research-article

Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction

Authors:

Dongliang Chang,

Kongming Liang,

Zhanyu MaAuthors Info & Claims

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

Pages 4821 - 4829

https://doi.org/10.1145/3503161.3548267

Published: 10 October 2022 Publication History

Abstract

Adaptation to out-of-distribution data is a meta-challenge for all statistical learning algorithms that strongly rely on the i.i.d. assumption. It leads to unavoidable labor costs and confidence crises in realistic applications. For that, domain generalization aims at mining domain-irrelevant knowledge from multiple source domains that can generalize to unseen target domains. In this paper, by leveraging the frequency domain of an image, we uniquely work with two key observations: (i) the high-frequency information of an image depicts object edge structure, which preserves high-level semantic information of the object is naturally consistent across different domains, and (ii) the low-frequency component retains object smooth structure, while this information is susceptible to domain shifts. Motivated by the above observations, we introduce (i) an encoder-decoder structure to disentangle high- and low-frequency features of an image, (ii) an information interaction mechanism to ensure the helpful knowledge from both two parts can cooperate effectively, and (iii) a novel data augmentation technique that works on the frequency domain to encourage the robustness of frequency-wise feature disentangling. The proposed method obtains state-of-the-art performance on three widely used domain generalization benchmarks (Digit-DG, Office-Home, and PACS).

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Index Terms

Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations

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cover image ACM Conferences

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

October 2022

7537 pages

ISBN:9781450392037

DOI:10.1145/3503161

General Chairs:
João Magalhães
NOVA University of Lisbon, Portugal
,
Alberto del Bimbo
University of Florence, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
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Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Xavier Alameda-Pineda
Inria, Grenoble, France
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Qin Jin
Renmin University of China, China
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Vincent Oria
New Jersey Institute of Technology, USA
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Laura Toni
University College London, UK

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Published: 10 October 2022

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MM '22: The 30th ACM International Conference on Multimedia

October 10 - 14, 2022

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Lee JHwang H(2025)Mix-Spectrum for Generalization in Visual Reinforcement LearningIEEE Access10.1109/ACCESS.2025.352695913(7939-7950)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3526959
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