Re-distributing Biased Pseudo Labels for Semi-supervised Semantic
Segmentation: A Baseline Investigation
- URL: http://arxiv.org/abs/2107.11279v2
- Date: Mon, 26 Jul 2021 06:11:54 GMT
- Title: Re-distributing Biased Pseudo Labels for Semi-supervised Semantic
Segmentation: A Baseline Investigation
- Authors: Ruifei He, Jihan Yang, Xiaojuan Qi
- Abstract summary: We present a simple and yet effective Distribution Alignment and Random Sampling (DARS) method to produce unbiased pseudo labels.
Our method performs favorably in comparison with state-of-the-art approaches.
- Score: 30.688753736660725
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While self-training has advanced semi-supervised semantic segmentation, it
severely suffers from the long-tailed class distribution on real-world semantic
segmentation datasets that make the pseudo-labeled data bias toward majority
classes. In this paper, we present a simple and yet effective Distribution
Alignment and Random Sampling (DARS) method to produce unbiased pseudo labels
that match the true class distribution estimated from the labeled data.
Besides, we also contribute a progressive data augmentation and labeling
strategy to facilitate model training with pseudo-labeled data. Experiments on
both Cityscapes and PASCAL VOC 2012 datasets demonstrate the effectiveness of
our approach. Albeit simple, our method performs favorably in comparison with
state-of-the-art approaches. Code will be available at
https://github.com/CVMI-Lab/DARS.
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