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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.16739 |
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| _version_ | 1866916703798034432 |
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| author | Piater, Tristan Barz, Björn Freytag, Alexander |
| author_facet | Piater, Tristan Barz, Björn Freytag, Alexander |
| contents | The Segment Anything Model (SAM) is widely used for segmenting a diverse range of objects in natural images from simple user prompts like points or bounding boxes. However, SAM's performance decreases substantially when applied to non-natural domains like microscopic imaging. Furthermore, due to SAM's interactive design, it requires a precise prompt for each image and object, which is unfeasible in many automated biomedical applications. Previous solutions adapt SAM by training millions of parameters via fine-tuning large parts of the model or of adapter layers. In contrast, we show that as little as 2,048 additional parameters are sufficient for turning SAM into a use-case specialist for a certain downstream task. Our novel PTSAM (prompt-tuned SAM) method uses prompt-tuning, a parameter-efficient fine-tuning technique, to adapt SAM for a specific task. We validate the performance of our approach on multiple microscopic and one medical dataset. Our results show that prompt-tuning only SAM's mask decoder already leads to a performance on-par with state-of-the-art techniques while requiring roughly 2,000x less trainable parameters. For addressing domain gaps, we find that additionally prompt-tuning SAM's image encoder is beneficial, further improving segmentation accuracy by up to 18% over state-of-the-art results. Since PTSAM can be reliably trained with as little as 16 annotated images, we find it particularly helpful for applications with limited training data and domain shifts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16739 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompt-Tuning SAM: From Generalist to Specialist with only 2048 Parameters and 16 Training Images Piater, Tristan Barz, Björn Freytag, Alexander Computer Vision and Pattern Recognition The Segment Anything Model (SAM) is widely used for segmenting a diverse range of objects in natural images from simple user prompts like points or bounding boxes. However, SAM's performance decreases substantially when applied to non-natural domains like microscopic imaging. Furthermore, due to SAM's interactive design, it requires a precise prompt for each image and object, which is unfeasible in many automated biomedical applications. Previous solutions adapt SAM by training millions of parameters via fine-tuning large parts of the model or of adapter layers. In contrast, we show that as little as 2,048 additional parameters are sufficient for turning SAM into a use-case specialist for a certain downstream task. Our novel PTSAM (prompt-tuned SAM) method uses prompt-tuning, a parameter-efficient fine-tuning technique, to adapt SAM for a specific task. We validate the performance of our approach on multiple microscopic and one medical dataset. Our results show that prompt-tuning only SAM's mask decoder already leads to a performance on-par with state-of-the-art techniques while requiring roughly 2,000x less trainable parameters. For addressing domain gaps, we find that additionally prompt-tuning SAM's image encoder is beneficial, further improving segmentation accuracy by up to 18% over state-of-the-art results. Since PTSAM can be reliably trained with as little as 16 annotated images, we find it particularly helpful for applications with limited training data and domain shifts. |
| title | Prompt-Tuning SAM: From Generalist to Specialist with only 2048 Parameters and 16 Training Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.16739 |