arXiv:2605.13674v2 Announce Type: replace-cross 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 d
The proliferation of foundation models like SAM has highlighted the limitations of current weakly supervised segmentation approaches, creating a need for more robust methods to integrate diverse annotations and prior knowledge.
This research addresses a critical bottleneck in AI development, enabling more efficient training of highly accurate segmentation models with less reliance on costly, meticulously labeled datasets.
The neurosymbolic approach, combining differentiable fuzzy logic with foundation models, offers a pathway to more flexible and knowledge-infused weakly supervised segmentation, potentially accelerating AI development cycles.
- · AI developers
- · Computer vision companies
- · Robotics
- · Healthcare AI
- · Manual data annotation services
- · AI companies reliant on purely data-driven segmentation models
More accurate and data-efficient semantic segmentation models become widely available.
This could lead to faster deployment of AI systems in real-world applications requiring precise object recognition and scene understanding.
Reduced data annotation costs might democratize advanced AI capabilities, fostering innovation in sectors currently limited by labeling expenses.
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Read at arXiv cs.AI