Abstract
Domain generalization aims to enhance robustness of models to different domains, which is crucial for safety-critical systems in practice. In this paper, we propose a simple plug-in module to promote the ability of generalization for semantic segmentation networks without extra loss function. Firstly, we rethink the relationship between semantics and style in the sight of feature maps, and divide the channels of them into two kinds (i.e. style-sensitive channels and semantic-sensitive channels) via the variance of Gram matrix. Secondly, with the assumption that the domain shift mainly lies in style, a random erasure method is proposed to style-sensitive-channel features with the hope of learning domain invariant features and preventing model from over-fitting to specific domain. Extensive experiments demonstrate that the generalization of our proposed method outperforms existing approaches.
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Acknowledgement
This work is partially supported by National Natural Science Foundation of China (Grants no. 61772568), Guangdong Basic and Applied Basic Research Foundation (Grant no. 2019A1515012029), and Youth science and technology innovation talent of Guangdong Special Support Program.
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Su, S., Wang, H., Yang, M. (2021). Suppressing Style-Sensitive Features via Randomly Erasing for Domain Generalizable Semantic Segmentation. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13022. Springer, Cham. https://doi.org/10.1007/978-3-030-88013-2_25
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