Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning (2206.06122v2)

Abstract: Freezing the pre-trained backbone has become a standard paradigm to avoid overfitting in few-shot segmentation. In this paper, we rethink the paradigm and explore a new regime: {\em fine-tuning a small part of parameters in the backbone}. We present a solution to overcome the overfitting problem, leading to better model generalization on learning novel classes. Our method decomposes backbone parameters into three successive matrices via the Singular Value Decomposition (SVD), then {\em only fine-tunes the singular values} and keeps others frozen. The above design allows the model to adjust feature representations on novel classes while maintaining semantic clues within the pre-trained backbone. We evaluate our {\em Singular Value Fine-tuning (SVF)} approach on various few-shot segmentation methods with different backbones. We achieve state-of-the-art results on both Pascal-5$^i$ and COCO-20$^i$ across 1-shot and 5-shot settings. Hopefully, this simple baseline will encourage researchers 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.

• The paper introduces Singular Value Fine-tuning (SVF), a novel method using Singular Value Decomposition (SVD) to fine-tune only the singular values of pre-trained backbones for few-shot segmentation.
• SVF avoids overfitting by only adjusting a small subset of parameters, preserving the backbone's rich semantic representation while adapting it to novel classes.
• SVF achieves state-of-the-art results on few-shot segmentation benchmarks like Pascal-5i and COCO-20i, showing improved generalization and accuracy over traditional backbone freezing.

Singular Value Fine-tuning: A Paradigm Shift in Few-shot Segmentation

The paper "Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning" introduces a noteworthy approach to ameliorate the prevalent problem of overfitting in few-shot segmentation tasks. The research challenges the conventional practice of freezing the pre-trained backbone—a strategy often employed to avert overfitting due to limited data—by proposing a novel technique that selectively fine-tunes a small subset of parameters within the backbone. This is achieved through the singular value decomposition (SVD), specifically focusing on the singular values.

Summary

The concept underpinning Singular Value Fine-tuning (SVF) is rooted in the hypothesis that not all parameters in a pre-trained backbone need adjustment to improve few-shot segmentation performance. Instead, adjusting a strategically chosen subset—the singular values—can strike a balance between preserving the backbone's rich semantic representation and adapting the model to novel classes. The backbone, typically trained on vast data sets like ImageNet for classification tasks, may contain semantic cues less relevant to segmentation tasks, especially in novel environments.

To implement SVF, the authors suggest decomposing the backbone’s convolutional layer parameters via SVD, yielding three components: matrices $\mathbf{U}$, diagonal singular value matrix $\mathbf{S}$, and matrix $\mathbf{V}^T$. Within SVF, only $\mathbf{S}$ is subjected to fine-tuning, while $\mathbf{U}$ and $\mathbf{V}^T$ are kept frozen. The singular values in $\mathbf{S}$ serve to reweight semantic cues, effectively enabling the model to better focus on segmentation tasks without altering the fundamental structure of the backbone.

Evaluation and Results

The paper reports that SVF achieves state-of-the-art results across various few-shot segmentation benchmarks, specifically Pascal-5$i$ and COCO-20$i$ datasets, in both 1-shot and 5-shot scenarios. The experiments highlight that models utilizing SVF consistently outperform those that utilize the traditional freezing paradigm, as it is impervious to overfitting while significantly boosting model generalization in recognizing novel classes. The paper provides extensive empirical evidence suggesting the efficacy of SVF in improving segmentation accuracy and the model's ability to differentiate foreground objects from background noise.

Implications and Discussions

The implications of this research extend beyond few-shot segmentation, suggesting potential applicability for fine-tuning extensively large pre-trained models while managing computational costs and memory resources. By reducing learnable parameters to a minimal yet impactful fraction—specifically, focusing on the singular values—SVF could serve as a guiding principle for optimizing neural networks in data-scarce environments.

In theoretical terms, SVF presents an insightful approach to leveraging model decompositions, offering adaptability solely through reweighting key semantic features. This approach to parameter fine-tuning might pave the way for similar techniques across other domains where semantic-rich pre-trained models are deployed.

Conclusion

This paper redefines the boundaries of backbone fine-tuning, advocating for a methodology that finely tunes only the singular value space of pre-trained networks. SVF not only addresses the overfitting problem inherent in few-shot segmentation but also maximizes the generalization capability of these models. This work encourages a reevaluation of backbone fine-tuning paradigms and sets the groundwork for further research into similar adaptive techniques in artificial intelligence applications. As such, Singular Value Fine-tuning stands as a promising direction for those seeking to enhance performance and efficiency in few-shot segmentation and potentially other machine learning tasks.