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.
arXiv preprint arXiv:2304.08506 (2023)
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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.
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CellNet -- Localizing Cells using Sparse and Noisy Point Annotations
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