Supervised ClassMix and a Sup-Unsup Feature Discriminator yield an average 2.07% mIoU gain over standard semi-supervised methods on Chase and COVID-19 datasets.
Semi-supervised semantic segmentation with cross pseudo supervision
2 Pith papers cite this work. Polarity classification is still indexing.
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A multi-teacher collaborative framework with reliability assessment for forward-looking sonar semantic segmentation reports 5.08% mIoU gain on FLSMD dataset using only 2% labeled data.
citing papers explorer
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Accuracy Improvement of Semi-Supervised Segmentation Using Supervised ClassMix and Sup-Unsup Feature Discriminator
Supervised ClassMix and a Sup-Unsup Feature Discriminator yield an average 2.07% mIoU gain over standard semi-supervised methods on Chase and COVID-19 datasets.
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CTFS : Collaborative Teacher Framework for Forward-Looking Sonar Image Semantic Segmentation with Extremely Limited Labels
A multi-teacher collaborative framework with reliability assessment for forward-looking sonar semantic segmentation reports 5.08% mIoU gain on FLSMD dataset using only 2% labeled data.