属性下降:在内容层面和超越层面上模拟以对象为中心的数据集
IEEE transactions on pattern analysis and machine intelligence
|December 1, 2023
概括
本研究引入了一种属性下降方法,以弥合合成数据中的内容级域差距,使其在计算机视觉任务中更现实. 优化合成数据可以提高图像分类和对象重新识别的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 计算机图形 计算机图形
背景情况:
- 在计算机视觉中,合成数据和现实数据之间存在一个显著的领域差距.
- 这种差距有两个层次:外观 (风格) 和内容 (诸如视角,照明等属性).
- 内容级域差距的研究较少,但对于真实的数据模拟至关重要.
研究的目的:
- 解决合成数据生成中的内容级域差距.
- 提出和验证一种用于优化合成数据属性的自动化方法.
- 增强合成数据的实用性,用于各种计算机视觉应用.
主要方法:
- 建议采用属性下降方法来自动优化引擎属性.
- 该方法侧重于以对象为中心的任务,其中优化信号是明确的.
- 收集了新的合成资产 (VehicleX),并重新使用了现有的资产 (ObjectX, PersonX).
主要成果:
- 属性下降方法有效地减少了内容级域差距.
- 图像分类和对象重新识别的实验显示性能有所改善.
- 适应合成数据在仅训练,数据增强和数据集理解场景中被证明是有效的.
结论:
- 优化合成数据属性是弥合内容级域差距的关键.
- 拟议的方法使合成数据在现实世界的计算机视觉任务中能够有效地使用.
- 这种方法为生成高质量,现实的培训数据提供了可行的解决方案.
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