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Referring Camouflaged Object Detection.

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    This study introduces referring camouflaged object detection (Ref-COD) and a new dataset, R2C7K. The proposed R2CNet framework effectively segments specified camouflaged objects using referring images.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Camouflaged object detection (COD) aims to segment objects that blend with their surroundings.
    • Existing COD methods struggle to identify specific targets when multiple camouflaged objects are present.
    • Referring images provide valuable context for specifying the target object.

    Purpose of the Study:

    • To introduce and address the novel task of referring camouflaged object detection (Ref-COD).
    • To develop a robust framework capable of segmenting specified camouflaged objects guided by reference images.
    • To create a large-scale dataset for training and evaluating Ref-COD models.

    Main Methods:

    • Assembled a large-scale dataset, R2C7K, comprising 7,000 images across 64 categories.
    • Developed a dual-branch framework, R2CNet, with reference and segmentation branches.
    • Introduced Referring Mask Generation and Referring Feature Enrichment modules for improved specificity.

    Main Results:

    • R2CNet demonstrated superior performance compared to conventional COD methods on the Ref-COD task.
    • The proposed methods effectively segment specified camouflaged objects.
    • The framework accurately identifies the main body of target objects within complex scenes.

    Conclusions:

    • Ref-COD is a viable and important task for precise object segmentation in challenging environments.
    • The R2CNet framework offers a strong baseline for future research in Ref-COD.
    • The R2C7K dataset facilitates advancements in the field of guided camouflaged object detection.