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A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application.

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    |August 21, 2024
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    Summary
    This summary is machine-generated.

    This review surveys Continual Semantic Segmentation (CSS), a key area in lifelong learning for AI. It categorizes CSS models and analyzes challenges, datasets, and applications for advancing AI systems.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision

    Background:

    • Continual learning enables AI systems to adapt and learn continuously in open-set conditions, overcoming limitations of traditional one-way training.
    • Continual semantic segmentation (CSS) is a challenging yet crucial task within computer vision, focusing on dense prediction.

    Purpose of the Study:

    • To provide a comprehensive review of Continual Semantic Segmentation (CSS).
    • To cover problem formulations, challenges, datasets, theories, and applications in CSS.

    Main Methods:

    • Categorization of current CSS models into data-replay and data-free approaches.
    • In-depth analysis and qualitative/quantitative comparison of CSS approaches on relevant datasets.
    • Introduction of CSS specialities and development tendencies.

    Main Results:

    • A structured overview of CSS methodologies, distinguishing between data-replay and data-free strategies.
    • Analysis of challenges and a curated list of datasets relevant to CSS research.
    • Development of a benchmark for CSS, including references and evaluation results.

    Conclusions:

    • This survey offers a reference for advancing lifelong learning in AI, particularly in computer vision.
    • It provides valuable perspectives for researchers and practitioners in Continual Semantic Segmentation and related fields.