Skip to main content
SpringerLink
Log in

Domain adaptive attention-based dropout for one-shot person re-identification

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Cross-domain person re-identification (re-ID) has attracted much attention due to its wide applications in the field of computer vision and surveillance. However, the domain shift issue leads to unsatisfactory generalization performance of a model on an unseen target domain when the model is trained on the source domain. Current methods usually adopt clustering methods to assign pseudo labels for unlabeled target images, resulting in high dependence on the performance of clustering method. In this paper, we firstly focus on extracting universal domain-adaptive features by designing a domain-adaptive-attention-based-dropout (DAAD) layer. DAAD layer is achieved by a universal attention-based dropout adapter (ADA) bank to hide the most discriminative region stochastically and a domain attention module to assign weights to the two domains (source and target). Then two feature memories are introduced according to one-shot learning in which only one image is annotated for each target identity. These two memories are designed to store target features from labeled and unlabeled images, respectively. The labeled feature memory is leveraged to estimate pseudo labels for these unlabeled images while the unlabeled feature memory aims to maximize distances between all the unlabeled images and minimize distances between similar images simultaneously. Extensive experiments on three re-ID datasets (DukeMTMC-reID, Market-1501, and MSMT17) demonstrate that the proposed model is effective to improve the domain adaptation performance than existing techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Bak S, Carr P (2017) One-shot metric learning for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2990–2999

  2. Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D(2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3722–3731

  3. Chen KW, Lai CC, Lee PJ, Chen CS, Hung YP (2011) Adaptive learning for target tracking and true linking discovering across multiple non-overlapping cameras. IEEE Trans Multimed 13(4):625–638

    Article  Google Scholar 

  4. Chen T, Ding S, Xie J, Yuan Y, Chen W, Yang Y, Ren Z, Wang Z (2019) Abd-net: Attentive but diverse person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 8351–8361

  5. Choe J, Shim H (2019) Attention-based dropout layer for weakly supervised object localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2219–2228

  6. Chong Y, Peng C, Zhang J, Pan S (2021) Style transfer for unsupervised domain-adaptive person re-identification. Neurocomputing (NC) 422:314–321

    Article  Google Scholar 

  7. 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

  8. 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

  9. Ding Y, Fan H, Xu M, Yang Y (2020) Adaptive exploration for unsupervised person re-identification. ACM Trans Multimed Comput Commun Appl 16(1):1551–6857

    Article  Google Scholar 

  10. Dong H, Lu P, Zhong S, Liu C, Ji Y, Gong S (2018) Person re-identification by enhanced local maximal occurrence representation and generalized similarity metric learning. Neurocomputing 307:25–37

    Article  Google Scholar 

  11. Dong X, Yu S.I, Weng X, Wei S.E, Yang Y, Sheikh Y (2018) Supervision-by-registration: an unsupervised approach to improve the precision of facial landmark detectors. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 360–368

  12. Fan H, Zheng L, Yan C, Yang Y (2018) Unsupervised person re-identification: clustering and fine-tuning. ACM Trans Multimed Comput Commun Appl (TOMM) 14(4):1–18

    Article  Google Scholar 

  13. Fu Y, Wei Y, Wang G, Zhou Y, Shi H, Huang TS (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

  14. Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: International conference on machine learning (ICML), pp 325–333

  15. Gretton A, Borgwardt K, Rasch M, Schölkopf B, Smola A.J (2007) A kernel method for the two-sample-problem. In: Advances in neural information processing systems, pp 513–520

  16. 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

  17. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  18. Ji Z, Wang H, Han J, Pang Y (2020) SMAN: stacked multimodal attention network for cross-modal image-text retrieval. IEEE Trans Cybern (TCYB). https://doi.org/10.1109/TCYB.2020.2985716

    Article  Google Scholar 

  19. Ji Z, Xiong K, Pang Y, Li X (2019) Video summarization with attention-based encoder-decoder networks. IEEE Trans Circuits Syst Video Technol (TCSVT) 30(6):1709–1717

    Article  Google Scholar 

  20. 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

  21. Li Z, Tang J (2015) Unsupervised feature selection via nonnegative spectral analysis and redundancy control. IEEE Trans Image Process 24(12):5343–5355

    Article  MathSciNet  Google Scholar 

  22. 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 preprint arXiv:1807.01440

  23. Lin Y, Dong X, Zheng L, Yan Y, Yang Y (2019) A bottom-up clustering approach to unsupervised person re-identification. In: Proceedings of the AAAI conference on artificial intelligence vol 33, pp 8738–8745

  24. Lin Y, Guo F, Cao L, Wang J (2016) Person re-identification based on multi-instance multi-label learning. Neurocomputing 217:19–26

    Article  Google Scholar 

  25. 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 (NC) 423:57–70

    Article  Google Scholar 

  26. Rezaei S, Tahmoresnezhad J, Solouk V (2020) A transductive transfer learning approach for image classification. Int J Mach Learn Cybern 12(3):747–762

  27. 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. Springer, New York, pp 17–35

  28. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520

  29. Song L, Cheng W, Zhang L, Bo D, Wang X (2018) Unsupervised domain adaptive re-identification: theory and practice. ArxivPrint, arXiv:1807.11334

  30. 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

  31. Wang D, Zhang S (2020) Unsupervised person re-identification via multi-label classification. In: IEEE computer society conference on computer vision and pattern recognition (CVPR), pp 10981–10990

  32. 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

  33. Wang X, Bao A, Cheng Y, Yu Q (2019) Weight-sharing multi-stage multi-scale ensemble convolutional neural network. Int J Mach Learn Cybern 10(7):1631–1642

    Article  Google Scholar 

  34. 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

  35. 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

    Article  MathSciNet  Google Scholar 

  36. Xia B.N, Gong Y, Zhang Y, Poellabauer C (2019) Second-order non-local attention networks for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 3760–3769

  37. Yang F, Li K, Zhong Z, Luo Z, Sun X, Cheng H, Guo X, Huang F, Ji R, Li S (2020) Asymmetric co-teaching for unsupervised cross domain person re-identification. In: the AAAI conference on artificial intelligence (AAAI), pp 12597–12604

  38. Yu C, Wang J, Chen Y, Qin X (2019) Transfer channel pruning for compressing deep domain adaptation models. Int J Mach Learn Cybern 10(11):3129–3144

    Article  Google Scholar 

  39. Zhang X, Luo H, Fan X, Xiang W, Sun Y, Xiao Q, Jiang W, Zhang C, Sun J (2017) Alignedreid: surpassing human-level performance in person re-identification. arXiv preprint arXiv:1711.08184

  40. Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848–6856

  41. 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

  42. Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: past, present and future. ArxivPrint, arXiv:1610.02984

  43. Zheng M, Karanam S, Wu Z, Radke RJ (2019) Re-identification with consistent attentive siamese networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5735–5744

  44. Zheng W, Gong S, Xiang T (2016) Towards open-world person re-identification by one-shot group-based verification. IEEE Trans Pattern Anal Mach Intell (TPAMI) 38(3):591–606. https://doi.org/10.1109/TPAMI.2015.2453984

    Article  Google Scholar 

  45. Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In: Proceedings of the IEEE international conference on computer vision, pp 3754–3762

  46. Zhong Z, Zheng L, Cao D, Li S (2017) Re-ranking person re-identification with k-reciprocal encoding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1318–1327

  47. Zhong Z, Zheng L, Li S, Yang Y (2018) Generalizing a person retrieval model hetero-and homogeneously. In: Proceedings of the European conference on computer vision (ECCV), pp 172–188

  48. 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

  49. Zhong Z, Zheng L, Zhenga Z, Li S, Yang Y (2019) Camstyle: a novel data augmentation method for person re-identification. IEEE Trans Image Process (TIP) 28(3):1176–1190

    Article  MathSciNet  Google Scholar 

  50. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921–2929

Download references

Acknowledgements

This work is partially supported by National Natural Science Foundation of China under Grant nos. 61872188, U1713208, 61972204, 61672287, 61861136011, 61773215.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Xulin Song & Zhong Jin

  2. Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, Nanjing, China

    Xulin Song & Zhong Jin

Authors
  1. Xulin Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhong Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhong Jin.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, X., Jin, Z. Domain adaptive attention-based dropout for one-shot person re-identification. Int. J. Mach. Learn. & Cyber. 13, 255–268 (2022). https://doi.org/10.1007/s13042-021-01399-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-021-01399-1

Keywords

Search

Navigation