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Hierarchical LSTMs with Adaptive Attention for Visual Captioning.

Lianli Gao, Xiangpeng Li, Jingkuan Song

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 23, 2019
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    This study introduces a hierarchical LSTM with adaptive attention (hLSTMat) for image and video captioning, improving performance by focusing attention on relevant visual words and adapting to context.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Natural Language Processing

    Background:

    • Attention-based encoder-decoder models are common for image and video captioning.
    • Existing methods apply attention to all words, including non-visual ones, potentially hindering performance.
    • Hierarchical LSTMs offer richer visual data representation.

    Purpose of the Study:

    • To propose a novel hierarchical LSTM with adaptive attention (hLSTMat) framework.
    • To enhance image and video captioning by optimizing attention mechanisms.
    • To improve the accuracy and relevance of generated captions.

    Main Methods:

    • Developed a hierarchical LSTM architecture with adaptive attention (hLSTMat).
    • Implemented spatial/temporal attention for region/frame selection.
    • Incorporated adaptive attention to balance visual and language context.
    • Designed a general framework instantiated for video and image captioning.

    Main Results:

    • Achieved state-of-the-art performance on video captioning tasks.
    • Demonstrated competitive results on image captioning tasks.
    • Ablation studies confirmed the effectiveness of key components.

    Conclusions:

    • The hLSTMat framework effectively improves image and video captioning.
    • Adaptive attention and hierarchical structure are crucial for performance.
    • The proposed method offers a significant advancement in visual captioning.