Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2501.01640v1
• Date: Fri, 03 Jan 2025 05:18:38 GMT
• Title: Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation
• Authors: Rini Smita Thakur, Vinod K. Kurmi,
• Abstract summary: Semi-supervised (SS) semantic segmentation exploits both labeled and unlabeled images to overcome tedious and costly pixel-level annotation problems. This work uses aleatoric or data uncertainty and energy based modeling in intersection-union pseudo supervised network.
• Score: 1.5269945475810085
• License:
• 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.

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