TS-SAM: Fine-Tuning Segment-Anything Model for Downstream Tasks

• URL: http://arxiv.org/abs/2408.01835v1
• Date: Sat, 3 Aug 2024 18:08:51 GMT
• Title: TS-SAM: Fine-Tuning Segment-Anything Model for Downstream Tasks
• Authors: Yang Yu, Chen Xu, Kai Wang,
• Abstract summary: There is still a significant performance gap between fine-tuned SAMs and domain-specific models. We propose Two-Stream SAM (TS-SAM), which integrates the powerful features from SAM into side network training for comprehensive feature fusion. Extensive experiments on ten public datasets from three tasks demonstrate that TS-SAM not only significantly outperforms the recently proposed SAM-Adapter and SSOM, but achieves competitive performance with the SOTA domain-specific models.
• Score: 10.75125721857487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adapter based fine-tuning has been studied for improving the performance of SAM on downstream tasks. However, there is still a significant performance gap between fine-tuned SAMs and domain-specific models. To reduce the gap, we propose Two-Stream SAM (TS-SAM). On the one hand, inspired by the side network in Parameter-Efficient Fine-Tuning (PEFT), we designed a lightweight Convolutional Side Adapter (CSA), which integrates the powerful features from SAM into side network training for comprehensive feature fusion. On the other hand, in line with the characteristics of segmentation tasks, we designed Multi-scale Refinement Module (MRM) and Feature Fusion Decoder (FFD) to keep both the detailed and semantic features. Extensive experiments on ten public datasets from three tasks demonstrate that TS-SAM not only significantly outperforms the recently proposed SAM-Adapter and SSOM, but achieves competitive performance with the SOTA domain-specific models. Our code is available at: https://github.com/maoyangou147/TS-SAM.

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