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Modeling Noisy Annotations for Point-Wise Supervision.

Jia Wan, Qiangqiang Wu, Antoni B Chan

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    This study introduces robust loss functions to address noise in point-wise annotations for computer vision tasks like crowd counting and pose estimation, improving algorithm performance and reliability.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Data Annotation

    Background:

    • Point-wise supervision is common in computer vision but vulnerable to annotation noise.
    • Noise in annotations, including spatial shifts, missing points, and duplicates, degrades algorithm performance.

    Purpose of the Study:

    • To investigate the impact of annotation noise on point-wise supervision.
    • To develop novel, robust loss functions mitigating various noise types in computer vision tasks.

    Main Methods:

    • Modeled spatial-shift noise using probability density functions and negative log-likelihood loss.
    • Empirically modeled missing-point and duplicate-point noise in high-density regions.
    • Applied proposed loss functions to crowd counting, human pose estimation, and visual tracking.

    Main Results:

    • Achieved superior performance and robustness across benchmark datasets.
    • Demonstrated the effectiveness of proposed loss functions in handling annotation noise.
    • Successfully applied to diverse computer vision applications.

    Conclusions:

    • The proposed robust loss functions effectively address spatial-shift, missing-point, and duplicate-point noise.
    • This work enhances the reliability and performance of point-wise supervised computer vision models.
    • The methods offer significant improvements for crowd counting, pose estimation, and visual tracking.