A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914979252273152 |
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| author | Zhang, Chaoning Cho, Joseph Puspitasari, Fachrina Dewi Zheng, Sheng Li, Chenghao Qiao, Yu Kang, Taegoo Shan, Xinru Zhang, Chenshuang Qin, Caiyan Rameau, Francois Lee, Lik-Hang Bae, Sung-Ho Hong, Choong Seon |
| author_facet | Zhang, Chaoning Cho, Joseph Puspitasari, Fachrina Dewi Zheng, Sheng Li, Chenghao Qiao, Yu Kang, Taegoo Shan, Xinru Zhang, Chenshuang Qin, Caiyan Rameau, Francois Lee, Lik-Hang Bae, Sung-Ho Hong, Choong Seon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_06211 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering Zhang, Chaoning Cho, Joseph Puspitasari, Fachrina Dewi Zheng, Sheng Li, Chenghao Qiao, Yu Kang, Taegoo Shan, Xinru Zhang, Chenshuang Qin, Caiyan Rameau, Francois Lee, Lik-Hang Bae, Sung-Ho Hong, Choong Seon Computer Vision and Pattern Recognition 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. |
| title | A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2306.06211 |