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[bibtex]@InProceedings{Thakur_2025_WACV, author = {Thakur, Rini Smita and Kurmi, Vinod K}, title = {Uncertainty and Energy Based Loss Guided Semi-Supervised Semantic Segmentation}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {8024-8034} }
Uncertainty and Energy Based Loss Guided Semi-Supervised Semantic Segmentation
Abstract
Semi-supervised (SS) semantic segmentation exploits both labeled and unlabeled images to overcome tedious and costly pixel-level annotation problems. Pseudolabel supervision is one of the core approaches of training networks with both pseudo labels and ground-truth labels. This work uses aleatoric or data uncertainty and energy based modeling in intersection-union pseudo supervised network. The aleatoric uncertainty is modeling the inherent noise variations of the data in a network with two predictive branches. The per-pixel variance parameter obtained from the network gives a quantitative idea about the data uncertainty. Moreover energy-based loss realizes the potential of generative modeling on the downstream SS segmentation task. The aleatoric and energy loss are applied in conjunction with pseudo-intersection labels pseudo-union labels and ground-truth on the respective network branch. The comparative analysis with state-of-the-art methods has shown improvement in performance metrics. The code is availaible at https://visdomlab.github.io/DUEB/.
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