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Self-Training for Class-Incremental Semantic Segmentation.

Lu Yu, Xialei Liu, Joost van de Weijer

    IEEE Transactions on Neural Networks and Learning Systems
    |March 17, 2022
    PubMed
    Summary
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    This study introduces a novel self-training method to combat catastrophic forgetting in class-incremental semantic segmentation using unlabeled data for knowledge rehearsal. The approach significantly improves performance on benchmark datasets.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Deep Learning

    Background:

    • Deep neural networks in class-incremental semantic segmentation forget previous knowledge due to lack of access to prior labeled data.
    • Catastrophic forgetting is a major challenge when incrementally learning new classes.

    Purpose of the Study:

    • To propose a self-training approach using unlabeled data to mitigate catastrophic forgetting in class-incremental semantic segmentation.
    • To enhance knowledge rehearsal by leveraging both old and newly trained models.

    Main Methods:

    • A temporary model is trained for the current task.
    • Pseudo-labels for unlabeled data are generated by fusing information from old and temporary models.
    • Conflict reduction and self-entropy maximization are employed to refine pseudo-labels and predictions.

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    Main Results:

    • Achieved significant performance gains, including up to 114% relative gain on Pascal-VOC 2012 and 8.5% on ADE20K.
    • Demonstrated that diverse, general-purpose auxiliary data can yield substantial performance improvements.
    • Outperformed previous state-of-the-art methods in class-incremental semantic segmentation.

    Conclusions:

    • The proposed self-training method effectively addresses catastrophic forgetting in class-incremental semantic segmentation.
    • Leveraging unlabeled data and employing conflict reduction strategies are key to successful knowledge rehearsal.
    • The approach shows robustness and adaptability with diverse auxiliary data.