From SAM to SAM 2: Exploring Improvements in Meta's Segment Anything Model
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910563764797440 |
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| author | Geetha, Athulya Sundaresan Hussain, Muhammad |
| author_facet | Geetha, Athulya Sundaresan Hussain, Muhammad |
| contents | The Segment Anything Model (SAM), introduced to the computer vision community by Meta in April 2023, is a groundbreaking tool that allows automated segmentation of objects in images based on prompts such as text, clicks, or bounding boxes. SAM excels in zero-shot performance, segmenting unseen objects without additional training, stimulated by a large dataset of over one billion image masks. SAM 2 expands this functionality to video, leveraging memory from preceding and subsequent frames to generate accurate segmentation across entire videos, enabling near real-time performance. This comparison shows how SAM has evolved to meet the growing need for precise and efficient segmentation in various applications. The study suggests that future advancements in models like SAM will be crucial for improving computer vision technology. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_06305 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From SAM to SAM 2: Exploring Improvements in Meta's Segment Anything Model Geetha, Athulya Sundaresan Hussain, Muhammad Computer Vision and Pattern Recognition The Segment Anything Model (SAM), introduced to the computer vision community by Meta in April 2023, is a groundbreaking tool that allows automated segmentation of objects in images based on prompts such as text, clicks, or bounding boxes. SAM excels in zero-shot performance, segmenting unseen objects without additional training, stimulated by a large dataset of over one billion image masks. SAM 2 expands this functionality to video, leveraging memory from preceding and subsequent frames to generate accurate segmentation across entire videos, enabling near real-time performance. This comparison shows how SAM has evolved to meet the growing need for precise and efficient segmentation in various applications. The study suggests that future advancements in models like SAM will be crucial for improving computer vision technology. |
| title | From SAM to SAM 2: Exploring Improvements in Meta's Segment Anything Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.06305 |