Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains, but due to the absence of style diversity during training, enhancing performance on unseen target domains using only single source domain data remains a challenging task. Generation of simulated data is a feasible alternative to retrieving large style-diverse real-world datasets as it is a cumbersome and budget-intensive process. However, the large domain-specific inconsistencies between simulated and real-world data pose a significant generalization challenge in semantic segmentation. In this work, to alleviate this problem, we propose a novel MultiResolution Feature Perturbation (MRFP) technique to randomize domain-specific fine-grained features and perturb style of coarse features. Our experimental results on various urban-scene segmentation datasets clearly indicate that, along with the perturbation of style-information, perturbation of fine-feature components is paramount to learn domain invariant robust feature maps for semantic segmentation models. MRFP is a simple and computationally efficient, transferable module with no additional learnable parameters or objective functions, that helps state-of-the-art deep neural networks to learn robust domain invariant features for simulation-to-real semantic segmentation.

本研究提出了一种新颖的多分辨率特征扰动（MRFP）技术，通过对领域特定的细粒度特征进行随机扰动和对粗特征的风格进行扰动，以提升神经网络在语义分割中的鲁棒性和域不变性。实验结果表明，除了风格信息的扰动外，对细粒度特征的扰动对于学习域不变鲁棒特征映射至关重要。MRFP是一个简单且计算效率高的可传递模块，不需要额外的可学习参数或目标函数，有助于现有深度神经网络在模拟到真实场景的语义分割中学习到鲁棒的域不变特征。

MRFP：利用多分辨率特征扰动从Sim-2-Real学习可泛化语义分割