arXiv 2112.075282021-12cs.CVcs.AIcs.LG

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

AI Clearing, Inc.（AI清算公司）；Semantic Technology Institute, Department of Computer Science, University of Innsbruck（因斯布鲁克大学语义技术研究所）；Institute of Informatics, University of Warsaw（华沙大学信息学研究所）

Dominik Filipiak, Piotr Tempczyk, Marek Cygan

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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 CutMix 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).

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