Weakly Supervised Deep Detection Networks

Hakan Bilen, Andrea Vedaldi

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


Weakly supervised learning of object detection is an important problem in image understanding that still does not have a satisfactory solution. In this paper, we address this problem by exploiting the power of deep convolutional neural networks pre-trained on large-scale image-level classification tasks. We propose a weakly supervised deep detection architecture that modifies one such network to operate at the level of image regions, performing simultaneously region selection and classification. Trained as an image classifier, the architecture implicitly learns object detectors that are better than alternative weakly supervised detection systems on the PASCAL VOC data. The model, which is a simple and elegant end-to-end architecture, outperforms standard data augmentation and fine-tuning techniques for the task of image-level classification as well.
Original languageEnglish
Title of host publication2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)978-1-4673-8851-1
ISBN (Print)978-1-4673-8852-8
Publication statusPublished - 12 Dec 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition - Las Vegas, United States
Duration: 26 Jun 20161 Jul 2016

Publication series

ISSN (Electronic)1063-6919


Conference29th IEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2016
CountryUnited States
CityLas Vegas
Internet address


  • Computer Vision
  • Deep Learning
  • object detection
  • weakly supervised learning

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