SeSAM adapts SAM for weakly supervised semantic segmentation via mask decomposition, skeleton-based point sampling, coverage-based mask selection, and pseudo-label iteration, outperforming baselines while cutting annotation costs.
Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-shot Medical Segmentation.Diagnostics, 13(11):1947,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Do Instance Priors Help Weakly Supervised Semantic Segmentation?
SeSAM adapts SAM for weakly supervised semantic segmentation via mask decomposition, skeleton-based point sampling, coverage-based mask selection, and pseudo-label iteration, outperforming baselines while cutting annotation costs.