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Soft Supervision Guided Spatial-Temporal Refinement Network For Video-based Visible-Infrared Person

Jinxing Li, Chuhao Zhou, Rundong Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 29, 2026
    PubMed
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    This study introduces a new method for video-based cross-modal person re-identification (Re-ID) using the HITSZ-PVCM dataset. The Soft Supervision guided Spatial-Temporal Refinement (S3TR) network improves pedestrian recognition across different camera modes.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Person re-identification (Re-ID) enables tracking individuals across cameras using visible and infrared modes.
    • Video-based Re-ID offers richer appearance details than still images but faces challenges in capturing fine-grained information.
    • Existing methods often lose details and struggle with intra-class variations, limiting model generalization.

    Purpose of the Study:

    • To develop a novel network for video-based cross-modal person Re-ID.
    • To address the loss of fine-grained details in temporal representations.
    • To improve model generalization by overcoming limitations of traditional metric losses.

    Main Methods:

    • Introduction of the Soft Supervision guided Spatial-Temporal Refinement (S3TR) network.

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  • Frame refinement guided by coarse temporal features for discriminative feature extraction.
  • Global-local mutual learning to bridge the modality gap and a novel soft-clustering center loss for improved generalization.

    • Main Results:

    • The proposed S3TR network effectively refines features and captures fine-grained details.
    • The global-local mutual learning module successfully reduces the modality gap.
    • The soft-clustering center loss enhances model generalization by considering group-wise similarities.

    Conclusions:

    • S3TR achieves superior performance in video-based cross-modal person Re-ID.
    • The HITSZ-PVCM dataset is the largest to date for this task.
    • The proposed methods offer a significant advancement in person re-identification accuracy and generalization.