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Topic-Oriented Image Captioning Based on Order-Embedding.

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    |January 1, 2019
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    This summary is machine-generated.

    This study introduces a novel image captioning framework that generates topic-specific captions. The method uses a hierarchical embedding space to improve caption quality and relevance, achieving state-of-the-art results on benchmark datasets.

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    Area of Science:

    • Computer Vision
    • Natural Language Processing
    • Artificial Intelligence

    Background:

    • Image captioning models often generate generic descriptions.
    • Controlling caption content to focus on specific aspects of an image remains a challenge.

    Purpose of the Study:

    • To develop an image captioning framework that generates captions tailored to a given topic.
    • To improve the relevance and quality of generated image captions by incorporating topic information.

    Main Methods:

    • A CNN-based multi-label classifier selects image topics from a corpus.
    • A cross-modal embedding method learns a hierarchical structure for images, topics, and captions using order-embedding.
    • A language model generates captions based on image-topic pairs within a bounded embedding space.

    Main Results:

    • The framework achieves state-of-the-art performance in bidirectional caption-image retrieval on MS-COCO and Flickr30K datasets.
    • Experiments show that different topics yield distinct captions, highlighting specific image aspects.
    • Generated captions demonstrate higher quality compared to models without topic guidance.

    Conclusions:

    • The proposed hierarchical embedding framework effectively integrates topics into image captioning.
    • The method enhances caption specificity and quality, outperforming existing approaches.
    • This framework offers a promising direction for controllable and context-aware image caption generation.