Consistency-Regularized Region-Growing Network for Semantic Segmentation
of Urban Scenes with Point-Level Annotations
- URL: http://arxiv.org/abs/2202.03740v1
- Date: Tue, 8 Feb 2022 09:27:01 GMT
- Title: Consistency-Regularized Region-Growing Network for Semantic Segmentation
of Urban Scenes with Point-Level Annotations
- Authors: Yonghao Xu and Pedram Ghamisi
- Abstract summary: We propose a consistency-regularized region-growing network (CRGNet) to reduce the annotation burden.
CRGNet iteratively selects unlabeled pixels with high confidence to expand the annotated area from the original sparse points.
We find such a simple regularization strategy is yet very useful to control the quality of the region-growing mechanism.
- Score: 17.13291434132985
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning algorithms have obtained great success in semantic segmentation
of very high-resolution (VHR) images. Nevertheless, training these models
generally requires a large amount of accurate pixel-wise annotations, which is
very laborious and time-consuming to collect. To reduce the annotation burden,
this paper proposes a consistency-regularized region-growing network (CRGNet)
to achieve semantic segmentation of VHR images with point-level annotations.
The key idea of CRGNet is to iteratively select unlabeled pixels with high
confidence to expand the annotated area from the original sparse points.
However, since there may exist some errors and noises in the expanded
annotations, directly learning from them may mislead the training of the
network. To this end, we further propose the consistency regularization
strategy, where a base classifier and an expanded classifier are employed.
Specifically, the base classifier is supervised by the original sparse
annotations, while the expanded classifier aims to learn from the expanded
annotations generated by the base classifier with the region-growing mechanism.
The consistency regularization is thereby achieved by minimizing the
discrepancy between the predictions from both the base and the expanded
classifiers. We find such a simple regularization strategy is yet very useful
to control the quality of the region-growing mechanism. Extensive experiments
on two benchmark datasets demonstrate that the proposed CRGNet significantly
outperforms the existing state-of-the-art methods. Codes and pre-trained models
will be available online.
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