Weakly Supervised Deep Detection Networks

Hakan Bilen, Andrea Vedaldi

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

Abstract

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)
Pages2846-2854
Number of pages9
ISBN (Electronic)978-1-4673-8851-1
ISBN (Print)978-1-4673-8852-8
DOIs
Publication statusPublished - 12 Dec 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition - Las Vegas, United States
Duration: 26 Jun 20161 Jul 2016
http://cvpr2016.thecvf.com/

Publication series

Name
PublisherIEEE
ISSN (Electronic)1063-6919

Conference

Conference29th IEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2016
CountryUnited States
CityLas Vegas
Period26/06/161/07/16
Internet address

Keywords

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

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