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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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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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