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GAN-Based Data Augmentation and Pseudo-label Refinement for Unsupervised Domain Adaptation Person Re-identification

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Computational Collective Intelligence (ICCCI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14162))

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Abstract

Person re-identification (re-ID) by using an unsupervised domain adaptation (UDA) approach has drawn considerable attention in contemporary security research. Thus, UDA person re-ID usually employs a model learned from a labeled source domain, adjusted by pseudo-labels, for an unlabeled target domain. However, this method still needs to overcome two main challenges: a significant gap between the source and target domains and the accuracy of pseudo-labels generated by a clustering algorithm. To address these problems, we propose a novel method to improve UDA person re-ID performance by combining GAN-based Data Augmentation and Unsupervised Pseudo-Label Editation methods for training on Target Domain, named DAUET. In particular, we first use a generative adversarial network (GAN) method to bridge the distribution of the source and target domains. Then we propose a supervised learning approach to maximize the benefits of the virtual dataset. Finally, we utilize a pseudo-label refinement to enhance the unsupervised learning process. Extensive experiments on two popular datasets, Market-1501 and DukeMTMC-reID, indicate that our DAUET method can substantially outperform the state-of-the-art performance of the UDA person re-ID.

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References

  1. Bai, Z., Wang, Z., Wang, J., Hu, D., Ding, E.: Unsupervised multi-source domain adaptation for person re-identification. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12909–12918 (2021). https://doi.org/10.1109/CVPR46437.2021.01272

  2. Chen, H., Wang, Y., Lagadec, B., Dantcheva, A., Bremond, F.: Learning invariance from generated variance for unsupervised person re-identification. IEEE Trans. Pattern Anal. Mach. Intell. 1–15 (2022). https://doi.org/10.1109/TPAMI.2022.3226866

  3. Cho, Y., Kim, W.J., Hong, S., Yoon, S.E.: Part-based pseudo label refinement for unsupervised person re-identification. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7298–7308 (2022). https://doi.org/10.1109/CVPR52688.2022.00716

  4. Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8789–8797 (2018). https://doi.org/10.1109/CVPR.2018.00916

  5. Deng, D.: DBSCAN clustering algorithm based on density. In: 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), pp. 949–953 (2020). https://doi.org/10.1109/IFEEA51475.2020.00199

  6. Ge, Y., Chen, D., Li, H.: Mutual mean-teaching: pseudo label refinery for unsupervised domain adaptation on person re-identification. In: International Conference on Learning Representations (2020). https://openreview.net/forum?id=rJlnOhVYPS

  7. He, T., Shen, L., Guo, Y., Ding, G., Guo, Z.: Secret: Self-consistent pseudo label refinement for unsupervised domain adaptive person re-identification, vol. 36, pp. 879–887, June 2022. https://doi.org/10.1609/aaai.v36i1.19970

  8. Le, H.V., Nguyen, T.N., Nguyen, H.N., Le, L.: An efficient hybrid webshell detection method for webserver of marine transportation systems. IEEE Trans. Intell. Transp. Syst. 24(2), 2630–2642 (2023). https://doi.org/10.1109/TITS.2021.3122979

    Article  Google Scholar 

  9. Liu, X., Zhang, S.: Domain adaptive person re-identification via coupling optimization. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 547–555. MM ’20, ACM (2020). https://doi.org/10.1145/3394171.3413904

  10. Miao, Y., Deng, J., Ding, G., Han, J.: Confidence-guided centroids for unsupervised person re-identification (2022). https://doi.org/10.48550/ARXIV.2211.11921

  11. Nguyen, A.D., Nguyen, D.T., Du, H.P., Dao, H.N., Nguyen, H.N.: EnsFace: an ensemble method of deep convolutional neural networks with novel effective loss functions for face recognition. In: The 11th International Symposium on Information and Communication Technology, pp. 231–238. SoICT 2022, ACM (2022). https://doi.org/10.1145/3568562.3568638

  12. Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17–35. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_2

    Chapter  Google Scholar 

  13. Shahapure, K.R., Nicholas, C.: Cluster quality analysis using silhouette score. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 747–748 (2020). https://doi.org/10.1109/DSAA49011.2020.00096

  14. Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1195–1204 (2017). https://doi.org/10.5555/3294771.3294885

  15. Tu, Y.: Domain camera adaptation and collaborative multiple feature clustering for unsupervised person re-id. In: Proceedings of the 3rd International Workshop on Human-Centric Multimedia Analysis, pp. 51–59. ACM (2022). https://doi.org/10.1145/3552458.3556446

  16. Yang, F., Zhong, Z., Luo, Z., Lian, S., Li, S.: Leveraging virtual and real person for unsupervised person re-identification. IEEE Trans. Multimed. 22(9), 2444–2453 (2020). https://doi.org/10.1109/TMM.2019.2957928

    Article  Google Scholar 

  17. Zhai, Y., Ye, Q., Lu, S., Jia, M., Ji, R., Tian, Y.: Multiple expert brainstorming for domain adaptive person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12352, pp. 594–611. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58571-6_35

    Chapter  Google Scholar 

  18. Zhang, X., et al.: Implicit sample extension for unsupervised person re-identification. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7359–7368, June 2022. https://doi.org/10.1109/CVPR52688.2022.00722

  19. Zheng, K., Lan, C., Zeng, W., Zhang, Z., Zha, Z.J.: Exploiting sample uncertainty for domain adaptive person re-identification, vol. 35, pp. 3538–3546, May 2021. https://doi.org/10.1609/aaai.v35i4.16468

  20. Zheng, K., Liu, W., He, L., Mei, T., Luo, J., Zha, Z.J.: Group-aware label transfer for domain adaptive person re-identification. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5306–5315 (2021). https://doi.org/10.1109/CVPR46437.2021.00527

  21. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1116–1124 (2015). https://doi.org/10.1109/ICCV.2015.133

  22. Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 13001–13008 (2020). https://doi.org/10.1609/aaai.v34i07.7000

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Authors and Affiliations

  1. VNU University of Engineering and Technology, Hanoi, Vietnam

    Anh D. Nguyen, Dang H. Pham & Hoa N. Nguyen

  2. University of Khanh Hoa, Khanh Hoa, Vietnam

    Dang H. Pham

Authors
  1. Anh D. Nguyen
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  2. Dang H. Pham
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  3. Hoa N. Nguyen
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Corresponding author

Correspondence to Hoa N. Nguyen .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Eötvös Loránd University, Budapest, Hungary

    János Botzheim

  3. Eötvös Loránd University, Budapest, Hungary

    László Gulyás

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  6. Universität Münster, Münster, Germany

    Gottfried Vossen

  7. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

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Nguyen, A.D., Pham, D.H., Nguyen, H.N. (2023). GAN-Based Data Augmentation and Pseudo-label Refinement for Unsupervised Domain Adaptation Person Re-identification. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_45

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  • DOI: https://doi.org/10.1007/978-3-031-41456-5_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41455-8

  • Online ISBN: 978-3-031-41456-5

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