The SAM-PD method explores using SAM for video object segmentation by treating tracking as prompt denoising. It introduces a multi-prompt strategy and point-based refinement to handle challenges like object displacement and occlusions. The approach shows promising results on various datasets.
SAM-PD leverages the denoising capabilities of SAM for video object segmentation tasks without external tracking modules. The method demonstrates effectiveness in handling variations in object position, size, and visibility through innovative strategies like multi-prompting and mask refinement.
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