@inproceedings{14219,
  abstract     = {In this paper, we show that recent advances in self-supervised feature
learning enable unsupervised object discovery and semantic segmentation with a
performance that matches the state of the field on supervised semantic
segmentation 10 years ago. We propose a methodology based on unsupervised
saliency masks and self-supervised feature clustering to kickstart object
discovery followed by training a semantic segmentation network on pseudo-labels
to bootstrap the system on images with multiple objects. We present results on
PASCAL VOC that go far beyond the current state of the art (50.0 mIoU), and we
report for the first time results on MS COCO for the whole set of 81 classes:
our method discovers 34 categories with more than $20\%$ IoU, while obtaining
an average IoU of 19.6 for all 81 categories.},
  author       = {Zadaianchuk, Andrii and Kleindessner, Matthaeus and Zhu, Yi and Locatello, Francesco and Brox, Thomas},
  booktitle    = {The 11th International Conference on Learning Representations},
  location     = {Kigali, Rwanda},
  title        = {{Unsupervised semantic segmentation with self-supervised object-centric representations}},
  year         = {2023},
}