Abstract
Although the single-domain person re-identification (Re-ID) method has achieved great accuracy, the dependence on the label in the same image domain severely limits the scalability of this method. Therefore, cross-domain Re-ID has received more and more attention. In this paper, a novel cross-domain Re-ID method combining supervised and unsupervised learning is proposed, which includes two models: a triple-condition generative adversarial network (TC-GAN) and a dual-task feature extraction network (DFE-Net). We first use TC-GAN to generate labeled images with the target style, and then we combine supervised and unsupervised learning to optimize DFE-Net. Specifically, we use labeled generated data for supervised learning. In addition, we mine effective information in the target data from two perspectives for unsupervised learning. To effectively combine the two types of learning, we design a dynamic weighting function to dynamically adjust the weights of these two approaches. To verify the validity of TC-GAN, DFE-Net, and the dynamic weight function, we conduct multiple experiments on Market-1501 and DukeMTMC-reID. The experimental results show that the dynamic weight function can improve the performance of the models, and our method is better than many state-of-the-art methods.
Similar content being viewed by others
References
Li R, Zhang B, Teng Z, Fan J (2020) A divide-and-unite deep network for person re-identification. Appl Intell:1–13
Yin J, Fan Z, Chen S, Wang Y (2020) In-depth exploration of attribute information for person re-identification. Appl Intell 50(11):3607–3622
Varior R R, Haloi M, Wang G (2016) Gated siamese convolutional neural network architecture for human re-identification. In: European conference on computer vision, pp 791–808
Li W, Zhu X, Gong S (2018) Harmonious attention network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2285–2294
Wu Y, Lin Y, Dong X, Yan Y, Bian W, Yang Y (2019) Progressive learning for person re-identification with one example. IEEE Trans Image Process 28(6):2872–2881
Zheng Z, Yang X, Yu Z, Zheng L, Yang Y, Kautz J (2019) Joint discriminative and generative learning for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2138–2147
Zhao H, Tian M, Sun S, Shao J, Yan J, Yi S et al (2017) Spindle net: Person re-identification with human body region guided feature decomposition and fusion. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1077–1085
Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In: European conference on computer vision, pp 480–496
Wang G, Yuan Y, Chen X, Li J, Zhou X (2018) Learning discriminative features with multiple granularities for person re-identification. In: Proceedings of the 26th ACM international conference on Multimedia, pp 274–282
Liu H, Xiao Z, Fan B, Zeng H, Zhang Y, Jiang G (2021) PrGCN: Probability prediction with graph convolutional network for person re-identification. Neurocomputing 423:57–70
Sun B, Feng J, Saenko K (2016) Return of frustratingly easy domain adaptation. Proc AAAI Conf Artif Intell 30(1)
Shu R, Bui H H, Narui H, Ermon S (2018) A dirt-t approach to unsupervised domain adaptation. arXiv:1802.08735
Morerio P, Cavazza J, Murino V (2017) Minimal-entropy correlation alignment for unsupervised deep domain adaptation. arXiv:1711.10288
Long M, Zhu H, Wang J, Jordan M I (2017) Deep transfer learning with joint adaptation networks. In: International conference on machine learning, pp 2208–2217
Lin Y, Xie L, Wu Y, Yan C, Tian Q (2020) Unsupervised person re-identification via softened similarity learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3390–3399
Feng H, Chen M, Hu J, Shen D, Liu H, Cai D (2021) Complementary pseudo labels for unsupervised domain adaptation on person Re-Identification. IEEE Trans Image Process 30:2898–2907
Wang J, Zhu X, Gong S, Li W (2018) Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2275–2284
Lin S, Li H, Li C T, Kot A C (2018) Multi-task mid-level feature alignment network for unsupervised cross-dataset person re-identification. arXiv:1807.01440
Wei L, Zhang S, Gao W, Tian Q (2018) 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
Deng W, Zheng L, Ye Q, Kang G, Yang Y, Jiao J (2018) Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 994–1003
Li Y J, Lin C S, Lin Y B, Wang Y C F (2019) Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation. In: Proceedings of the IEEE international conference on computer vision, pp 7919–7929
Liu J, Zha Z J, Chen D, Hong R, Wang M (2019) Adaptive transfer network for cross-domain person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7202–7211
Huang Y, Wu Q, Xu J, Zhong Y (2019) SBSGAN: Suppression of inter-domain background shift for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 9527–9536
Chen Y, Zhu X, Gong S (2019) Instance-guided context rendering for cross-domain person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 232–242
Zhou S, Wang Y, Zhang F, Wu J (2021) Cross-view similarity exploration for unsupervised cross-domain person re-identification. Neural Comput Appl:1–11
Zhong Z, Zheng L, Luo Z, Li S, Yang Y (2019) Invariance matters: Exemplar memory for domain adaptive person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 598–607
Yu H X, Zheng W S, Wu A, Guo X, Gong S, Lai J H (2019) Unsupervised person re-identification by soft multilabel learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2148–2157
Kendall A, Gal Y, Cipolla R (2018) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7482–7491
Wang G, Yuan Y, Chen X, Li J, Zhou X (2018) Learning discriminative features with multiple granularities for person re-identification. In: Proceedings of the 26th ACM international conference on Multimedia, pp 274–282
Zheng F, Deng C, Sun X, Jiang X, Guo X, Yu Z et al (2019) Pyramidal person re-identification via multi-loss dynamic training. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8514–8522
Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In: International conference on machine learning, pp 97–105
Yan H, Ding Y, Li P, Wang Q, Xu Y, Zuo W (2017) Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2272–2281
Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7167–7176
Volpi R, Morerio P, Savarese S, Murino V (2018) Adversarial feature augmentation for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5495–5504
Zhu J Y, Park T, Isola P, Efros A A (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223–2232
Fan H, Zheng L, Yan C, Yang Y (2018) Unsupervised person re-identification: Clustering and fine-tuning. ACM Trans Multimed Comput Commun Appl 14(4):1–18
Yu H X, Wu A, Zheng W S (2017) Cross-view asymmetric metric learning for unsupervised person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 994–1002
Yu H X, Wu A, Zheng W S (2018) Unsupervised person re-identification by deep asymmetric metric embedding. IEEE Trans Pattern Anal Mach Intell 42(4):956–973
Fu Y, Wei Y, Wang G, Zhou Y, Shi H, Huang T S (2019) Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 6112–6121
Zhang X, Cao J, Shen C, You M (2019) Self-training with progressive augmentation for unsupervised cross-domain person re-identification. In: Proceedings of the IEEE international conference on computer vision pp 8222–8231
Wang G, Lai J H, Liang W, Wang G (2020) Smoothing adversarial domain attack and p-memory reconsolidation for cross-domain person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp 10568–10577
Lin T Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988
Li C, Yan J, Wei F, Dong W, Liu Q, Zha H (2017) Self-paced multi-task learning. In: Proceedings of the AAAI conference on artificial intelligence 31(1)
Kendall A, Gal Y, Cipolla R (2018) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7482–7491
Rosenbaum C, Klinger T, Riemer M (2017) Routing networks: Adaptive selection of non-linear functions for multi-task learning. arXiv:1711.01239
Chen Z, Badrinarayanan V, Lee C Y, Rabinovich A (2018) Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In: International Conference on Machine Learning, pp 794–803
Cao Z, Simon T, Wei S E, Sheikh Y (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7291–7299
Gong K, Liang X, Zhang D, Shen X, Lin L (2017) Look into person: Self-supervised structure-sensitive learning and a new benchmark for human parsing. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp 932–940
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: international conference on medical image computing and computer-assisted intervention, pp 234–241
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S et al (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Deng J, Dong W, Socher R, Li L J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 248–255
Sun C, Shrivastava A, Singh S, Gupta A (2017) Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE international conference on computer vision, pp 843–852
Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: A benchmark. In: Proceedings of the IEEE international conference on computer vision, pp 1116–1124
Ristani E, Solera F, Zou R, Cucchiara R, Tomasi C (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: European conference on computer vision, pp 17–35
Kingma D P, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980
Ulyanov D, Vedaldi A, Lempitsky V (2017) Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6924–6932
Zhang R, Isola P, Efros A A, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 586–595
Krizhevsky A, Sutskever I, Hinton G E (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105
Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. arXiv:1706.08500
Liao S, Hu Y, Zhu X, Li S Z (2015) Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp. 2197–2206
Peng P, Xiang T, Wang Y, Pontil M, Gong S, Huang T, Tian Y (2016) Unsupervised cross-dataset transfer learning for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1306–1315
Zeng K, Ning M, Wang Y, Guo Y (2020) Hierarchical clustering with hard-batch triplet loss for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 13657–13665
Jin X, Lan C, Zeng W, Chen Z, Zhang L (2020) Style normalization and restitution for generalizable person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3143–3152
Wang D, Zhang S (2020) Unsupervised person re-identification via multi-label classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 10981–10990
Chong Y, Peng C, Zhang C, Wang Y, Feng W, Pan S (2021) Learning domain invariant and specific representation for cross-domain person re-identification. Appl Intell:1–14
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Informed Consent
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported by the Basic Scientific Research Projects of Central Universities 2572018BH07 and the Natural Science Foundation of Heilongjiang Province under Grant LH2019C003.
Rights and permissions
About this article
Cite this article
Pang, Z., Guo, J., Sun, W. et al. Cross-domain person re-identification by hybrid supervised and unsupervised learning. Appl Intell 52, 2987–3001 (2022). https://doi.org/10.1007/s10489-021-02551-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-021-02551-8