mlsanityData to 7 Oct 2026

A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering

arXiv 2306.06211 ↗First posted 12 May 2023cs.CVcited by 45

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Abstract (arXiv)

The Segment Anything Model (SAM), developed by Meta AI Research, represents a significant breakthrough in computer vision, offering 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. Our study demonstrates SAM's versatility across a wide range of applications while identifying areas where improvements are needed, particularly in scenarios requiring high granularity and in the absence of explicit prompts. By mapping the evolution and capabilities of SAM models, we offer insights into their strengths and limitations and suggest future research directions, including domain-specific adaptations and enhanced memory and propagation mechanisms. We believe that this survey comprehensively covers the breadth of SAM's applications and challenges, setting the stage for ongoing advancements in segmentation technology.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Builds on 90 of its 307 references that are in the map. First posted on arXiv 12 May 2023.

Builds on

90 of 307 references.

References · 90

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  7. 2023Segment Anythingcited by 8,158
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38 more
  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  2. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  4. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,848
  5. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
  6. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
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  8. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  9. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  10. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,148
  11. 2016Adversarial Machine Learning at Scalecited by 934
  12. 2021Florence: A New Foundation Model for Computer Visioncited by 802
  13. 2018Panoptic Segmentationcited by 796
  14. 2022Open-Vocabulary Semantic Segmentation with Mask-adapted CLIPcited by 560
  15. 2023Segment Everything Everywhere All at Oncecited by 546
  16. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 485
  17. 2017Look, Listen and Learncited by 452
  18. 2022XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Modelcited by 436
  19. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 397
  20. 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291
  21. 2023Customized Segment Anything Model for Medical Image Segmentationcited by 277
  22. 2023Personalize Segment Anything Model with One Shotcited by 250
  23. 2023Inpaint Anything: Segment Anything Meets Image Inpaintingcited by 233
  24. 2023Track Anything: Segment Anything Meets Videoscited by 230
  25. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 221
  26. 2023Segment and Track Anythingcited by 193
  27. 2023Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imagingcited by 161
  28. 2022Audio-Visual Segmentationcited by 148
  29. 2022MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectorscited by 148
  30. 2022Decoupling Features in Hierarchical Propagation for Video Object Segmentationcited by 146
  31. 2023Robo3D: Towards Robust and Reliable 3D Perception against Corruptionscited by 128
  32. 2023Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applicationscited by 121
  33. 2023Can SAM Segment Anything? When SAM Meets Camouflaged Object Detectioncited by 99
  34. 2023Caption Anything: Interactive Image Description with Diverse Multimodal Controlscited by 98
  35. 2023InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Languagecited by 96
  36. 2023Anything-3D: Towards Single-view Anything Reconstruction in the Wildcited by 91
  37. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  38. 2023SAM on Medical Images: A Comprehensive Study on Three Prompt Modescited by 81

50 most cited of 90 All 90 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 19; 2024: 11; 2025: 12; 2026: 3.

45 papers.

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  1. 2023Faster Segment Anything: Towards Lightweight SAM for Mobile Applicationscited by 345
  2. 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
  3. 2023SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimationcited by 115
  4. 2023Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysiscited by 99
  5. 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
  6. 2024Visual Prompting in Multimodal Large Language Models: A Surveycited by 52
  7. 2024ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentationcited by 49
  8. 2023MobileSAMv2: Faster Segment Anything to Everythingcited by 41
  9. 2023Robustness of SAM: Segment Anything Under Corruptions and Beyondcited by 30
  10. 2024GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splattingcited by 25
  11. 2023RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentationcited by 24
  12. 2023A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Applicationcited by 21
33 more
  1. 2023Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learningcited by 18
  2. 2024On Efficient Variants of Segment Anything Model: A Surveycited by 16
  3. 2023Black-box Targeted Adversarial Attack on Segment Anything (SAM)cited by 13
  4. 2025SAM2 for Image and Video Segmentation: A Comprehensive Surveycited by 13
  5. 2023SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentationcited by 12
  6. 2023Understanding Segment Anything Model: SAM is Biased Towards Texture Rather than Shapecited by 10
  7. 2023Segment Anything Meets Semantic Communicationcited by 8
  8. 2025WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM with Sub-Class Exploration and Prompt Affinity Miningcited by 8
  9. 2025AoP-SAM: Automation of Prompts for Efficient Segmentationcited by 7
  10. 2024Curriculum Prompting Foundation Models for Medical Image Segmentationcited by 6
  11. 2023Leveraging Foundation models for Unsupervised Audio-Visual Segmentationcited by 5
  12. 2024Segment Anything for Videos: A Systematic Surveycited by 5
  13. 2023Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaulcited by 4
  14. 2023Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challengescited by 4
  15. 2023CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentationcited by 4
  16. 2023SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbationcited by 4
  17. 2024A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligencecited by 3
  18. 2025Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instructioncited by 3
  19. 2025Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challengescited by 3
  20. 2024Urban Waterlogging Detection: A Challenging Benchmark and Large-Small Model Co-Adaptercited by 1
  21. 2024Leveraging Segment Anything Model in Identifying Buildings within Refugee Camps (SAM4Refugee) from Satellite Imagery for Humanitarian Operationscited by 1
  22. 2025SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Promptingcited by 1
  23. 2025SCOPE: Speech-guided COllaborative PErception Framework for Surgical Scene Segmentationcited by 1
  24. 2025PatchEAD: Unifying Industrial Visual Prompting Frameworks for Patch-Exclusive Anomaly Detectioncited by 1
  25. 2025Evaluating an Adaptive Multispectral Turret System for Autonomous Tracking Across Variable Illumination Conditionscited by 1
  26. 2026DeepSVU: Towards In-depth Security-oriented Video Understanding via Unified Physical-world Regularized MoEcited by 1
  27. 2026PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentationcited by 1
  28. 2026From Drop-off to Recovery: A Mechanistic Analysis of Segmentation in MLLMscited by 1
  29. 2024Towards In-Vehicle Multi-Task Facial Attribute Recognition: Investigating Synthetic Data and Vision Foundation Modelscited by 0
  30. 2024OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusionscited by 0
  31. 2025Attack for Defense: Adversarial Agents for Point Prompt Optimization Empowering Segment Anything Modelcited by 0
  32. 2025AD-SAM: Adapting the Segment Anything Model for Semantic Segmentation in Autonomous Drivingcited by 0
  33. 2025Vision-Language Enhanced Foundation Model for Semi-Supervised Medical Image Segmentationcited by 0