Abstract
Due to the large domain shift and the discriminative feature learning with unlabeled datasets, unsupervised domain adaptation (UDA) for person re-identification (re-ID) still remains a challenging task. Some current methods adopt a clustering-based strategy to assign pseudo labels to the unlabeled samples in target domains for classification. However, the rich knowledge of the model in different training stages is not fully utilized in those methods and the pseudo labels generated by clustering algorithms inevitably contain noise, which would limit the performance of re-ID models. To tackle this problem, a Knowledge Compensation Network with Divisible feature learning (KCND) is proposed in this paper, which aggregates the past-to-present knowledge of models from training samples for discriminative feature learning and resists the label noise produced by clustering. Also, a novel compensation-guided softened loss is developed to enhance the generalization and robustness of re-ID models. Our experimental results on large-scale datasets (Market-1501, DukeMTMC-reID and MSMT17) have demonstrated the performance of KCND is better than other methods in terms of the mAP and CMC accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Luo, H., Jiang, W., Zhang, X., Fan, X., Qian, J., Zhang, C.: AlignedReID++: dynamically matching local information for person re-identification. Pattern Recogn. 94, 53–61 (2019)
Qi, L., Wang, L., Huo, J., Zhou, L., Shi, Y., Gao, Y.: A novel unsupervised camera-aware domain adaptation framework for person re-identification. In: ICCV (2019)
Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., Jiao, J.: Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: CVPR (2018)
Huang, Y., Wu, Q., Xu, J., Zhong, Y.: SBSGAN: suppression of inter-domain background shift for person re-identification. In: ICCV (2019)
Zhai, Y., et al.: Ad-cluster: augmented discriminative clustering for domain adaptive person re-identification. In: CVPR (2020)
Fu, Y., Wei, Y., Wang, G., Zhou, Y., Shi, H., Huang, T.: Self-similarity grouping: a simple unsupervised cross domain adaptation approach for person re-identification. In: ICCV (2019)
Ge, Y., Chen, D., Li, H.: Mutual mean-teaching: pseudo label refinery for unsupervised domain adaptation on person re-identification. In: ICLR (2020)
Wang, G., Lai, J., Liang, W., Wang, G.: Smoothing adversarial domain attack and P-memory reconsolidation for cross-domain person re-identification. In: CVPR (2020)
Zhao, F., Liao, S., Xie, G.-S., Zhao, J., Zhang, K., Shao, L.: Unsupervised domain adaptation with noise resistible mutual-training for person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 526–544. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_31
Zou, Y., Yang, X., Yu, Z., Kumar, B.V.K.V., Kautz, J.: Joint disentangling and adaptation for cross-domain person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 87–104. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58536-5_6
Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD (1996)
Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer GAN to bridge domain gap for person re-identification. In: CVPR (2018)
Zhou, S., Wang, Y., Zhang, F., Wu, J.: Cross-view similarity exploration for unsupervised cross-domain person re-identification. Neural Comput. Appl. 33(9), 4001–4011 (2021). https://doi.org/10.1007/s00521-020-05566-3
Chen, G., Lu, Y., Lu, J., Zhou, J.: Deep credible metric learning for unsupervised domain adaptation person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 643–659. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58598-3_38
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)
Zhong, Z., Zheng, L., Zheng, Z., Li, S., Yang, Y.: Camera style adaptation for person re-identification. In: CVPR (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR (2009)
Szegedy, C., Vanhoucke, V., Loffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: ICCV (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
Zhong, Z., Zheng, L., Luo, Z., Li, S., Yang, Y.: Invariance matters: Exemplar memory for domain adaptive person re-identification. In: CVPR (2019)
Yu, H., Zheng, W., Wu, A., Guo, X., Gong, S., Lai, J.: Unsupervised person re-identification by soft multilabel learning. In: CVPR (2019)
Zhang, X., Cao, J., Shen, C., You, M.: Self-training with progressive augmentation for unsupervised cross-domain person re-identification. In: ICCV (2019)
Wang, J., Zhu, X., Gong, S., Li, W.: Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: CVPR (2018)
Li, J., Zhang, S.: Joint visual and temporal consistency for unsupervised domain adaptive person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 483–499. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58586-0_29
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Hong, J., Zhang, Y., Zhu, Y. (2021). Knowledge Compensation Network with Divisible Feature Learning for Unsupervised Domain Adaptive Person Re-identification. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13032. Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-89363-7_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89362-0
Online ISBN: 978-3-030-89363-7
eBook Packages: Computer ScienceComputer Science (R0)