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• W4386075540 abstract "Unsupervised domain adaptation (UDA) for semantic segmentation aims at improving the model performance on the unlabeled target domain by leveraging a labeled source domain. Existing approaches have achieved impressive progress by utilizing pseudo-labels on the unlabeled target-domain images. Yet the low-quality pseudo-labels, arising from the domain discrepancy, inevitably hinder the adaptation. This calls for effective and accurate approaches to estimating the reliability of the pseudo-labels, in order to rectify them. In this paper, we propose to estimate the rectification values of the predicted pseudo-labels with implicit neural representations. We view the rectification value as a signal defined over the continuous spatial domain. Taking an image coordinate and the nearby deep features as inputs, the rectification value at a given coordinate is predicted as an output. This allows us to achieve high-resolution and detailed rectification values estimation, important for accurate pseudo-label generation at mask boundaries in particular. The rectified pseudo-labels are then leveraged in our rectification-aware mixture model (RMM) to be learned end-to-end and help the adaptation. We demonstrate the effectiveness of our approach on different UDA benchmarks, including synthetic-to-real and day-to-night. Our approach achieves superior results compared to state-of-the-art. The implementation is available at https://github.com/ETHRuiGong/IR2F." @default.
• W4386075540 created "2023-08-23" @default.
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• W4386075540 date "2023-06-01" @default.
• W4386075540 modified "2023-10-17" @default.
• W4386075540 title "Continuous Pseudo-Label Rectified Domain Adaptive Semantic Segmentation with Implicit Neural Representations" @default.
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• W4386075540 doi "https://doi.org/10.1109/cvpr52729.2023.00698" @default.
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