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Segment Concealed Objects With Incomplete Supervision.

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    This study introduces SEE, a novel method for Incompletely-Supervised Concealed Object Segmentation (ISCOS). SEE effectively segments challenging objects by using the Segment Anything Model (SAM) and advanced pseudo-labeling strategies.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Incompletely-Supervised Concealed Object Segmentation (ISCOS) is challenging due to limited annotations and difficulty distinguishing objects from backgrounds.
    • Existing methods struggle with the inherent similarities between concealed objects and their environments.

    Purpose of the Study:

    • To propose the first unified method for ISCOS that addresses both limited supervision and object-background similarity.
    • To enhance the performance of segmentation models in scenarios with incomplete annotations.

    Main Methods:

    • A unified mean-teacher framework, SEE, is introduced, leveraging the Segment Anything Model (SAM) for pseudo-label generation.
    • Strategies for pseudo-label generation, storage, and supervision are developed to ensure robust training.
    • A hybrid-granularity feature grouping module is designed to improve segmentation coherence by clustering similar features.

    Main Results:

    • The proposed method achieves state-of-the-art performance across multiple ISCOS tasks.
    • Experimental results validate the effectiveness of SEE in segmenting concealed objects.
    • SEE demonstrates significant improvements in segmentation accuracy and completeness.

    Conclusions:

    • SEE provides a robust and effective solution for Incompletely-Supervised Concealed Object Segmentation.
    • The method offers a plug-and-play capability to enhance existing segmentation models.
    • This work advances the field of segmentation for challenging, real-world scenarios.