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    This study introduces a new framework for cloth-changing person re-identification (ReID) using Contrastive Language-Image Pre-training (CLIP). The proposed method enhances identity perception and integrates multi-scale features for improved accuracy in dynamic scenarios.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Contrastive Language-Image Pre-training (CLIP) shows promise in person re-identification (ReID) due to its cross-modal understanding.
    • Clothing variations significantly challenge person ReID accuracy, particularly in cloth-changing scenarios (CC ReID).
    • Direct application of CLIP in CC ReID is limited by its focus on static attributes like clothing.

    Purpose of the Study:

    • To propose a novel CLIP-based multi-modal feature learning framework (CMFF) specifically designed for cloth-changing person re-identification (CC ReID).
    • To address the limitations of existing CLIP models in handling dynamic feature changes caused by clothing variations.
    • To enhance the accuracy and robustness of person identification in scenarios with changing attire.

    Main Methods:

    • Developed a pose-aware identity enhancement module (PIE) to strengthen intrinsic identity features and reduce clothing interference using a ranking loss.
    • Introduced a global-local hybrid attention module (GLHA) to fuse head and global features via cross-attention, improving recognition of key head information.
    • Proposed a graph-based multi-layer interactive enhancement module (GMIE) to integrate multi-layer features, boosting contextual awareness of multi-scale representations.

    Main Results:

    • The proposed CMFF framework demonstrates superior performance on multiple benchmark pedestrian datasets for CC ReID.
    • The PIE module effectively enhances identity-specific information while mitigating the impact of clothing variations.
    • The GLHA and GMIE modules contribute to improved global recognition and multi-scale feature understanding, respectively.

    Conclusions:

    • The CMFF framework offers a significant advancement in addressing the challenges of cloth-changing person re-identification.
    • Integrating pose-aware enhancement, hybrid attention, and multi-layer feature interaction leads to state-of-the-art performance.
    • This approach provides a robust solution for person identification systems operating in dynamic and challenging environments.