You Get What You Focus on: A Weighting Factor for IoU-based Regression Loss

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Loss functions are essential to bounding box regression which plays a significant role in deep learning based object detection. Despite the effectiveness of the popular Intersection over Union (IoU) based losses, there is still an imbalance problem of high- and low-quality predicted bounding boxes, impeding the accuracy and convergence speed during bound box regression. Specifically, we observe that the huge amount of predicted bounding boxes having small overlapping regions with ground truth box overwhelms the amount of predicted bounding boxes having large overlapping regions. In this paper, we propose a simple weighting factor that is able to reshape the existing IoU-based losses according to a geometric relationship of bounding boxes. In this way, we are able to effectively down-weight the contribution of low-quality predicted boxes and focus training on high-quality ones. Extensive experiments have been carried out on popular IoU-based losses with various object detection techniques. By simply incorporating the proposed weighting factor, we are able to achieve notable performance gains on the popular MS COCO dataset.

Original languageEnglish
Title of host publicationIJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738133669
DOIs
Publication statusPublished - 18 Jul 2021
Event2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
Duration: 18 Jul 202122 Jul 2021

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2021-July

Conference

Conference2021 International Joint Conference on Neural Networks, IJCNN 2021
Country/TerritoryChina
CityVirtual, Shenzhen
Period18/07/2122/07/21

Keywords

  • IoU loss
  • bounding box regression
  • object detection

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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