Weakly Supervised Segmentation as Semantic-Based Regularization

• URL: http://arxiv.org/abs/2605.13674v1
• Date: Wed, 13 May 2026 15:33:27 GMT
• Title: Weakly Supervised Segmentation as Semantic-Based Regularization
• Abstract summary: Weakly supervised semantic segmentation (WSSS) trains dense pixel-level segmentation models from partial or coarse annotations.<n>We take a neurosymbolic perspective: integrating differentiable fuzzy logic with deep segmentation models.<n>We show that our logic-guided fine-tuning yields higher-quality pseudo-labels, leading to state-of-the-art segmentation.
• Score: 5.8837215684098645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Weakly supervised semantic segmentation (WSSS) trains dense pixel-level segmentation models from partial or coarse annotations such as bounding boxes, scribbles, or image-level tags. While recent work leverages foundation models such as the Segment Anything Model (SAM) to generate pseudo-labels, these approaches typically depend on heuristic prompt choices and offer limited ways to incorporate prior knowledge or heterogeneous labels. We address this gap by taking a neurosymbolic perspective: integrating differentiable fuzzy logic with deep segmentation models. Weak annotations and domain-specific priors are unified as continuous logical constraints that fine-tune SAM under weak supervision. The refined foundation model then produces improved pseudo-labels, from which we train a second-stage prompt-free segmentation model. Experiments on Pascal VOC 2012 and the REFUGE2 optic disc/cup segmentation dataset show that our logic-guided fine-tuning yields higher-quality pseudo-labels, leading to state-of-the-art segmentation accuracy that often exceeds densely supervised baselines.

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