arXiv Machine Learning August 12, 2026 By Ebenezer Tarubinga, Jenifer Kalafatovich, Seong-Whan Lee

FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation

Read the original on arXiv Machine Learning →

arXiv:2506. 11142v3 Announce Type: replace-cross Abstract: Semi-supervised semantic segmentation (SSSS) faces persistent challenges in effectively leveraging unlabeled data, such as ineffective utilization of pseudo-labels, exacerbation of class imbalance biases, and neglect of prediction uncertainty.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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