Interpolation-based Semi-supervised Learning for Object Detection

Jisoo Jeong, Vikas Verma, Minsung Hyun, Juho Kannala, Nojun Kwak

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

41 Citations (Scopus)


Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much. In this paper, we propose an Interpolation-based Semi-supervised learning method for object Detection (ISD), which considers and solves the problems caused by applying conventional Interpolation Regularization (IR) directly to object detection. We divide the output of the model into two types according to the objectness scores of both original patches that are mixed in IR. Then, we apply a separate loss suitable for each type in an unsupervised manner. The proposed losses dramatically improve the performance of semi-supervised learning as well as supervised learning. In the supervised learning setting, our method improves the baseline methods by a significant margin. In the semi-supervised learning setting, our algorithm improves the performance on a benchmark dataset (PASCAL VOC and MSCOCO) in a benchmark architecture (SSD). Our code is available at
Original languageEnglish
Title of host publicationProceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
Number of pages10
ISBN (Electronic)978-1-6654-4509-2
ISBN (Print)978-1-6654-4510-8
Publication statusPublished - 13 Nov 2021
MoE publication typeA4 Conference publication
EventIEEE Conference on Computer Vision and Pattern Recognition - Virtual, online, Nashville, United States
Duration: 19 Jun 202125 Jun 2021

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Electronic)2575-7075


ConferenceIEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR
Country/TerritoryUnited States
Internet address


  • Interpolation
  • Supervised learning
  • Object detection
  • Detectors
  • Computer architecture
  • Semisupervised learning
  • Benchmark testing


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