n-CPS

n-CPS: Generalising Cross Pseudo Supervision to n networks for Semi-Supervised Semantic Segmentation

by Dominik Filipiak^1,2, Piotr Tempczyk^1,3, Marek Cygan³.

¹ AI Clearing, Inc.
² Semantic Technology Institute, Department of Computer Science, University of Innsbruck
³ Institute of Informatics, University of Warsaw

We present n-CPS – a generalisation of the recent state-of-the-art cross pseudo supervision (CPS) approach for the task of semi-supervised semantic segmentation. In n-CPS, there are n simultaneously trained subnetworks that learn from each other through one-hot encoding perturbation and consistency regularisation. We also show that ensembling techniques applied to subnetworks outputs can significantly improve the performance. To the best of our knowledge, n-CPS paired with Cut-Mix outperforms CPS and sets the new state-of-the-art for Pascal VOC 2012 with (1/16, 1/8, 1/4, and 1/2 supervised regimes) and Cityscapes (1/16 supervised).

The code in this repository is based mostly on the original CPS repository and NVidia Apex.

Running the code

Please refer to the Installation document.

Citation

Please consider citing this project in your publications if it helps your research.

@misc{filipiak2021ncps,
      title={n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation}, 
      author={Dominik Filipiak and Piotr Tempczyk and Marek Cygan},
      year={2021},
      eprint={2112.07528},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}