Semi-Supervised Semantic Segmentation With Region Relevance
- URL: http://arxiv.org/abs/2304.11539v1
- Date: Sun, 23 Apr 2023 04:51:27 GMT
- Title: Semi-Supervised Semantic Segmentation With Region Relevance
- Authors: Rui Chen, Tao Chen, Qiong Wang, Yazhou Yao
- Abstract summary: Semi-supervised semantic segmentation aims to learn from a small amount of labeled data and plenty of unlabeled ones.
The most common approach is to generate pseudo-labels for unlabeled images to augment the training data.
This paper proposes a Region Relevance Network (RRN) to alleviate the problem mentioned above.
- Score: 28.92449538610617
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semi-supervised semantic segmentation aims to learn from a small amount of
labeled data and plenty of unlabeled ones for the segmentation task. The most
common approach is to generate pseudo-labels for unlabeled images to augment
the training data. However, the noisy pseudo-labels will lead to cumulative
classification errors and aggravate the local inconsistency in prediction. This
paper proposes a Region Relevance Network (RRN) to alleviate the problem
mentioned above. Specifically, we first introduce a local pseudo-label
filtering module that leverages discriminator networks to assess the accuracy
of the pseudo-label at the region level. A local selection loss is proposed to
mitigate the negative impact of wrong pseudo-labels in consistency
regularization training. In addition, we propose a dynamic region-loss
correction module, which takes the merit of network diversity to further rate
the reliability of pseudo-labels and correct the convergence direction of the
segmentation network with a dynamic region loss. Extensive experiments are
conducted on PASCAL VOC 2012 and Cityscapes datasets with varying amounts of
labeled data, demonstrating that our proposed approach achieves
state-of-the-art performance compared to current counterparts.
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