On Efficient Variants of Segment Anything Model: A Survey

• URL: http://arxiv.org/abs/2410.04960v2
• Date: Fri, 18 Oct 2024 14:42:50 GMT
• Title: On Efficient Variants of Segment Anything Model: A Survey
• Authors: Xiaorui Sun, Jun Liu, Heng Tao Shen, Xiaofeng Zhu, Ping Hu,
• Abstract summary: The Segment Anything Model (SAM) is a foundational model for image segmentation tasks, known for its strong generalization across diverse applications. 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.
• Score: 63.127753705046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts [17.6980007370549]
  We make the first attempt to adapt Segment Anything Model (SAM) for multi-modal semantic segmentation. By training only the MoE-LoRA layers while keeping SAM's weights frozen, SAM's strong generalization and segmentation capabilities can be preserved for downstream tasks. Specifically, to address cross-modal inconsistencies, we propose a novel MoE routing strategy that adaptively generates weighted features across modalities.
  arXiv  Detail & Related papers  (2024-12-05T14:54:31Z)
• SAM-SP: Self-Prompting Makes SAM Great Again [11.109389094334894]
  Segment Anything Model (SAM) has demonstrated impressive capabilities in zero-shot segmentation tasks. SAM encounters noticeably degradation performance when applied to specific domains, such as medical images. We introduce a novel self-prompting based fine-tuning approach, called SAM-SP, tailored for extending the vanilla SAM model.
  arXiv  Detail & Related papers  (2024-08-22T13:03:05Z)
• Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection [58.241593208031816]
  Segment Anything Model (SAM) has been proposed as a visual fundamental model, which gives strong segmentation and generalization capabilities. We propose a Multi-scale and Detail-enhanced SAM (MDSAM) for Salient Object Detection (SOD) Experimental results demonstrate the superior performance of our model on multiple SOD datasets.
  arXiv  Detail & Related papers  (2024-08-08T09:09:37Z)
• AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning [61.666973416903005]
  Segment Anything Model (SAM) has demonstrated its impressive generalization capabilities in open-world scenarios with the guidance of prompts. We propose a novel framework, termed AlignSAM, designed for automatic prompting for aligning SAM to an open context.
  arXiv  Detail & Related papers  (2024-06-01T16:21:39Z)
• ASAM: Boosting Segment Anything Model with Adversarial Tuning [9.566046692165884]
  This paper introduces ASAM, a novel methodology that amplifies a foundation model's performance through adversarial tuning. We harness the potential of natural adversarial examples, inspired by their successful implementation in natural language processing. Our approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations.
  arXiv  Detail & Related papers  (2024-05-01T00:13:05Z)
• SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes [34.796859088106636]
  Segment anything model (SAM) has demonstrated excellent generalizability in common vision scenarios, yet falling short of the ability to understand specialized data. Recent methods have combined parameter-efficient techniques with task-specific designs to fine-tune SAM on particular tasks. We present a simple and unified framework, namely SU-SAM, that can easily and efficiently fine-tune the SAM model with parameter-efficient techniques.
  arXiv  Detail & Related papers  (2024-01-31T12:53:11Z)
• TinySAM: Pushing the Envelope for Efficient Segment Anything Model [73.06322749886483]
  We propose a framework to obtain a tiny segment anything model (TinySAM) while maintaining the strong zero-shot performance. With all these proposed methods, our TinySAM leads to orders of magnitude computational reduction and pushes the envelope for efficient segment anything task.
  arXiv  Detail & Related papers  (2023-12-21T12:26:11Z)
• Boosting Segment Anything Model Towards Open-Vocabulary Learning [69.24734826209367]
  Segment Anything Model (SAM) has emerged as a new paradigmatic vision foundation model. Despite SAM finding applications and adaptations in various domains, its primary limitation lies in the inability to grasp object semantics. We present Sambor to seamlessly integrate SAM with the open-vocabulary object detector in an end-to-end framework.
  arXiv  Detail & Related papers  (2023-12-06T17:19:00Z)
• A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering [49.732628643634975]
  The Segment Anything Model (SAM), developed by Meta AI Research, offers a robust framework for image and video segmentation. This survey provides a comprehensive exploration of the SAM family, including SAM and SAM 2, highlighting their advancements in granularity and contextual understanding.
  arXiv  Detail & Related papers  (2023-05-12T07:21:59Z)
• An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems [35.547433613976104]
  The Segment Anything Model (SAM) has demonstrated exceptional performance and versatility. This report explores the application of SAM in Weakly-Supervised Semantic (WSSS) We adapt SAM as the pseudo-label generation pipeline given only the image-level class labels.
  arXiv  Detail & Related papers  (2023-05-02T16:35:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.