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 mobile devices. To address this, a variety of SAM variants have been proposed to enhance efficiency without sacrificing accuracy. This survey provides the first comprehensive review of these efficient SAM variants. We begin by exploring the motivations driving this research. We then present core techniques used in SAM and model acceleration. This is followed by an in-depth analysis of various acceleration strategies, categorized by approach. Finally, we offer a unified and extensive evaluation of these methods, assessing their efficiency and accuracy on representative benchmarks, and providing a clear comparison of their overall performance.

本研究针对Segment Anything Model（SAM）在图像分割任务上的高计算和资源需求问题，提出了一种高效的变体综述，以解决在资源有限环境下的部署挑战。通过对各类加速策略的深入分析，本论文提供了对这些高效变体的统一评估，揭示了其效率与准确性的比较，为后续研究提供了重要参考。

高效的任意分割模型变体：综述