FARCLUSS: fuzzy adaptive rebalancing and contrastive uncertainty learning for semi-supervised semantic segmentation

Semi-supervised semantic segmentation (SSSS) faces persistent challenges in effectively leveraging unlabeled data, including ineffective utilization of pseudo-labels, the exacerbation of class imbalance biases, and the neglect of prediction uncertainty. Moreover, current approaches often discard uncertain regions through strict thresholding, thereby 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 modulates pixel-wise contributions via entropy-based reliability scores; (3) adaptive class rebalancing, which dynamically adjusts the loss to counteract long-tailed class distributions; and (4) lightweight contrastive regularization, that encourages compact and discriminative feature embeddings. Extensive experiments on Pascal VOC and Cityscapes demonstrate that our method outperforms current state-of-the-art approaches, with pronounced gains on under-represented classes and ambiguous boundary regions.