Abstract
Most existing unsupervised re-identification uses a clustering-based approach to generate pseudo-labels as supervised signals, allowing deep neural networks to learn discriminative representations without annotations. However, drawbacks in clustering algorithms and the absence of discriminatory ability early in training limit better performance seriously. A severe problem arises from path dependency, wherein noisy samples rarely have a chance to escape from their assigned clusters during iterative training. To tackle this challenge, we propose a novel label refinement strategy based on the stable cluster reconstruction. Our approach contains two modules, the stable cluster reconstruction (SCR) module and the similarity recalculate (SR) module. It reconstructs more stable clusters and re-evaluates the relationship between samples and clearer cluster representatives, providing complementary information for pseudo labels at the instance level. Our proposed approach effectively improves unsupervised reID performance, achieving state-of-the-art performance on four benchmark datasets. Specifically, our method achieves 46.0% and 39.1% mAP on the challenging dataset VeRi776 and MSMT17.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ming, Z., et al.: Deep learning-based person re-identification methods: a survey and outlook of recent works. Image Vis. Comput. 119, 104394 (2022)
He, S., Luo, H., Wang, P., Wang, F., Li, H., Jiang, W.: TransReID: transformer-based object re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15013–15022 (2021)
Chen, Y., Wang, H., Sun, X., Fan, B., Tang, C., Zeng, H.: Deep attention aware feature learning for person re-identification. Pattern Recogn. 126, 108567 (2022)
Wang, D., Zhang, S.: Unsupervised person re-identification via multi-label classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10981–10990 (2020)
Fu, Y., Wei, Y., Wang, G., Zhou, Y., Shi, H., Huang, T.S.: Self-similarity grouping: a simple unsupervised cross domain adaptation approach for person re-identification. In: proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6112–6121 (2019)
Zhai, Y., et al.: AD-Cluster: augmented discriminative clustering for domain adaptive person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9021–9030 (2020)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)
Ge, Y., Zhu, F., Chen, D., Zhao, R., et al.: Self-paced contrastive learning with hybrid memory for domain adaptive object re-id. Adv. Neural. Inf. Process. Syst. 33, 11309–11321 (2020)
Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)
He, T., Shen, L., Guo, Y., Ding, G., Guo, Z.: Secret: Self-consistent pseudo label refinement for unsupervised domain adaptive person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 879–887 (2022)
Ge, Y., Chen, D., Li, H.: Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification. arXiv preprint arXiv:2001.01526 (2020)
Lin, Y., Xie, L., Wu, Y., Yan, C., Tian, Q.: Unsupervised person re-identification via softened similarity learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3390–3399 (2020)
Lin, Y., Dong, X., Zheng, L., Yan, Y., Yang, Y.: A bottom-up clustering approach to unsupervised person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8738–8745 (2019)
Chen, H., Lagadec, B., Bremond, F.: Ice: inter-instance contrastive encoding for unsupervised person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14960–14969 (2021)
Cho, Y., Kim, W.J., Hong, S., Yoon, S.E.: Part-based pseudo label refinement for unsupervised person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7308–7318 (2022)
Lian, J., Wang, D.H., Du, X., Wu, Y., Zhu, S.: Exploiting robust memory features for unsupervised reidentification. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol. 13535, pp. 655–667. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-18910-4_52
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Adv. Neural. Inf. Process. Syst. 33, 9912–9924 (2020)
Dai, Z., Wang, G., Yuan, W., Zhu, S., Tan, P.: Cluster contrast for unsupervised person re-identification. In: Proceedings of the Asian Conference on Computer Vision, pp. 1142–1160 (2022)
Zhang, X., Ge, Y., Qiao, Y., Li, H.: Refining pseudo labels with clustering consensus over generations for unsupervised object re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3436–3445 (2021)
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)
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
Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer GAN to bridge domain gap for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 79–88 (2018)
Liu, X., Liu, W., Mei, T., Ma, H.: A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 869–884. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_53
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, pp. 13001–13008 (2020)
Zhang, X., et al.: Implicit sample extension for unsupervised person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7369–7378 (2022)
Acknowledgement
This work is supported by Industry-University Cooperation Project of Fujian Science and Technology Department (No. 2021H6035), the Science and Technology Planning Project of Fujian Province (No. 2021J011191), and Fujian Key Technological Innovation and Industrialization Projects (No. 2023XQ023), and Fu-Xia-Quan National Independent Innovation Demonstration Project (No. 2022FX4).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, Z. et al. (2024). Pseudo Labels Refinement with Stable Cluster Reconstruction for Unsupervised Re-identification. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_17
Download citation
DOI: https://doi.org/10.1007/978-981-99-8462-6_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8461-9
Online ISBN: 978-981-99-8462-6
eBook Packages: Computer ScienceComputer Science (R0)