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Related Experiment Video

Updated: Dec 29, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Image Captioning with End-to-end Attribute Detection and Subsequent Attributes Prediction.

Yiqing Huang, Jiansheng Chen, Wanli Ouyang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 4, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces novel modules for image captioning, enhancing attribute detection and prediction for more accurate and explainable results. The approach achieves state-of-the-art performance on the MSCOCO dataset.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Natural Language Processing

    Background:

    • Semantic attention improves image captioning by focusing on important attributes.
    • Previous methods suffer from independent attribute detection and captioning, leading to suboptimal semantic information usage.
    • Attending to all detected attributes, regardless of linguistic context, can disrupt captioning accuracy.

    Purpose of the Study:

    • To develop end-to-end trainable modules that tightly integrate attribute detection with image captioning.
    • To improve the utilization of semantic information by predicting relevant attributes at each generation step.
    • To enhance the explainability of attribute influence on generated captions.

    Main Methods:

    • Introduced the Multimodal Attribute Detector (MAD) module, leveraging image features and attribute word embeddings for improved detection accuracy.
    • Developed the Selective Attribute Predictor (SAP) module to dynamically select a concise subset of attributes at each time step.
    • Enabled end-to-end training of both MAD and SAP modules with the captioning network.

    Main Results:

    • Achieved a state-of-the-art single model performance of 128.8 CIDEr-D on the MSCOCO dataset.
    • Demonstrated simultaneous improvements in both image captioning and attribute detection accuracy.
    • Showcased enhanced explainability in how attributes influence word generation.

    Conclusions:

    • The proposed integrated approach effectively couples attribute detection and image captioning.
    • Dynamic attribute prediction mitigates attribute diversity and improves captioning relevance.
    • The method offers a significant advancement in explainable and high-performing image captioning systems.