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Related Experiment Video

Updated: Jul 5, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Inter-Class and Inter-Domain Semantic Augmentation for Domain Generalization.

Mengzhu Wang, Yuehua Liu, Jianlong Yuan

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 23, 2024
    PubMed
    Summary

    This study introduces Inter-Class and Inter-Domain Semantic Augmentation (CDSA) to improve domain generalization. CDSA enhances model performance on unseen data by increasing source domain diversity through novel semantic augmentation techniques.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Domain generalization aims to create models applicable to new, unseen data domains.
    • Data augmentation, particularly semantic-level methods, improves domain generalization but often lacks source domain diversity.
    • Existing semantic augmentation techniques sample directions within-class and within-domain, limiting their effectiveness.

    Purpose of the Study:

    • To propose a novel approach, Inter-Class and Inter-Domain Semantic Augmentation (CDSA), to enhance domain generalization.
    • To address the limitation of insufficient source domain diversity in current semantic-level data augmentation methods.

    Main Methods:

    • Introduced CrossSmooth, a sampling-based method to generate semantic directions from inter-class variations.
    • Developed CrossVariance to capture diverse domain styles by sampling these inter-class semantic directions.
    • Implemented CDSA for domain generalization tasks.

    Main Results:

    • Demonstrated significant effectiveness of CDSA on four benchmark datasets: Digits-DG, PACS, Office-Home, and DomainNet.
    • Achieved substantial improvements when validating CDSA on semantic segmentation datasets: GTAV, SYNTHIA, Cityscapes, Mapillary, and BDDS.

    Conclusions:

    • CDSA effectively enhances domain generalization by increasing source domain diversity.
    • The proposed method shows broad applicability and significant performance gains across various computer vision tasks.