论文标题
文档图像中对象检测的置信度估算
Confidence Estimation for Object Detection in Document Images
论文作者
论文摘要
深度神经网络变得越来越强大，大大，并且总是需要培训更多标记的数据。但是，由于注释数据是耗时的，因此现在有必要开发出在学习有限数据时显示出良好性能的系统。这些数据必须正确选择以获得仍然有效的模型。为此，系统必须能够确定应注释哪些数据以获得最佳结果。 在本文中，我们提出了四个估计器来估计对象检测预测的信心。前两个是基于蒙特卡洛辍学物，第三个是关于描述性统计的第三个，最后一个是检测器后验概率。在主动学习框架中，与随机选择图像相比，三个第一估计器在检测文档物理页面和文本线的性能方面有显着改善。我们还表明，基于描述性统计的提议估计器可以替代MC辍学，从而降低了计算成本而不会损害性能。
Deep neural networks are becoming increasingly powerful and large and always require more labelled data to be trained. However, since annotating data is time-consuming, it is now necessary to develop systems that show good performance while learning on a limited amount of data. These data must be correctly chosen to obtain models that are still efficient. For this, the systems must be able to determine which data should be annotated to achieve the best results. In this paper, we propose four estimators to estimate the confidence of object detection predictions. The first two are based on Monte Carlo dropout, the third one on descriptive statistics and the last one on the detector posterior probabilities. In the active learning framework, the three first estimators show a significant improvement in performance for the detection of document physical pages and text lines compared to a random selection of images. We also show that the proposed estimator based on descriptive statistics can replace MC dropout, reducing the computational cost without compromising the performances.
