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    This study introduces the causal prompting network (CPNet) for improved image captioning. CPNet enhances causal intervention by refining feature representations, achieving state-of-the-art performance on benchmark datasets.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Natural Language Processing

    Background:

    • Image captioning models often struggle with causal inference.
    • Existing methods for causal intervention in image captioning have limitations.

    Purpose of the Study:

    • To introduce a novel causal prompting network (CPNet) for enhanced causal intervention in image captioning.
    • To improve the performance and flexibility of causal intervention techniques in image captioning.

    Main Methods:

    • Developed a causal prompting network (CPNet) utilizing visual prompt engineering in the feature space.
    • Employed two visual prompts: causal region of interest (RoI) prompt (CRP) and causal matching prompt (CMP).
    • CRP enhances RoI features with deconfounded causal features; CMP strengthens contextual representation of confounders.

    Main Results:

    • CPNet demonstrated superior performance compared to state-of-the-art (SOTA) image captioning methods.
    • Experiments were conducted on MS-COCO and Flickr30k datasets, validated using the Karpathy split.
    • The proposed method shows significant improvements in causal intervention tasks for image captioning.

    Conclusions:

    • CPNet offers a flexible and adaptable approach to causal intervention in image captioning.
    • The method effectively refines feature representations for better causal understanding.
    • CPNet represents a significant advancement in causal intervention for image captioning systems.