对象亲和学习:朝着没有注释的实例分割

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概括

本研究介绍了对象亲和学习,用于无注释的实例细分,使用几何线索来克服基于外观的方法的局限性. 该方法在复杂场景中显著提高了细分精度.

科学领域:

计算机视觉计算机视觉
机器学习机器学习

背景情况:

实例细分需要昂贵的手动口罩注释.
使用外观线索的现有方法与复杂的场景和背景模糊性作斗争.

研究的目的:

开发一种没有注释的实例细分方法,克服基于外观的方法的局限性.
为了利用几何线索 (空间连续性,运动一致性) 来改进对象分割.

主要方法:

提出一种基于亲和的范式:对象亲和学习.
对象亲和学习作为代理任务来确定像素是否属于同一对象.
用几何线索来学习特征表示.
通过图形分区算法将学习的对象亲和度转换为实例分区面具.

主要成果:

对象亲和学习显著优于现有的基于伪面具的方法.
在像Waymo开放数据集和KITTI这样的大规模数据集上表现出卓越的性能.
在复杂的现实场景中成功解决了无注释实例细分的挑战.

结论:

几何线索对于强大的没有注释的实例细分至关重要.
拟议的对象亲和学习范式提供了一个比基于外观的方法更有效的方法.
这项工作减少了对手工注释的依赖,例如细分任务.

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