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Found 3 papers, 1 papers with code
Active Label Correction for Semantic Segmentation with Foundation Models
Training and validating models for semantic segmentation require datasets with pixel-wise annotations, which are notoriously labor-intensive.
Adaptive Superpixel for Active Learning in Semantic Segmentation
Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive.
Robust Deep Learning from Crowds with Belief Propagation
Crowdsourcing systems enable us to collect large-scale dataset, but inherently suffer from noisy labels of low-paid workers.
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