Segment objects by expanding high-quality regions with CorrMatch
Segment objects by expanding high-quality regions with CorrMatch
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
arXiv paper abstract https://arxiv.org/abs/2306.04300
arXiv PDF paper https://arxiv.org/pdf/2306.04300.pdf
GitHub https://github.com/BBBBchan/CorrMatch
… present a simple but performant semi-supervised semantic segmentation approach, termed CorrMatch.
… goal is to mine more high-quality regions from the unlabeled images to leverage the unlabeled data more efficiently via consistency regularization.
… introduce an adaptive threshold updating strategy with a relaxed initialization to expand the high-quality regions.
… propose to propagate high-confidence predictions through measuring the pairwise similarities between pixels.
… show that CorrMatch achieves great performance on popular semi-supervised semantic segmentation benchmarks.
… also achieve a consistent improvement over previous semi-supervised semantic segmentation models …
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