We introduced SSR, which utilizes SAM (segment-anything) as a strong
regularizer during training, to greatly enhance the robustness of the image
encoder for handling various domains. Specifically, given the fact that SAM is
pre-trained with a large number of images over the internet, which cover a
diverse variety of domains, the feature encoding extracted by the SAM is
obviously less dependent on specific domains when compared to the traditional
ImageNet pre-trained image encoder. Meanwhile, the ImageNet pre-trained image
encoder is still a mature choice of backbone for the semantic segmentation
task, especially when the SAM is category-irrelevant. As a result, our SSR
provides a simple yet highly effective design. It uses the ImageNet pre-trained
image encoder as the backbone, and the intermediate feature of each stage (ie
there are 4 stages in MiT-B5) is regularized by SAM during training. After
extensive experimentation on GTA5$\rightarrow$Cityscapes, our SSR significantly
improved performance over the baseline without introducing any extra inference
overhead.

本文介绍了 SSR，它利用 SAM（任意分割）作为强正则化器，在训练过程中大大增强了处理各个领域的图像编码器的健壮性。