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SAN: Selective Alignment Network for Cross-Domain Pedestrian Detection.

Yifan Jiao, Hantao Yao, Changsheng Xu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 20, 2021
    PubMed
    Summary
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    This study introduces a Selective Alignment Network for cross-domain pedestrian detection, improving accuracy by aligning image and instance-level features separately. The novel approach enhances pedestrian detection across different data distributions.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Cross-domain pedestrian detection addresses challenges where training and testing data have different distributions.
    • Current methods aligning entire instances struggle with significant inter-instance visual variations.
    • Aligning individual instance types is proposed as a more effective strategy.

    Purpose of the Study:

    • To propose a novel Selective Alignment Network (SAN) for improved cross-domain pedestrian detection.
    • To address the limitations of whole-instance alignment by introducing instance-level adaptation.
    • To enhance the robustness of pedestrian detection across diverse visual domains.

    Main Methods:

    • The proposed Selective Alignment Network (SAN) integrates a Base Detector, Image-Level Adaptation Network, and Instance-Level Adaptation Network.

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  • Image-level adaptation aligns global image descriptions using an adversarial domain classifier.
  • Instance-level adaptation clusters source proposals, generates pseudo-labels, and aligns domains iteratively.

    • Main Results:

    • The Image-Level Adaptation Network performs global alignment, while the Instance-Level Adaptation Network handles local alignment.
    • The Instance-Level Adaptation Network clusters proposals and uses pseudo-labels for domain alignment.
    • Extensive evaluations on multiple benchmarks validate the proposed method's effectiveness.

    Conclusions:

    • The Selective Alignment Network effectively improves cross-domain pedestrian detection by incorporating both global and local alignment strategies.
    • Instance-level alignment, by considering visual feature clusters, proves more effective than whole-instance alignment.
    • The method demonstrates significant improvements in detecting pedestrians across different data distributions.