论文标题
对图像量表和方向的自我监督学习
Self-Supervised Learning of Image Scale and Orientation
论文作者
论文摘要
我们研究了为图像所吸引的图像区域分配特征姿势（即规模和方向）的学习问题。尽管它显然很简单，但问题是不平凡的。很难获得具有模型直接从中学习的明确姿势注释的大规模图像区域。为了解决这个问题，我们通过直方图对准技术提出了一个自制的学习框架。它通过随机重新缩放/旋转来生成成对的图像贴片，然后训练估算器以预测其比例/方向值，以使它们的相对差异与所使用的重新分组/旋转相一致。估算器学会了预测规模/方向的非参数直方图分布，而无需任何监督。实验表明，它在规模/方向估计上显着优于先前的方法，还可以通过将我们的斑块姿势纳入匹配过程中来改善图像匹配和6个DOF相机姿势估计。
We study the problem of learning to assign a characteristic pose, i.e., scale and orientation, for an image region of interest. Despite its apparent simplicity, the problem is non-trivial; it is hard to obtain a large-scale set of image regions with explicit pose annotations that a model directly learns from. To tackle the issue, we propose a self-supervised learning framework with a histogram alignment technique. It generates pairs of image patches by random rescaling/rotating and then train an estimator to predict their scale/orientation values so that their relative difference is consistent with the rescaling/rotating used. The estimator learns to predict a non-parametric histogram distribution of scale/orientation without any supervision. Experiments show that it significantly outperforms previous methods in scale/orientation estimation and also improves image matching and 6 DoF camera pose estimation by incorporating our patch poses into a matching process.
