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    The new knowledge-based iterative consensus Visual Question Answering with Natural Language Explanation (VQA-NLE) model improves answer-explanation consistency. It uses iterative generation and knowledge retrieval for more accurate VQA-NLE results.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Visual Question Answering with Natural Language Explanation (VQA-NLE) requires accurate answers and justifiable explanations.
    • Existing VQA-NLE methods lack consistency between answers and explanations.
    • Current approaches fail to integrate external knowledge, limiting semantic understanding.

    Purpose of the Study:

    • To develop a novel VQA-NLE model addressing consistency and knowledge integration.
    • To enhance the accuracy and quality of generated explanations in VQA-NLE tasks.

    Main Methods:

    • Introduced a knowledge-based iterative consensus VQA-NLE (KICNLE) model.
    • Implemented an iterative consensus generator for multi-iteration answer-explanation refinement.
    • Integrated a knowledge retrieval module to bridge the question-image semantic gap.

    Main Results:

    • The KICNLE model demonstrated superior performance over state-of-the-art methods.
    • Achieved improved consistency between generated answers and explanations.
    • Showcased enhanced accuracy in VQA-NLE tasks across three datasets.

    Conclusions:

    • The proposed KICNLE model effectively addresses limitations in existing VQA-NLE approaches.
    • Iterative consensus and knowledge retrieval are crucial for high-quality VQA-NLE.
    • The model offers a promising direction for advancing explainable AI in visual question answering.