Proceedings

Indirect Alignment and Relationship Preservation for Domain Generalization

Wei Wei, Zixiong Li, Jing Yan, Mingwen Shao, Lin Li

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 2054-2062.

https://doi.org/10.24963/ijcai.2025/229

Keywords

  • Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning
  • Computer Vision: CV: Machine learning for vision
  • Machine Learning: ML: Classification

Abstract

Domain generalization (DG) aims to train models on multiple source domains to generalize effectively to unseen target domains, addressing performance degradation caused by domain shifts. Many existing methods rely on direct feature alignment, which disrupts natural sequence relationships, causes misalignment and feature distortion, and leads to overfitting, especially with significant domain gaps. To tackle these issues, we propose a novel DG approach with two key modules: the Sample Difference Keeping (SDK) module, which preserves natural sequence relationships to enhance feature diversity and separability, and the Sample Consistency Alignment (SCA) module, which achieves indirect alignment by modeling inter-class and inter-domain relationship consistencies. This approach mitigates overfitting and misalignment, ensuring adaptability to significant domain gaps. Extensive experiments demonstrate that our framework consistently outperforms state-of-the-art methods.

Wei Wei, Zixiong Li, Jing Yan, Mingwen Shao, and Lin Li. 2025. Indirect Alignment and Relationship Preservation for Domain Generalization. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 2054–2062. https://doi.org/10.24963/ijcai.2025/229
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