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Cross-modal group-relation optimization for visible-infrared person re-identification.

Jianqing Zhu1, Hanxiao Wu2, Yutao Chen1

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Summary

This study introduces a new method for visible-infrared person re-identification (VIPR) that optimizes cross-modal group relations to reduce appearance discrepancies. The approach significantly improves identification accuracy by minimizing differences between visible and infrared images.

Keywords:
Artificial intelligenceGroup-correlationModal-discrepancyVisible–infrared person re-identification

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Visible-infrared person re-identification (VIPR) is crucial for intelligent transportation systems.
  • Modal discrepancies between visible and infrared images hinder accurate person appearance discrimination.
  • Existing methods often require complex networks to disentangle modal and appearance differences.

Purpose of the Study:

  • To propose a novel method for optimizing modal discrepancies in VIPR without complex disentanglement.
  • To introduce a cross-modal group-relation (CMGR) metric for measuring modal discrepancies.
  • To develop a group-relation correlation (GRC) loss function to optimize CMGR.

Main Methods:

  • Developed a cross-modal group-relation (CMGR) metric to capture relationships between individuals across visible and infrared modalities.
  • Designed a group-relation correlation (GRC) loss function, utilizing Pearson correlations, to optimize CMGR.
  • Integrated CMGR as a training-phase constraint to minimize modal discrepancies.

Main Results:

  • The CMGR method demonstrated superior performance compared to state-of-the-art approaches on RegDB and SYSU-MM01 datasets.
  • Achieved a notable improvement of over 7% in rank-1 identification rate on the RegDB dataset when using CMGR.
  • The CMGR model effectively minimizes modal discrepancies without requiring execution during inference.

Conclusions:

  • The proposed CMGR method offers an effective and efficient solution for addressing modal discrepancies in VIPR.
  • Optimizing cross-modal group relations provides a promising direction for enhancing person re-identification accuracy.
  • CMGR serves as a valuable training constraint, improving VIPR system performance significantly.