Advertisement

Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12356)

Abstract

Unsupervised domain adaptation (UDA) in the task of person re-identification (re-ID) is highly challenging due to large domain divergence and no class overlap between domains. Pseudo-label based self-training is one of the representative techniques to address UDA. However, label noise caused by unsupervised clustering is always a trouble to self-training methods. To depress noises in pseudo-labels, this paper proposes a Noise Resistible Mutual-Training (NRMT) method, which maintains two networks during training to perform collaborative clustering and mutual instance selection. On one hand, collaborative clustering eases the fitting to noisy instances by allowing the two networks to use pseudo-labels provided by each other as an additional supervision. On the other hand, mutual instance selection further selects reliable and informative instances for training according to the peer-confidence and relationship disagreement of the networks. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art UDA methods for person re-ID.

Keywords

Unsupervised domain adaptation Person re-identification Collaborative clustering Mutual instance selection 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant 61702163.

References

  1. 1.
    Arpit, D., et al.: A closer look at memorization in deep networks. In: International Conference on Machine Learning (ICML) (2017)Google Scholar
  2. 2.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory (1998)Google Scholar
  3. 3.
    Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: Domain separation networks. In: Advances in Neural Information Processing Systems (NeurIPS) (2016)Google Scholar
  4. 4.
    Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 160–172. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37456-2_14CrossRefGoogle Scholar
  5. 5.
    Chen, Y., Zhu, X., Gong, S.: Instance-guided context rendering for cross-domain person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019)Google Scholar
  6. 6.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009)Google Scholar
  7. 7.
    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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  8. 8.
    Fan, H., Zheng, L., Yan, C., Yang, Y.: Unsupervised person re-identification: clustering and fine-tuning. ACM Trans. Multimed. Comput. Commun. Appl. (TOMCCAP) 14(4), 1–18 (2018) CrossRefGoogle Scholar
  9. 9.
    Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model (2008)Google Scholar
  10. 10.
    Feng, Q., Kang, G., Fan, H., Yang, Y.: Attract or distract: exploit the margin of open set. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019)Google Scholar
  11. 11.
    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 International Conference on Computer Vision (ICCV) (2019)Google Scholar
  12. 12.
    Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. (JMLR) 17(1), 2096–2130 (2016)MathSciNetGoogle Scholar
  13. 13.
    Ge, Y., Chen, D., Li, H.: Mutual mean-teaching: pseudo label refinery for unsupervised domain adaptation on person re-identification. In: International Conference on Learning Representations (ICLR) (2020)Google Scholar
  14. 14.
    Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)Google Scholar
  15. 15.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  16. 16.
    Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)
  17. 17.
    Li, Y.J., Yang, F.E., Liu, Y.C., Yeh, Y.Y., Du, X., Frank Wang, Y.C.: Adaptation and re-identification network: an unsupervised deep transfer learning approach to person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2018)Google Scholar
  18. 18.
    Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)Google Scholar
  19. 19.
    Liu, Z., Wang, J., Gong, S., Lu, H., Tao, D.: Deep reinforcement active learning for human-in-the-loop person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019)Google Scholar
  20. 20.
    Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning (ICML) (2015)Google Scholar
  21. 21.
    Lv, J., Chen, W., Li, Q., Yang, C.: Unsupervised cross-dataset person re-identification by transfer learning of spatial-temporal patterns. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)Google Scholar
  22. 22.
    Malach, E., Shalev-Shwartz, S.: Decoupling “when to update” from “how to update”. In: Advances in Neural Information Processing Systems (NeurIPS) (2017)Google Scholar
  23. 23.
    Panareda Busto, P., Gall, J.: Open set domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  24. 24.
    Peng, P., et al.: Unsupervised cross-dataset transfer learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  25. 25.
    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: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019)Google Scholar
  26. 26.
    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_2CrossRefGoogle Scholar
  27. 27.
    Saito, K., Yamamoto, S., Ushiku, Y., Harada, T.: Open set domain adaptation by backpropagation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 156–171. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01228-1_10CrossRefGoogle Scholar
  28. 28.
    Shu, R., Bui, H.H., Narui, H., Ermon, S.: A dirt-t approach to unsupervised domain adaptation. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)Google Scholar
  29. 29.
    Song, C., Huang, Y., Ouyang, W., Wang, L.: Mask-guided contrastive attention model for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  30. 30.
    Song, J., Yang, Y., Song, Y.Z., Xiang, T., Hospedales, T.M.: Generalizable person re-identification by domain-invariant mapping network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)Google Scholar
  31. 31.
    Song, L., et al.: Unsupervised domain adaptive re-identification: theory and practice. arXiv preprint arXiv:1807.11334 (2018)
  32. 32.
    Suh, Y., Wang, J., Tang, S., Mei, T., Lee, K.M.: Part-aligned bilinear representations for person re-identification. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 418–437. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01264-9_25CrossRefGoogle Scholar
  33. 33.
    Sun, B., Feng, J., Saenko, K.: Return of frustratingly easy domain adaptation. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)Google Scholar
  34. 34.
    Sun, Y., Zheng, L., Deng, W., Wang, S.: SVDNet for pedestrian retrieval. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  35. 35.
    Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 501–518. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01225-0_30CrossRefGoogle Scholar
  36. 36.
    Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2015)Google Scholar
  37. 37.
    Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  38. 38.
    Wang, J., Zhu, X., Gong, S., Li, W.: Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)Google Scholar
  39. 39.
    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 (CVPR) (2018)Google Scholar
  40. 40.
    Wu, A., Zheng, W.S., Lai, J.H.: Unsupervised person re-identification by camera-aware similarity consistency learning. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019)Google Scholar
  41. 41.
    Xie, G.S., et al.: Attentive region embedding network for zero-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)Google Scholar
  42. 42.
    Xie, G.S., et al.: Region graph embedding network for zero-shot learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 562–580. Springer, Cham (2020).  https://doi.org/10.1007/978-3-030-58548-8_33 CrossRefGoogle Scholar
  43. 43.
    Yang, F., et al.: Asymmetric co-teaching for unsupervised cross-domain person re-identification. In: Thirtieth AAAI Conference on Artificial Intelligence (AAAI) (2020)Google Scholar
  44. 44.
    Yang, Q., Yu, H.X., Wu, A., Zheng, W.S.: Patch-based discriminative feature learning for unsupervised person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)Google Scholar
  45. 45.
    Yu, H.X., Wu, A., Zheng, W.S.: Unsupervised person re-identification by deep asymmetric metric embedding. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 42, 956–973 (2018)CrossRefGoogle Scholar
  46. 46.
    Yu, H.X., Zheng, W.S., Wu, A., Guo, X., Gong, S., Lai, J.H.: Unsupervised person re-identification by soft multilabel learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)Google Scholar
  47. 47.
    Yu, X., Han, B., Yao, J., Niu, G., Tsang, I., Sugiyama, M.: How does disagreement help generalization against label corruption? In: International Conference on Machine Learning (ICML) (2019)Google Scholar
  48. 48.
    Zhang, K., Luo, W., Ma, L., Liu, W., Li, H.: Learning joint gait representation via quintuplet loss minimization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)Google Scholar
  49. 49.
    Zhang, X., Cao, J., Shen, C., You, M.: Self-training with progressive augmentation for unsupervised cross-domain person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2019)Google Scholar
  50. 50.
    Zhao, H., et al.: 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 (CVPR) (2017)Google Scholar
  51. 51.
    Zhao, L., Li, X., Zhuang, Y., Wang, J.: Deeply-learned part-aligned representations for person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  52. 52.
    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 (ICCV) (2015)Google Scholar
  53. 53.
    Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., Kautz, J.: Joint discriminative and generative learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)Google Scholar
  54. 54.
    Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  55. 55.
    Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. arXiv preprint arXiv:1708.04896 (2017)
  56. 56.
    Zhong, Z., Zheng, L., Li, S., Yang, Y.: Generalizing a Person retrieval model hetero- and homogeneously. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 176–192. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01261-8_11CrossRefGoogle Scholar
  57. 57.
    Zhong, Z., Zheng, L., Luo, Z., Li, S., Yang, Y.: Invariance matters: exemplar memory for domain adaptive person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)Google Scholar
  58. 58.
    Zhou, S., Wang, J., Wang, J., Gong, Y., Zheng, N.: Point to set similarity based deep feature learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Inception Institute of Artificial IntelligenceAbu DhabiUAE
  2. 2.Institute of North Electronic EquipmentBeijingChina
  3. 3.Tencent AI LabShenzhenChina
  4. 4.Mohamed bin Zayed University of Artificial IntelligenceAbu DhabiUAE

Personalised recommendations