On Efficient Variants of Segment Anything Model: A Survey

[2410.04960] On Efficient Variants of Segment Anything Model: A Survey Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2410.04960 (cs) [Submitted on 7 Oct 2024 ( v1 ), last revised 4 Jun 2026 (this version, v6)] Title: On Efficient Variants of Segment Anything Model: A Survey Authors: Xiaorui Sun , Jun Liu , Heng Tao Shen , Xiaofeng Zhu , Ping Hu View a PDF of the paper titled On Efficient Variants of Segment Anything Model: A Survey, by Xiaorui Sun and 4 other authors View PDF HTML (experimental) Abstract: 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. We begin by exploring the motivations driving this research. We then present core techniques used in SAM and model acceleration. This is followed by a detailed exploration of SAM acceleration strategies, categorized by approach, and a discussion of several future research directions. Finally, we offer a unified and extensive evaluation of these methods across various hardware, assessing their efficiency and accuracy on representative benchmarks, and providing a clear comparison of their overall performance. Comments: IJCV Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2410.04960 [cs.CV] (or arXiv:2410.04960v6 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2410.04960 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xiaorui Sun [ view email ] [v1] Mon, 7 Oct 2024 11:59:54 UTC (8,546 KB) [v2] Fri, 18 Oct 2024 14:42:50 UTC (6,117 KB) [v3] Wed, 2 Jul 2025 02:59:38 UTC (4,171 KB) [v4] Wed, 3 Dec 2025 05:20:39 UTC (4,153 KB) [v5] Tue, 14 Apr 2026 03:24:50 UTC (4,154 KB) [v6] Thu, 4 Jun 2026 11:59:03 UTC (6,764 KB) Full-text links: Access Paper: View a PDF of the paper titled On Efficient Variants of Segment Anything Model: A Survey, by Xiaorui Sun and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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