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

• URL: http://arxiv.org/abs/2506.11142v2
• Date: Mon, 23 Jun 2025 08:58:30 GMT
• Title: FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic Segmentation
• Authors: Ebenezer Tarubinga, Jenifer Kalafatovich, Seong-Whan Lee,
• Abstract summary: Semi-supervised semantic segmentation (SSSS) faces persistent challenges in effectively leveraging unlabeled data.<n>Current approaches often discard uncertain regions through strict thresholding favouring dominant classes.<n>We introduce a holistic framework that transforms uncertainty into a learning asset through four principal components.
• Score: 26.585985828583304
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Current approaches often discard uncertain regions through strict thresholding favouring dominant classes. To address these limitations, we introduce a holistic framework that transforms uncertainty into a learning asset through four principal components: (1) fuzzy pseudo-labeling, which preserves soft class distributions from top-K predictions to enrich supervision; (2) uncertainty-aware dynamic weighting, that modulate pixel-wise contributions via entropy-based reliability scores; (3) adaptive class rebalancing, which dynamically adjust losses to counteract long-tailed class distributions; and (4) lightweight contrastive regularization, that encourage compact and discriminative feature embeddings. Extensive experiments on benchmarks demonstrate that our method outperforms current state-of-the-art approaches, achieving significant improvements in the segmentation of under-represented classes and ambiguous regions.

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