论文标题
N-RPN：区域建议网络的辛苦示例学习
N-RPN: Hard Example Learning for Region Proposal Networks
论文作者
论文摘要
区域建议任务是生成一组包含对象的候选区域。在此任务中，最重要的是在固定数量的建议中提出尽可能多的地面真相候选者。然而，与大量易于负面的负面关系相比，在典型的图像中，艰难的否定例子太少了，因此地区提案网络很难训练硬质否定性。由于这个问题，网络倾向于提出艰苦的负面因素作为候选人，而未能提出地面真相的候选者，这导致性能不佳。在本文中，我们提出了一个负面的区域建议网络（NRPN），以改善区域建议网络（RPN）。 NRPN从RPN的误报中学习，并为RPN提供了严重的负面示例。我们提出的NRPN导致假阳性的降低和更好的RPN性能。经过NRPN培训的RPN可以在Pascal VOC 2007数据集上提高性能。
The region proposal task is to generate a set of candidate regions that contain an object. In this task, it is most important to propose as many candidates of ground-truth as possible in a fixed number of proposals. In a typical image, however, there are too few hard negative examples compared to the vast number of easy negatives, so region proposal networks struggle to train on hard negatives. Because of this problem, networks tend to propose hard negatives as candidates, while failing to propose ground-truth candidates, which leads to poor performance. In this paper, we propose a Negative Region Proposal Network(nRPN) to improve Region Proposal Network(RPN). The nRPN learns from the RPN's false positives and provide hard negative examples to the RPN. Our proposed nRPN leads to a reduction in false positives and better RPN performance. An RPN trained with an nRPN achieves performance improvements on the PASCAL VOC 2007 dataset.
