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.

RobustSAM 是一种改进的 Segment Anything Model (SAM) 方法，通过增强 SAM 在低质量图像上的性能，同时保持其可提示性和零样本泛化能力。其在各种分割任务和数据集上的广泛实验证实了其卓越性能，特别是在零样本条件下，在广泛的现实世界应用中具有巨大的潜力。