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Related Experiment Video

Updated: Dec 7, 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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TapLab: A Fast Framework for Semantic Video Segmentation Tapping Into Compressed-Domain Knowledge.

Junyi Feng, Songyuan Li, Xi Li

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 28, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces TapLab, a novel framework for real-time semantic video segmentation that leverages compressed video data. TapLab significantly accelerates inference speed by using motion and residual information, enabling faster video analysis.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Video Processing

    Background:

    • Real-time semantic video segmentation demands high inference speed.
    • Existing methods focus on model size reduction for efficiency.
    • Compressed video data offers untapped potential for acceleration.

    Purpose of the Study:

    • To propose a novel framework, TapLab, for efficient real-time semantic video segmentation.
    • To exploit knowledge from the compressed video domain to reduce computational load.
    • To improve inference speed without substantial accuracy loss.

    Main Methods:

    • Developed TapLab, a framework utilizing compressed video resources.
    • Implemented a fast feature warping module with motion vectors.
    • Introduced residual-guided correction and frame selection modules to mitigate noise.
    • Reduced redundant computations in state-of-the-art semantic segmentation models.

    Main Results:

    • TapLab achieves 3-10x speedup over existing fast semantic segmentation models.
    • Demonstrated 70.6% mIoU at 99.8 FPS on Cityscapes dataset (1024x2048 videos) using a single GPU.
    • A high-speed variant reached over 160 FPS.
    • Controllable accuracy degradation was observed.

    Conclusions:

    • TapLab offers an effective approach to accelerate real-time semantic video segmentation.
    • Leveraging compressed domain information is a viable strategy for efficient video analysis.
    • The framework provides a significant speed advantage with manageable accuracy trade-offs.