Neurosymbolic Approach Improves Weakly Supervised Image Segmentation Using Fuzzy Logic
Researchers developed a neurosymbolic method that combines differentiable fuzzy logic with foundation models like SAM to improve weakly supervised semantic segmentation from partial annotations. The approach integrates weak annotations and domain-specific priors as continuous logical constraints to refine pseudo-labels. The method achieves state-of-the-art results on standard benchmarks, sometimes exceeding fully supervised baselines.
A new computer vision technique addresses limitations in weakly supervised semantic segmentation by integrating fuzzy logic with deep learning models. Rather than relying on heuristic prompt choices, the method treats weak annotations and domain knowledge as continuous logical constraints that fine-tune the Segment Anything Model (SAM) under weak supervision. The refined foundation model then generates improved pseudo-labels, which train a second-stage segmentation model without requiring prompts. Experiments on Pascal VOC 2012 and the REFUGE2 optic disc/cup segmentation dataset demonstrate that logic-guided fine-tuning produces higher-quality pseudo-labels and achieves state-of-the-art accuracy. This neurosymbolic approach offers a principled way to incorporate heterogeneous labels and prior knowledge into foundation model adaptation.
What's missing
The paper does not discuss computational cost or inference time compared to baseline methods, nor does it address potential limitations of the fuzzy logic formulation for other segmentation domains beyond the tested datasets.
What different sources said
- arXiv cs.AICenter
Weakly Supervised Segmentation as Semantic-Based Regularization
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