AI RESEARCH
On Efficient Variants of Segment Anything Model: A Survey
arXiv CS.CV
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ArXi:2410.04960v5 Announce Type: replace The Segment Anything Model (SAM) is a foundational model for image segmentation tasks, known for its strong generalization across diverse applications. However, its impressive performance comes with significant computational and resource demands, making it challenging to deploy in resource-limited environments such as edge devices. To address this, a variety of SAM variants have been proposed to enhance efficiency while keeping accuracy. This survey provides the first comprehensive review of these efficient SAM variants.