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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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Instance Annotation via Optimal BoW for Weakly Supervised Object Localization.

Liantao Wang, Deyu Meng, Xuelei Hu

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    This study introduces a novel instance annotation scheme for improved irregular-shape object localization using weakly supervised learning. The method enhances localization accuracy by considering all positive instances within a bag, outperforming existing approaches.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Object localization under weak supervision is challenging, especially for irregular shapes.
    • Existing multiple-instance learning methods often yield imprecise localization by focusing on a single positive instance.

    Purpose of the Study:

    • To develop an effective scheme for instance annotation in irregular-shape object localization.
    • To improve the precision and completeness of localization in weakly supervised settings.

    Main Methods:

    • Proposed a novel instance annotation scheme labeling all positive instances within each bag.
    • Leveraged bag-of-words (BoW) at the instance level to model class distributions.
    • Integrated BoW learning and instance labeling into a unified optimization framework.

    Main Results:

    • The proposed scheme effectively addresses imprecise and incomplete localization issues.
    • Demonstrated suitability for weakly supervised object localization of irregular-shape objects.
    • Experimental results show superior performance compared to existing methods for instance annotation and localization.

    Conclusions:

    • The developed instance annotation scheme significantly enhances weakly supervised object localization for irregular shapes.
    • The integrated approach offers a robust solution for both generic instance annotation and specific localization tasks.