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Updated: Oct 16, 2025

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Dense Relational Image Captioning via Multi-Task Triple-Stream Networks.

Dong-Jin Kim, Tae-Hyun Oh, Jinsoo Choi

    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    Summary
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    We developed dense relational captioning to generate detailed image descriptions based on object relationships. Our multi-task triple-stream network (MTTSNet) improves caption diversity and understanding by analyzing part-of-speech (POS) tags.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Natural Language Processing

    Background:

    • Image captioning traditionally focuses on object recognition.
    • Understanding relationships between objects is crucial for comprehensive scene interpretation.
    • Existing methods often lack detailed relational descriptions.

    Purpose of the Study:

    • To introduce dense relational captioning for generating richer, relationship-aware image descriptions.
    • To leverage part-of-speech (POS) information to guide caption generation.
    • To develop a model capable of both caption generation and POS understanding.

    Main Methods:

    • Proposed the multi-task triple-stream network (MTTSNet) with three recurrent units for subject, object, and predicate.
    • Jointly trained the network to predict captions and the POS of each word.
    • Incorporated an explicit relational module to modulate object embeddings.

    Main Results:

    • MTTSNet generates more diverse and informative captions compared to baseline methods.
    • The model effectively learns to predict part-of-speech (POS) tags for words within captions.
    • Performance improvements were observed by integrating the explicit relational module.

    Conclusions:

    • Dense relational captioning offers a more comprehensive approach to image understanding.
    • MTTSNet demonstrates the efficacy of incorporating relational information and POS tagging.
    • The framework shows potential for applications in holistic captioning, scene graph generation, and retrieval.