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Chinese Image Caption Generation via Visual Attention and Topic Modeling.

Maofu Liu, Huijun Hu, Lingjun Li

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    |June 23, 2020
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    This study introduces a new model for Chinese image captioning that uses visual attention and topic modeling to generate more accurate and diverse descriptions. The NICVATP2L model enhances natural language generation for images, outperforming existing methods.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Automatic image captioning bridges visual content and text, a complex AI challenge.
    • Existing deep learning models (NIC) face issues like descriptive deviation, low scene accuracy, and monotonous outputs.
    • Current datasets and methods predominantly focus on English, necessitating specialized Chinese approaches.

    Purpose of the Study:

    • To develop an advanced Chinese image captioning model addressing current limitations.
    • To improve the accuracy, diversity, and naturalness of generated Chinese captions.
    • To reduce the deviation between image content and generated descriptions.

    Main Methods:

    • A novel NICVATP2L model integrating visual attention and topic modeling.
    • Utilizing Convolutional Neural Network (CNN) for visual feature extraction and topic modeling for topic features.
    • Employing an attention mechanism for visual region features and a two-layer Long Short-Term Memory (LSTM) network for caption generation.

    Main Results:

    • The model successfully generates more informative and descriptive Chinese captions.
    • Experimental results on the Chinese AIC-ICC dataset demonstrate superior performance compared to existing NIC models.
    • The integration of visual attention and topic modeling effectively addresses captioning challenges.

    Conclusions:

    • The NICVATP2L model offers a significant advancement in Chinese automatic image captioning.
    • The proposed method generates more natural, accurate, and diverse captions.
    • This work highlights the importance of specialized models for different linguistic contexts in AI.