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    This study introduces a novel granularity-controllable interactive segmentation (IS) paradigm, UniGraCo, to precisely control segmentation detail. UniGraCo overcomes ambiguity and redundancy, offering a more efficient and practical interactive tool for users.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Analysis

    Background:

    • Interactive Segmentation (IS) deduces human intent from sparse prompts for object segmentation.
    • Ambiguity in sparse-to-dense mapping leads to user trial-and-error and suboptimal segmentation granularity.
    • Existing multi-granularity IS methods lack scalability and produce redundant outputs.

    Purpose of the Study:

    • To develop a granularity-controllable IS paradigm that resolves ambiguity and allows precise user control over segmentation detail.
    • To introduce a Unified Granularity Controller (UniGraCo) supporting diverse control signals for varied segmentation needs.
    • To enhance system efficiency and practicality in interactive segmentation tasks.

    Main Methods:

    • Proposed a Unified Granularity Controller (UniGraCo) with multi-type optional control signals for flexible granularity adjustment.
    • Developed an automated data engine for low-cost generation of high-quality, multi-granularity mask-control signal data pairs.
    • Designed a granularity-controllable learning strategy to train IS models efficiently and stably, preserving segmentation capabilities.

    Main Results:

    • UniGraCo demonstrated significant advantages over existing methods in intricate instance and part-level segmentation scenarios.
    • The automated data engine effectively reduced annotation costs while generating abundant training data.
    • The granularity-controllable learning strategy successfully endowed pre-trained IS models with precise control over segmentation granularity.

    Conclusions:

    • UniGraCo offers a creative and effective solution for precise granularity control in interactive segmentation.
    • The proposed paradigm significantly improves upon existing multi-granularity IS methods in terms of efficiency and practicality.
    • UniGraCo shows strong potential as a practical interactive tool for complex segmentation tasks.