Weakly supervised learning for object localization based on an attention mechanism

Nojin Park, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Recently, deep learning has been successfully applied to object detection and localization tasks in images. When setting up deep learning frameworks for supervised training with large datasets, strongly labeling the objects facilitates good performance; however, the complexity of the image scene and large size of the dataset make this a laborious task. Hence, it is of paramount importance that the expensive work associated with the tasks involving strong labeling, such as bounding box annotation, is reduced. In this paper, we propose a method to perform object localization tasks without bounding box annotation in the training process by means of employing a two-path activation-map-based classifier framework. In particular, we develop an activation-map-based framework to judicially control the attention map in the perception branch by adding a two-feature extractor so that better attention weights can be distributed to induce improved performance. The experimental results indicate that our method surpasses the performance of the existing deep learning models based on weakly supervised object localization. The experimental results show that the proposed method achieves the best performance, with 75.21% Top-1 classification accuracy and 55.15% Top-1 localization accuracy on the CUB-200-2011 dataset.

Original languageEnglish
Article number10953
JournalApplied Sciences (Switzerland)
Issue number22
Publication statusPublished - 2021 Nov

Bibliographical note

Funding Information:
Funding: This research is supported by the Government-Wide R&D Fund Project for Infectious Disease Research (GFID), Republic of Korea (grant number: HG19C0682).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Attention mechanism
  • Joint training
  • Weakly supervised object localization

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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