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Discrepant mutual learning fusion network for unsupervised domain adaptation on person re-identification

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Abstract

Existing methods mainly assign pseudo labels by clustering algorithms to solve the problems in domain adaptive pedestrian re-identification caused by unlabeled target domain data and scene style differences between the source and the target domains. But clustering algorithms with single network may generate incorrect noisy pseudo labels which affects the effectiveness of domain adaptation. To address this problem, the dual-branch teacher-student networks uses two networks with almost the same structures to assign soft pseudo labels. However, the soft pseudo labels assigned are largely the same because of the similar structure of the dual networks, which may lead to the same result with single-network training. Therefore, this paper proposes the discrepant mutual learning fusion network to improve the performance of unsupervised domain adaptive person re-identification by both increasing the difference between dual networks and enhancing their feature expressiveness. Firstly, this paper proposes the discrepant dual-branch network (DDNet) to mine the global and local features of the network by constructing two branches with different depths, and constructs the feature random scaling (FRS) module to further enhance the diversity of the extracted feature. Secondly, the feature fusion (FF) module is built to fuse the discrepant features generated by DDNet, and achieve mutual supervise learning of the fusion classification results, which enhances the ability of network feature expression. Experiments show that the proposed method outperforms most classical domain adaptive methods in recognition accuracy.

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References

  1. Li R., Zhang B., Teng Z. (2021) A divide-and-unite deep network for person re-identification. Appl Intell 51:1479–1491

    Article  Google Scholar 

  2. Yan J., Fan Z., Chen S. (2020) In-depth exploration of attribute information for person re-identification. Appl Intell 11:3607–3622

    Article  Google Scholar 

  3. Luo H., Jiang W., Gu Y., Liu F., Liao X. (2020) A strong baseline and batch normalization neck for deep person re-identication. IEEE Transactions on Multimedia 22:2597–2609

    Article  Google Scholar 

  4. Quan R., Dong X., Wu Y., Zhu L., Yang Y. (2019) Auto-reid: Searching for a part-aware convnet for person re-identication. In: IEEE/ CVF International Conference on Computer Vision (ICCV), pp 3749–3758

  5. Yang G, Ding M., Zhang Y., Zhong H. (2021) Bi-directional class-wise adversaries for unsupervised domain adaptation, Appl Intell, 6615–6622

  6. Zhang X., Cao J., Shen C., You M. (2019) Self-training with progressive augmentation for unsupervised cross-domain person re-identication, International Conference on Computer Vision (ICCV), 8221–8230

  7. Yang F., Wei Y., Wang G., Zhou Y., Shi H, Huang T. (2019) Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identication IEEE/CVF International Conference on Computer Vision (ICCV), pp 6111-6120

  8. Yang F., Li K., Zhong Z., Luo Z., Sun X., Cheng H., Guo X., Huang F. (2020) Asymmetric co-teaching for unsupervised cross-domain person re-identication. In: 34th AAAI Conference on Artificial Intelligence Asymmetric co-teaching for unsupervised cross-domain person re-identication 34th AAAI Conference on Artificial intelligence pp 12597-12604

  9. Fan H., Zheng L., Yan C., Yang Y. (2018) Unsupervised Person Re-identication:, Clustering and Fine-tuning. ACM Transactions on Multimedia Computing 14:1–18

    Article  Google Scholar 

  10. Ge Y., Chen D., Li H. (2020) Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identication, Computer Vision and Pattern Recognition

  11. Duan L., Xiong D., Lee J., Guo F. (2006) A local density based spatial clustering algorithm with noise. In: IEEE International Conference on Systems, Man and Cybernetics, pp 4061–4066

  12. Pang Z., Guo J., Sun W., Xiao Y., Yu M. (2021) Cross-domain person re-identification by hybrid supervised and unsupervised learning Applied Intelligence

  13. Wu G, Zhu X, Gong X (2020) Tracklet Self-Supervised Learning for Unsupervised Person re-identification. The Thirty-Fourth AAAI Conference on Artificial Intelligence 34:12362–12369

    Article  Google Scholar 

  14. Sun J., Jung C. (2019) Unsupervised person re-identification using reliable and soft labels. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 3007–3011

  15. Zhang B., Qian J. (2021) Autoencoder-based unsupervised clustering and hashing. Appl Intell 51:493–505

    Article  Google Scholar 

  16. Lin Y., Dong X., Zheng L., Yan Y. (2019) A Bottom-Up clustering approach to unsupervised person Re-Identification. The Thirty-Third AAAI Conference on Artificial Intelligence 33:8738– 8745

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Zhu J., Park T., Isola P., Efros A. (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE Inter-national Conference on Computer Vision (ICCV), pp 2242-2251

  19. Wei L., Zhang S., Gao W., Tian Q. (2018) Person transfer gan to bridge domain gap for person re-identication. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 79–88

  20. 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-identication. In: IEEE/CVF Conference on Computer Vision and Pattern recognition pp 994–1003

  21. Long M., Cao Y., Cao Z., Wang J., Jordan M. (2019) Transferable representation learning with deep adaptation networks. IEEE transactions on pattern analysis and machine intelligence 41(12):3071–3085

    Article  Google Scholar 

  22. Lv J., Chen W., Li Q., Yang C. (2018) Unsupervised cross-dataset person re-identication by transfer learning of spatial-temporal patterns. In: IEEE/CVF Conference on Computer Vision and Pattern recognition pp 7948-7956

  23. Wang J., Zhu X., Gong S., Li W. (2018) Transferable joint attribute-identity deep learning for unsupervised person re-identication. In: IEEE/CVF Conference on Computer Vision and Pattern recognition, pp 2275-2284

  24. Mingote V., Miguel A., Ribas D., Ortega A., Lleida E. (2018) Knowledge distilla- tion and random erasing data augmentation for text-dependent speaker verication . In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp 6824–6828

  25. Dai Z., Chen M., Gu X., Zhu S., Tan P. (2019) Batch dropblock network for person re-identication and beyond. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp 3690–3700

  26. Wang W., Zhao F., Liao S., Shao L. (2020) Attentive waveblock: Complementarity-enhanced mutual networks for unsupervised domain adaptation in person re-identication

  27. He K., Zhang X, Ren S. (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778

  28. Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z. (2016) Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2818–2826

  29. 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 (ECCV), pp 201–518

  30. Hermans A., Beyer L., Leibe B. (2017) In defense of the triplet loss for person re-identification

  31. Fernando B., Fromont E., Muselet D., Sebban M. (2012) Discriminative feature fusion for image classication. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 3434–3441

  32. Qin J., Huang Y., Wen W. (2020) Multi-scale feature fusion residual network for single image super-resolution. Neurocomputing 379:334–342

    Article  Google Scholar 

  33. Tarvainen A., Valpola H. (2017) Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results, Advances in Neural Information Processing Systems, 1195–1204

  34. Hinton G.E., Vinyals O., Dean J. (2015) Distilling the knowledge in a neural network. Computer Science 14:38–39

    Google Scholar 

  35. Erven T., Harremos P. (2014) Rényi Divergence and Kullback-Leibler Divergence. IEEE Trans Inf Theory 60:3797– 3820

    Article  MATH  Google Scholar 

  36. Ristani E., Solera F., Zou R.S., Cucchiara R., Tomasi C. (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: European Conference on Computer Vision (ECCV), pp 17-35

  37. Zheng L., Shen L., Tian L., Wang S., Wang J., Tian Q. (2015) Scalable person re- identication: a benchmark. In: IEEE International Conference on Computer Vision (ICCV), pp 1116–1124

  38. Deng J., Dong W., Socher R., Li L.J., Li K., Li F. (2009) Imagenet: A large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 248– 255

  39. Zhong Z., Zheng L., Luo Z., Li S., Yang Y. (2019) Invariance matters: Exemplar Memory for Domain Adaptive Person Re-Identification. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 598-607

  40. Chong Y., Peng C., Zhang C., et al. (2021) Learning domain invariant and specific representation for cross-domain person re-identification. Appl Intell 51:1–14

    Article  Google Scholar 

  41. Bai Y., Wang C., Lou Y., et al. (2021) Hierarchical Connectivity-Centered Clustering for Unsupervised Domain Adaptation on Person Re-Identification. IEEE Transactions on Image Processing 30:6715–6729

    Article  Google Scholar 

  42. Zheng K., Liu W., He L., Mei T., Luo J. (2021) Group-aware Label Transfer for Domain Adaptive Person Re-identification. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, pp 5306–5315

  43. Zheng Y., Tang S., Teng G., Ge Y. (2021) Online Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-identification. In: IEEE/CVF International Conference on Computer vision(ICCV)

  44. Isobe T., Li D., Tian L., et al. (2021) Towards Discriminative Representation Learning for Unsupervised Person Re-identification. In: IEEE/CVF International Conference on Computer vision(ICCV)

  45. Lin Y., Dong X., Zheng L., Yan Y., Yang Y. (2021) A bottom-up clustering approach to unsupervised person re-identification. Inproceedings of the AAAI Conference on Artificial Intelligence 33:8738–8745

    Article  Google Scholar 

  46. Wu G., Zhu X., Gong S. (2021) Tracklet self-supervised learning for unsupervised person reidentification, In AAAI vol. 34, pp. 12362-123699

  47. Wang D., Zhang S. (2020) Unsupervised person reidentification via multi-label classification. In: In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 10978–10987

  48. Zeng K., Ning M., Wang Y., Guo Y. (2020) Hierarchical Clustering With Hard-Batch Triplet Loss for Person Re-Identification,2020. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA USA, pp 13654–13662

  49. Ge Y., Chen D., Zhu F., et al. (2020) Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object re-ID

  50. Zhai Y., Lu S., Ye Q., et al. (2020) AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-Identification. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp 9018–9027

  51. 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 Transactions on Image Processing 30:2898–2907

    Article  Google Scholar 

  52. Zhao. F, Liao. S, Xie. G, et al. (2020) Unsupervised domain adaptation with noise resistible mutual-training for person re-identification. In: European conference on computer vision (ECCV), pp 526–544

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Acknowledgements

This work is supported by the Natural National Science Foundation of China (61902404, 51734009, 61771417, 61873246), and the State Key Research Development Program (2016YFC0801403).

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Authors and Affiliations

  1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Xiao Yun, Qunqun Wang, Xiaozhou Cheng, Kaili Song & Yanjing Sun

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  1. Xiao Yun
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Correspondence to Yanjing Sun.

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Yun, X., Wang, Q., Cheng, X. et al. Discrepant mutual learning fusion network for unsupervised domain adaptation on person re-identification. Appl Intell 53, 2951–2966 (2023). https://doi.org/10.1007/s10489-022-03532-1

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