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From Generalization to Precision: Exploring SAM for Tool Segmentation in Surgical Environments
We argue that SAM drastically over-segment images with high corruption levels, resulting in degraded performance when only a single segmentation mask is considered, while the combination of the masks overlapping the object of interest generates an accurate prediction.
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