SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction

• URL: http://arxiv.org/abs/2308.14604v3
• Date: Mon, 18 Dec 2023 07:40:35 GMT
• Title: SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction
• Authors: Zelin Peng, Zhengqin Xu, Zhilin Zeng, Xiaokang Yang, Wei Shen
• Abstract summary: Segment Anything Model (SAM) has received remarkable attention as it offers a powerful and versatile solution for object segmentation in images. We propose fine-tuning SAM efficiently by parameter space reconstruction (SAM-PARSER) We obtain the bases by matrix decomposition, and fine-tuning the coefficients to reconstruct the parameter space tailored to the new scenario by an optimal linear combination of the bases.
• Score: 53.871596866809725
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Segment Anything Model (SAM) has received remarkable attention as it offers a powerful and versatile solution for object segmentation in images. However, fine-tuning SAM for downstream segmentation tasks under different scenarios remains a challenge, as the varied characteristics of different scenarios naturally requires diverse model parameter spaces. Most existing fine-tuning methods attempt to bridge the gaps among different scenarios by introducing a set of new parameters to modify SAM's original parameter space. Unlike these works, in this paper, we propose fine-tuning SAM efficiently by parameter space reconstruction (SAM-PARSER), which introduce nearly zero trainable parameters during fine-tuning. In SAM-PARSER, we assume that SAM's original parameter space is relatively complete, so that its bases are able to reconstruct the parameter space of a new scenario. We obtain the bases by matrix decomposition, and fine-tuning the coefficients to reconstruct the parameter space tailored to the new scenario by an optimal linear combination of the bases. Experimental results show that SAM-PARSER exhibits superior segmentation performance across various scenarios, while reducing the number of trainable parameters by $\approx 290$ times compared with current parameter-efficient fine-tuning methods.

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