RobustSAM: Segment Anything Robustly on Degraded Images (CVPR 2024 Highlight)

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Introduction

Segment Anything Model (SAM) has emerged as a transformative approach in image segmentation, acclaimed for its robust zero-shot segmentation capabilities and flexible prompting system. Nonetheless, its performance is challenged by images with degraded quality. Addressing this limitation, we propose the Robust Segment Anything Model (RobustSAM), which enhances SAM's performance on low-quality images while preserving its promptability and zero-shot generalization.

Our method leverages the pre-trained SAM model with only marginal parameter increments and computational requirements. The additional parameters of RobustSAM can be optimized within 30 hours on eight GPUs, demonstrating its feasibility and practicality for typical research laboratories. We also introduce the Robust-Seg dataset, a collection of 688K image-mask pairs with different degradations designed to train and evaluate our model optimally. Extensive experiments across various segmentation tasks and datasets confirm RobustSAM's superior performance, especially under zero-shot conditions, underscoring its potential for extensive real-world application. Additionally, our method has been shown to effectively improve the performance of SAM-based downstream tasks such as single image dehazing and deblurring.

Comparison of computational requirements

Visual Comparison

Quantitative Comparison

Seen dataset with synthetic degradation

Unseen dataset with synthetic degradation

Unseen dataset with real degradation

Reference

If you find this work useful, please consider citing us!

@inproceedings{chen2024robustsam,
  title={RobustSAM: Segment Anything Robustly on Degraded Images},
  author={Chen, Wei-Ting and Vong, Yu-Jiet and Kuo, Sy-Yen and Ma, Sizhou and Wang, Jian},
  journal={CVPR},
  year={2024}
}

Acknowledgements

We thank the authors of SAM from which our repo is based off of.