SAM 2: Segment Anything in Images and Videos
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| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| author | Ravi, Nikhila Gabeur, Valentin Hu, Yuan-Ting Hu, Ronghang Ryali, Chaitanya Ma, Tengyu Khedr, Haitham Rädle, Roman Rolland, Chloe Gustafson, Laura Mintun, Eric Pan, Junting Alwala, Kalyan Vasudev Carion, Nicolas Wu, Chao-Yuan Girshick, Ross Dollár, Piotr Feichtenhofer, Christoph |
| author_facet | Ravi, Nikhila Gabeur, Valentin Hu, Yuan-Ting Hu, Ronghang Ryali, Chaitanya Ma, Tengyu Khedr, Haitham Rädle, Roman Rolland, Chloe Gustafson, Laura Mintun, Eric Pan, Junting Alwala, Kalyan Vasudev Carion, Nicolas Wu, Chao-Yuan Girshick, Ross Dollár, Piotr Feichtenhofer, Christoph |
| contents | We present Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos. We build a data engine, which improves model and data via user interaction, to collect the largest video segmentation dataset to date. Our model is a simple transformer architecture with streaming memory for real-time video processing. SAM 2 trained on our data provides strong performance across a wide range of tasks. In video segmentation, we observe better accuracy, using 3x fewer interactions than prior approaches. In image segmentation, our model is more accurate and 6x faster than the Segment Anything Model (SAM). We believe that our data, model, and insights will serve as a significant milestone for video segmentation and related perception tasks. We are releasing our main model, dataset, as well as code for model training and our demo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00714 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SAM 2: Segment Anything in Images and Videos Ravi, Nikhila Gabeur, Valentin Hu, Yuan-Ting Hu, Ronghang Ryali, Chaitanya Ma, Tengyu Khedr, Haitham Rädle, Roman Rolland, Chloe Gustafson, Laura Mintun, Eric Pan, Junting Alwala, Kalyan Vasudev Carion, Nicolas Wu, Chao-Yuan Girshick, Ross Dollár, Piotr Feichtenhofer, Christoph Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning We present Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos. We build a data engine, which improves model and data via user interaction, to collect the largest video segmentation dataset to date. Our model is a simple transformer architecture with streaming memory for real-time video processing. SAM 2 trained on our data provides strong performance across a wide range of tasks. In video segmentation, we observe better accuracy, using 3x fewer interactions than prior approaches. In image segmentation, our model is more accurate and 6x faster than the Segment Anything Model (SAM). We believe that our data, model, and insights will serve as a significant milestone for video segmentation and related perception tasks. We are releasing our main model, dataset, as well as code for model training and our demo. |
| title | SAM 2: Segment Anything in Images and Videos |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.00714 |