Unsupervised Learning of Foreground Object Segmentation

@article{Croitoru2019UnsupervisedLO,
  title={Unsupervised Learning of Foreground Object Segmentation},
  author={Ioana Croitoru and Simion-Vlad Bogolin and Marius Leordeanu},
  journal={International Journal of Computer Vision},
  year={2019},
  pages={1-24}
}
Unsupervised learning represents one of the most interesting challenges in computer vision today. The task has an immense practical value with many applications in artificial intelligence and emerging technologies, as large quantities of unlabeled images and videos can be collected at low cost. In this paper, we address the unsupervised learning problem in the context of segmenting the main foreground objects in single images. We propose an unsupervised learning system, which has two pathways… CONTINUE READING
1
Twitter Mention

Citations

Publications citing this paper.

Self-supervised Training of Proposal-based Segmentation via Background Prediction

VIEW 4 EXCERPTS
CITES BACKGROUND, RESULTS & METHODS
HIGHLY INFLUENCED

References

Publications referenced by this paper.
SHOWING 1-10 OF 81 REFERENCES

ImageNet Large Scale Visual Recognition Challenge

  • International Journal of Computer Vision
  • 2014
VIEW 18 EXCERPTS
HIGHLY INFLUENTIAL

Learning object class detectors from weakly annotated video

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition
  • 2012
VIEW 18 EXCERPTS
HIGHLY INFLUENTIAL

YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Learning Features by Watching Objects Move

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Unsupervised Joint Object Discovery and Segmentation in Internet Images

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition
  • 2013
VIEW 11 EXCERPTS
HIGHLY INFLUENTIAL

Network Dissection: Quantifying Interpretability of Deep Visual Representations

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL