Point annotations are considerably more time-efficient than bounding box annotations. However, how to use cheap point annotations to boost the performance of semi-supervised object detection remains largely unsolved. In this work, we present Point-Teaching, a weakly semi-supervised object detection framework to fully exploit the point annotations. Specifically, we propose a Hungarian-based point matching method to generate pseudo labels for point annotated images. We further propose multiple instance learning (MIL) approaches at the level of images and points to supervise the object detector with point annotations. Finally, we propose a simple-yet-effective data augmentation, termed point-guided copy-paste, to reduce the impact of the unmatched points. Experiments demonstrate the effectiveness of our method on a few datasets and various data regimes.
翻译:点说明比捆绑框说明更具有时间效率。然而,如何使用低价点说明来提高半受监督物体探测的性能基本上仍未解决。在此工作中,我们介绍了点教学,这是一个微弱的半受监督物体探测框架,以充分利用点说明。具体地说,我们提议了一种基于匈牙利的点匹配方法,为点附加说明的图像制作假标签。我们进一步提议了图像和点一级的多例学习(MIL)方法,以监督点说明的物体探测器。最后,我们提议了一种简单而有效的数据增强,称为点制版拷贝,以减小未匹配点的影响。实验表明我们方法对少数数据集和各种数据制度的有效性。