图像级注释,与密集注释相比简化数据要求.
- 现有的WSOL方法经常忽略背景影响,导致过度的背景激活和不准确的本地化.
- 这种限制阻碍了在复杂场景中精确识别对象边界和位置.
研究的目的:
- 提出一种新的机制,即背景识别分类激活地图 (B-CAM),以增强WSOL.
- 在WSOL培训过程中引入背景意识,以减轻背景干扰.
- 为了提高对象本地化的准确性和稳定性,只使用图像级注释.
主要方法:
- 开发了B-CAM,这是一种将背景意识纳入WSOL培训的机制.
- 聚合对象图像级特征用于标准监控.
- 引入了一个额外的背景图像级别功能来表示纯背景样本,提供背景线索来抑制背景激活.
- 训练了一个带有图像级注释的背景分类器,以生成用于二进制定位面具决定的自适应背景分数.
主要成果:
- 证明了提出的B-CAM方法的有效性.
- 在各种WSOL基准中实现了更好的对象本地化性能.
- 成功地抑制了背景激活,导致更精确的对象定位地图.
结论:
- B-CAM机制有效地解决了WSOL中背景干扰的挑战.
- 整合背景意识显著提高了弱监督对象定位的性能.
- 拟议的方法显示了在多个基准数据集 (CUB-200,ILSVRC,OpenImages,VOC2012) 中强大的概括能力.
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