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.

翻译：弱监督语义分割（WSSS）旨在从边界框、涂鸦或图像级标签等部分或粗略标注中训练密集像素级分割模型。尽管近期研究利用Segment Anything Model（SAM）等基础模型生成伪标签，但这些方法通常依赖于启发式提示选择，且整合先验知识或异构标签的方式有限。我们通过神经符号学视角弥补这一不足：将可微模糊逻辑与深度分割模型相结合。弱标注与领域特定先验被统一为连续逻辑约束，通过弱监督微调SAM。优化后的基础模型生成改进的伪标签，进而训练出无需提示的第二阶段分割模型。在Pascal VOC 2012与REFUGE2视盘/视杯分割数据集上的实验表明，逻辑引导的微调能产生更高质量的伪标签，从而取得超越强监督基准线的先进分割精度。