不确定性意识主动域自适应突出对象检测
概括
本研究介绍了一种具有成本效益的突出物体检测 (SOD) 框架,该框架使用主动学习将模型从合成数据调整为现实数据. 它显著降低了注释成本,同时实现了与完全监督的方法可比的性能.
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:
- 深度学习显著提升了突出物体检测 (SOD).
- 目前的深度学习 SOD 方法需要广泛的像素智能注释,这造成了数据注释负担.
- 与完全监督的方法相比,现有的弱监督和无监督的SOD方法显示出性能差距.
研究的目的:
- 提出一个新的,成本效益高的突出物体检测 (SOD) 框架.
- 通过使用有限的,主动选择的注释,将SOD模型从合成数据调整为现实数据.
- 为了弥合弱监督/无监督SOD和完全监督的方法之间的性能差距.
主要方法:
- 通过复制和粘贴前景对象到背景上构建了一个合成的SOD数据集.
- 开发了一个不确定性意识的主动域适应算法,用于现实世界的数据标签.
- 利用对数据增强的预测差异来计算超像素级别的不确定性.
- 为低不确定性超像素和手动标记高不确定性超像素生成伪标签.
主要成果:
- 拟议的框架有效地将模型从合成转移到现实世界SOD数据集.
- 活跃域名适应策略产生高质量的标签,注释成本最小.
- 六个基准SOD数据集的实验结果显示,相对于现有的弱监督和无监督方法,性能优越.
- 实现了与完全监督的SOD方法可比的性能.
结论:
- 提出的成本效益框架成功地解决了基于深度学习的SOD的注释负担.
- 积极域调整与不确定性估计是一个可行的策略,以改善SOD性能与有限的注释.
- 该方法提供了一个实际的解决方案,可以在没有广泛的手动标签的情况下在现实场景中实现高性能SOD.
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