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W3208147746 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." @default.
W3208147746 created "2021-11-08" @default.
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W3208147746 date "2021-01-01" @default.
W3208147746 modified "2023-09-23" @default.
W3208147746 title "Suppressing Style-Sensitive Features via Randomly Erasing for Domain Generalizable Semantic Segmentation" @default.
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W3208147746 doi "https://doi.org/10.1007/978-3-030-88013-2_25" @default.
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