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.
Chuanfei Hu, Tianyi Xia, Shenghong Ju, and Xinde Li
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A survey that reviews efficient variants of the Segment Anything Model, categorizes acceleration strategies, and provides a unified hardware evaluation on benchmarks.
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.
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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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On Efficient Variants of Segment Anything Model: A Survey
A survey that reviews efficient variants of the Segment Anything Model, categorizes acceleration strategies, and provides a unified hardware evaluation on benchmarks.
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Data-Centric Foundation Models in Computational Healthcare: A Survey
The paper surveys data-centric strategies for foundation models in computational healthcare and supplies a curated list of related models and datasets.