Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning

@article{Vezhnevets2010TowardsWS,
  title={Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning},
  author={Alexander Vezhnevets and Joachim M. Buhmann},
  journal={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
  year={2010},
  pages={3249-3256}
}
  • A. Vezhnevets, J. Buhmann
  • Published 13 June 2010
  • Computer Science
  • 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
We address the task of learning a semantic segmentation from weakly supervised data. Our aim is to devise a system that predicts an object label for each pixel by making use of only image level labels during training – the information whether a certain object is present or not in the image. Such coarse tagging of images is faster and easier to obtain as opposed to the tedious task of pixelwise labeling required in state of the art systems. We cast this task naturally as a multiple instance… 

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