Contrastive viewpoint-aware shape learning for long-term person re-identification

This repository contains research code for CVSL (Contrastive Viewpoint-aware Shape Learning), a Long-term Person Re-Identification (LRe-ID) method that improves robustness to clothing changes and viewpoint variations by combining appearance cues with texture-invariant body shape cues.

Problem

Classic Re-ID methods rely on appearance. In long-term scenarios this breaks down when:

• the same person changes clothes / hairstyle, or their face is occluded;
• different people wear similar clothes.

In addition, viewpoint shifts (front/side/back) can cause both texture and shape embeddings to drift, creating false matches.

Method: CVSL

CVSL has two feature branches and learns them with viewpoint-aware objectives.

1) Relational Shape Embedding (RSE) branch

• Extracts 2D pose keypoints and encodes them as a graph.
• Uses a refinement MLP to lift raw joint coordinates to a higher-dimensional space.
• Uses a Graph Attention Network (GAT) over the skeleton graph to capture local part relations and higher-order shape structure.
• Produces a global shape embedding via global pooling.

2) Texture (appearance) branch

• Uses a CNN backbone (ResNet-50 in the paper) to extract appearance features.
• Uses clothing-aware objectives to discourage over-reliance on clothing texture.

3) Contrastive Viewpoint-aware Losses (CVL)

• Shape CVL: positive pairs are the same identity across different viewpoints; negatives are different identities under the same viewpoint.
• Appearance CVL: encourages cross-view consistency and includes a hard-mined component to handle look-alike clothing cases.

4) Adaptive Fusion Module (AFM)

Instead of naive concatenation, AFM projects shape/appearance features to a shared space and learns adaptive weights:

Results (cloth-changing setting)

Reported in the paper:

• LTCC: Rank-1 44.5, mAP 21.3
• PRCC: Rank-1 57.5, mAP 56.9

📄 Citation

If you use this code in your research, please cite:

@InProceedings{Nguyen_2024_WACV,
    author    = {Vuong D. Nguyen, Khadija Khaldi, Dung Nguyen, Pranav Mantini, Shishir Shah},
    title     = {Contrastive Viewpoint-Aware Shape Learning for Long-Term Person Re-Identification},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    url       = {https://openaccess.thecvf.com/content/WACV2024/html/Nguyen_Contrastive_Viewpoint-Aware_Shape_Learning_for_Long-Term_Person_Re-Identification_WACV_2024_paper.html}
}

Paper Link: WACV 2024 Open Access

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

Keywords: Person Re-identification, Long-term ReID, Cloth-changing, Pose estimation, Graph Neural Networks, Contrastive Learning

Recommended citation: V. D. Nguyen, K. Khaldi, D. Nguyen, P. Mantini, and S. Shah, “Contrastive viewpoint-aware shape learning for long-term person re-identification,” in Proc. IEEE/CVF Winter Conf. on Applications of Computer Vision (WACV), 2024, pp. 1030–1038, doi: 10.1109/WACV57701.2024.00108.
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