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Freezing the pre-trained backbone has become a standard paradigm to avoidoverfitting in few-shot segmentation. In this paper, we rethink the paradigmand explore a new regime: {\em fine-tuning a small part of parameters in thebackbone}. We present a solution to overcome the overfitting problem, leadingto better model generalization on learning novel classes. Our method decomposesbackbone parameters into three successive matrices via the Singular ValueDecomposition (SVD), then {\em only fine-tunes the singular values} and keepsothers frozen. The above design allows the model to adjust featurerepresentations on novel classes while maintaining semantic clues within thepre-trained backbone. We evaluate our {\em Singular Value Fine-tuning (SVF)}approach on various few-shot segmentation methods with different backbones. Weachieve state-of-the-art results on both Pascal-5$^i$ and COCO-20$^i$ across1-shot and 5-shot settings. Hopefully, this simple baseline will encourageresearchers to rethink the role of backbone fine-tuning in few-shot settings.The source code and models will be available at https://github.com/syp2ysy/SVF.

Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning