Feature request: Semantic audit module for mask coherence #846
Description
I propose a semantic audit module for Segment Anything that evaluates the conceptual coherence of generated masks.
This could improve interpretability and alignment in downstream tasks.
Motivation:
While Segment Anything excels at zero-shot segmentation, it currently lacks a semantic introspection layer.
A reflection module — using embeddings and conceptual memory — could help detect incoherent or misaligned segmentations.
Proposed Implementation:
- Embed the generated mask or prompt
- Compare with a conceptual memory index
- Trigger revision or flagging if semantic misalignment is detected
Inspired by https://github.com/elly99-AI/MarCognity-AI.git, which explores reflective architectures and semantic checkpoints.