SAM3-UNet: Simplified Adaptation of Segment Anything Model 3
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911296441548800 |
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| author | Xiong, Xinyu Wu, Zihuang Lu, Lei Xia, Yufa |
| author_facet | Xiong, Xinyu Wu, Zihuang Lu, Lei Xia, Yufa |
| contents | In this paper, we introduce SAM3-UNet, a simplified variant of Segment Anything Model 3 (SAM3), designed to adapt SAM3 for downstream tasks at a low cost. Our SAM3-UNet consists of three components: a SAM3 image encoder, a simple adapter for parameter-efficient fine-tuning, and a lightweight U-Net-style decoder. Preliminary experiments on multiple tasks, such as mirror detection and salient object detection, demonstrate that the proposed SAM3-UNet outperforms the prior SAM2-UNet and other state-of-the-art methods, while requiring less than 6 GB of GPU memory during training with a batch size of 12. The code is publicly available at https://github.com/WZH0120/SAM3-UNet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SAM3-UNet: Simplified Adaptation of Segment Anything Model 3 Xiong, Xinyu Wu, Zihuang Lu, Lei Xia, Yufa Computer Vision and Pattern Recognition In this paper, we introduce SAM3-UNet, a simplified variant of Segment Anything Model 3 (SAM3), designed to adapt SAM3 for downstream tasks at a low cost. Our SAM3-UNet consists of three components: a SAM3 image encoder, a simple adapter for parameter-efficient fine-tuning, and a lightweight U-Net-style decoder. Preliminary experiments on multiple tasks, such as mirror detection and salient object detection, demonstrate that the proposed SAM3-UNet outperforms the prior SAM2-UNet and other state-of-the-art methods, while requiring less than 6 GB of GPU memory during training with a batch size of 12. The code is publicly available at https://github.com/WZH0120/SAM3-UNet. |
| title | SAM3-UNet: Simplified Adaptation of Segment Anything Model 3 |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.01789 |