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Updated: Jun 11, 2025

Quantifying Fish Swimming Behavior in Response to Acute Exposure of Aqueous Copper Using Computer Assisted Video and Digital Image Analysis
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Video Instance Shadow Detection Under the Sun and Sky.

Zhenghao Xing, Tianyu Wang, Xiaowei Hu

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    ViShadow, a novel semi-supervised framework, enhances video instance shadow detection by using labeled images and unlabeled videos. It improves tracking through contrastive learning and cycle consistency, enabling advanced video editing applications.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Instance shadow detection is vital for image editing and light estimation.
    • Extending shadow detection to videos faces challenges like data annotation, occlusion, and temporary object disappearances.

    Purpose of the Study:

    • Introduce ViShadow, a semi-supervised framework for video instance shadow detection (VISD).
    • Leverage both labeled image and unlabeled video data for robust training.

    Main Methods:

    • A two-stage training pipeline using contrastive learning for cross-frame instance pairing.
    • Incorporating associated cycle consistency loss and a retrieval mechanism for enhanced tracking continuity in unlabeled videos.

    Main Results:

    • ViShadow effectively detects shadow and object instances and their associations in videos.
    • The framework demonstrates improved tracking ability, managing temporary disappearances.
    • Introduced the SOBA-VID dataset and SOAP-VID metric for VISD evaluation.

    Conclusions:

    • ViShadow offers a robust solution for semi-supervised video instance shadow detection.
    • The framework enables advanced video manipulation tasks like inpainting, cloning, and shadow editing.