Abstract
Domain generalization aims to train a robust model on multiple source domains that generalizes well to unseen target domains. While considerable attention has focused on training domain generalizable models, a few recent studies have shifted the attention to test time, i.e., leveraging test samples for better target generalization. To this end, this paper proposes a novel test-time domain generalization method, Domain Conditioned Normalization (DCN), to infer the normalization statistics of the target domain from only a single test sample. In order to learn to predict the normalization statistics, DCN adopts a meta-learning framework and simulates the inference process of the normalization statistics at training. Extensive experimental results have shown that DCN brings consistent improvements to many state-of-the-art domain generalization methods on three widely adopted benchmarks.
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Acknowledgement
This work is supported by the National Key R &D Program of China (No. 2019YFB1804304), 111 plan (No. BP0719010), and STCSM (No. 18DZ2270700, No. 21DZ1100100), and State Key Laboratory of UHD Video and Audio Production and Presentation.
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Jiang, Y. et al. (2023). Domain-Conditioned Normalization for Test-Time Domain Generalization. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13808. Springer, Cham. https://doi.org/10.1007/978-3-031-25085-9_17
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