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.
We observed that the model is unable to localize class ‘road’ in any of the images, and wrongly predicts it as bus (see Fig
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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.