N-RPN: Hard Example Learning for Region Proposal Networks

Myeongah Cho, Tae Young Chung, Hyeongmin Lee, Sangyoun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538662496
Publication statusPublished - 2019 Sept
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: 2019 Sept 222019 Sept 25

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference26th IEEE International Conference on Image Processing, ICIP 2019
Country/TerritoryTaiwan, Province of China

Bibliographical note

Funding Information:
This work was supported by Institute of Information & communications TechnologyPlanning & Evaluation (IITP) grant funded by the Korea government(MSIT) (2016-0-00197, Development of the high-precision natural 3D view generation technology using smart-car multi sensors and deep learning)

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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