2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023
DOI: 10.1109/cvpr52729.2023.02307
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Dynamically Instance-Guided Adaptation: A Backward-free Approach for Test-Time Domain Adaptive Semantic Segmentation

Wei Wang,
Zhun Zhong,
Weijie Wang
et al.

Abstract: encourage the model to capture robust representation. SAM combines the historical prototypes with instance-level prototypes to adjust semantic predictions, which can be associated with the parametric classifier to mutually benefit the final results. Extensive experiments evaluated on five target domains demonstrate the effectiveness and efficiency of the proposed method. Our DIGA establishes new state-of-theart performance in TTDA-Seg. Source code is available at: https://github.com/Waybaba/DIGA.

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Cited by 13 publications
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