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Domain Generalization with Global Sample Mixup

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

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Abstract

Deep models have demonstrated outstanding ability in various computer vision tasks but are also notoriously known to generalize poorly when encountering unseen domains with different statistics. To alleviate this issue, in this technical report we present a new domain generalization method based on training sample mixup. The main enabling factor of our superior performance lies in the global mixup strategy across the source domains, where the batched samples from multiple graphic devices are mixed up for a better generalization ability. Since the domain gap in NICO datasets is mainly due to the intertwined background bias, the global mix strategy decreases such gap to a great extent by producing abundant mixed backgrounds. Besides, we have conducted extensive experiments on different backbones combined with various data augmentation to study the generalization performance of different model structures. Our final ensembled model achieved 74.07% on the test set and took the 3rd place according to the image classification accuracy (Acc.) in NICO Common Context Generalization Challenge 2022.

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Acknowledgements

This work was supported by the National Key Research & Development Project of China (2021ZD0110700), the National Natural Science Foundation of China (U19B2043, 61976185), Zhejiang Natural Science Foundation (LR19F020002), Zhejiang Innovation Foundation (2019R52002), and the Fundamental Research Funds for the Central Universities (226-2022-00051).

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Authors and Affiliations

  1. Zhejiang University, Hangzhou, 310007, Zhejiang, China

    Yulei Lu, Yawei Luo, Antao Pan, Yangjun Mao & Jun Xiao

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  1. Yulei Lu
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  2. Yawei Luo
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  3. Antao Pan
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  4. Yangjun Mao
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  5. Jun Xiao
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Correspondence to Yawei Luo .

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Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Lu, Y., Luo, Y., Pan, A., Mao, Y., Xiao, J. (2023). Domain Generalization with Global Sample Mixup. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13806. Springer, Cham. https://doi.org/10.1007/978-3-031-25075-0_35

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  • DOI: https://doi.org/10.1007/978-3-031-25075-0_35

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