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Related Experiment Video

Updated: Jun 28, 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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Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning.

Yiran Li, Junpeng Wang, Prince Aboagye

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    This study introduces a visual analytics system for exploring large image datasets and evaluating image captions. It helps identify data biases and improve language-image model captioning through interactive exploration.

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    Area of Science:

    • Computer Vision
    • Data Science
    • Human-Computer Interaction

    Background:

    • Pre-trained language-image models offer advanced visual comprehension capabilities.
    • Visual analytics faces challenges in exploring large image datasets and assessing caption quality.
    • Identifying and mitigating data biases in image datasets is crucial for reliable AI.

    Purpose of the Study:

    • To develop a visual analytics system for efficient exploration of large-scale image datasets.
    • To enable the identification and understanding of data biases within image collections.
    • To evaluate and steer the caption generation process of language-image models.

    Main Methods:

    • Visual examination of captions generated by language-image models for dataset bias detection.
    • Analysis of associations between visual features and textual captions to expose model weaknesses.
    • Development of an interactive interface for steering image caption generation.
    • Integration of visual and textual analysis into a coordinated system.

    Main Results:

    • The system facilitates deeper insights into visual content and unearths entrenched data biases.
    • Weaknesses in pre-trained language-image models' captioning capabilities are exposed.
    • An interactive interface effectively steers the caption generation process.
    • The system fosters mutual enrichment between visual and textual data.

    Conclusions:

    • The developed visual analytics system is effective for exploring large image datasets and evaluating image captions.
    • The system aids in identifying data biases and improving language-image model performance.
    • Domain practitioners validated the system's effectiveness through case studies.