论文标题
基于KL-Divergence的区域建议网络用于对象检测
KL-Divergence-Based Region Proposal Network for Object Detection
论文作者
论文摘要
使用深神经网络（DNN）在对象检测中学习区域建议的学习分为两个任务：二进制分类和边界框回归任务。但是，传统的RPN（区域提案网络）将这两个任务定义为不同的问题，并且对它们进行了独立培训。在本文中，我们提出了一种新的区域建议学习方法，该方法考虑了边界框偏移框中的不确定性。我们的方法将RPN重新定义为最小化KL差异的问题，即两个概率分布之间的差异。我们将使用KL-Divergence执行区域建议的KL-RPN应用于现有的两阶段对象检测框架，并表明它可以改善现有方法的性能。实验表明，使用VGG-16和RESNET-101骨链的R-CNN中，它在更快的R-CNN中可获得2.6％和2.0％的AP提高。
The learning of the region proposal in object detection using the deep neural networks (DNN) is divided into two tasks: binary classification and bounding box regression task. However, traditional RPN (Region Proposal Network) defines these two tasks as different problems, and they are trained independently. In this paper, we propose a new region proposal learning method that considers the bounding box offset's uncertainty in the objectness score. Our method redefines RPN to a problem of minimizing the KL-divergence, difference between the two probability distributions. We applied KL-RPN, which performs region proposal using KL-Divergence, to the existing two-stage object detection framework and showed that it can improve the performance of the existing method. Experiments show that it achieves 2.6% and 2.0% AP improvements on MS COCO test-dev in Faster R-CNN with VGG-16 and R-FCN with ResNet-101 backbone, respectively.
