Skip to main content
SpringerLink
Log in

Modality Synergy Complement Learning with Cascaded Aggregation for Visible-Infrared Person Re-Identification

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13674))

Included in the following conference series:

Abstract

Visible-Infrared Re-Identification (VI-ReID) is challenging in image retrievals. The modality discrepancy will easily make huge intra-class variations. Most existing methods either bridge different modalities through modality-invariance or generate the intermediate modality for better performance. Differently, this paper proposes a novel framework, named Modality Synergy Complement Learning Network (MSCLNet) with Cascaded Aggregation. Its basic idea is to synergize two modalities to construct diverse representations of identity-discriminative semantics and less noise. Then, we complement synergistic representations under the advantages of the two modalities. Furthermore, we propose the Cascaded Aggregation strategy for fine-grained optimization of the feature distribution, which progressively aggregates feature embeddings from the subclass, intra-class, and inter-class. Extensive experiments on SYSU-MM01 and RegDB datasets show that MSCLNet outperforms the state-of-the-art by a large margin. On the large-scale SYSU-MM01 dataset, our model can achieve 76.99% and 71.64% in terms of Rank-1 accuracy and mAP value. Our code will be available at https://github.com/bitreidgroup/VI-ReID-MSCLNet.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahmed, S.M., Lejbolle, A.R., Panda, R., Roy-Chowdhury, A.K.: Camera on-boarding for person re-identification using hypothesis transfer learning. In: CVPR, pp. 12144–12153 (2020)

    Google Scholar 

  2. Bai, S., Tang, P., Torr, P.H., Latecki, L.J.: Re-ranking via metric fusion for object retrieval and person re-identification. In: CVPR, pp. 740–749 (2019)

    Google Scholar 

  3. Chen, G., Lin, C., Ren, L., Lu, J., Zhou, J.: Self-critical attention learning for person re-identification. In: ICCV, pp. 9637–9646 (2019)

    Google Scholar 

  4. Chen, T., et al.: ABD-net: attentive but diverse person re-identification. In: CVPR, pp. 8351–8361 (2019)

    Google Scholar 

  5. Chen, Y., Wan, L., Li, Z., Jing, Q., Sun, Z.: Neural feature search for RGB-infrared person re-identification. In: CVPR, pp. 587–597, June 2021

    Google Scholar 

  6. Choi, S., Lee, S., Kim, Y., Kim, T., Kim, C.: Hi-CMD: hierarchical cross-modality disentanglement for visible-infrared person re-identification. In: CVPR, pp. 10257–10266 (2020)

    Google Scholar 

  7. Dai, P., Ji, R., Wang, H., Wu, Q., Huang, Y.: Cross-modality person re-identification with generative adversarial training. In: IJCAI, pp. 677–683 (2018)

    Google Scholar 

  8. 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, pp. 994–1003 (2018)

    Google Scholar 

  9. Feng, Z., Lai, J., Xie, X.: Learning modality-specific representations for visible-infrared person re-identification. IEEE TIP 29, 579–590 (2019)

    MathSciNet  MATH  Google Scholar 

  10. Fu, C., Hu, Y., Wu, X., Shi, H., Mei, T., He, R.: CM-NAS: cross-modality neural architecture search for visible-infrared person re-identification. In: ICCV, pp. 11823–11832, October 2021

    Google Scholar 

  11. Hao, X., Zhao, S., Ye, M., Shen, J.: Cross-modality person re-identification via modality confusion and center aggregation. In: ICCV, pp. 16403–16412, October 2021

    Google Scholar 

  12. Hao, Y., Wang, N., Li, J., Gao, X.: HSME: hypersphere manifold embedding for visible thermal person re-identification. In: AAAI, pp. 8385–8392 (2019)

    Google Scholar 

  13. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

    Google Scholar 

  14. Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)

  15. Jia, M., Zhai, Y., Lu, S., Ma, S., Zhang, J.: A similarity inference metric for RGB-infrared cross-modality person re-identification. arXiv preprint arXiv:2007.01504 (2020)

  16. Jin, X., Lan, C., Zeng, W., Chen, Z., Zhang, L.: Style normalization and restitution for generalizable person re-identification. In: CVPR, pp. 3143–3152 (2020)

    Google Scholar 

  17. Li, D., Wei, X., Hong, X., Gong, Y.: Infrared-visible cross-modal person re-identification with an x modality. In: AAAI, pp. 4610–4617 (2020)

    Google Scholar 

  18. Li, H., Wu, G., Zheng, W.S.: Combined depth space based architecture search for person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6729–6738 (2021)

    Google Scholar 

  19. Li, Y., He, J., Zhang, T., Liu, X., Zhang, Y., Wu, F.: Diverse part discovery: occluded person re-identification with part-aware transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2898–2907 (2021)

    Google Scholar 

  20. Lin, Y., Xie, L., Wu, Y., Yan, C., Tian, Q.: Unsupervised person re-identification via softened similarity learning. In: CVPR, pp. 3390–3399 (2020)

    Google Scholar 

  21. Lu, Y., et al.: Cross-modality person re-identification with shared-specific feature transfer. In: CVPR, pp. 13379–13389 (2020)

    Google Scholar 

  22. Luo, C., Chen, Y., Wang, N., Zhang, Z.: Spectral feature transformation for person re-identification. In: CVPR, pp. 4976–4985 (2019)

    Google Scholar 

  23. Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: CVPR Workshops (2019)

    Google Scholar 

  24. van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Lear. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

  25. Melis, G., Kočiskỳ, T., Blunsom, P.: Mogrifier LSTM. arXiv preprint arXiv:1909.01792 (2019)

  26. Meng, J., Zheng, W.S., Lai, J.H., Wang, L.: Deep graph metric learning for weakly supervised person re-identification. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6074–6093 (2021)

    Google Scholar 

  27. Moon, H., Phillips, P.J.: Computational and performance aspects of PCA-based face-recognition algorithms. Perception 30(3), 303–321 (2001)

    Article  Google Scholar 

  28. Nguyen, D.T., Hong, H.G., Kim, K.W., Park, K.R.: Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors 17(3), 605 (2017)

    Article  Google Scholar 

  29. Paisitkriangkrai, S., Shen, C., Van Den Hengel, A.: Learning to rank in person re-identification with metric ensembles. In: CVPR, pp. 1846–1855 (2015)

    Google Scholar 

  30. Pu, N., Chen, W., Liu, Y., Bakker, E.M., Lew, M.S.: Dual Gaussian-based variational subspace disentanglement for visible-infrared person re-identification. In: ACMMM, pp. 2149–2158 (2020)

    Google Scholar 

  31. Ren, C.X., Liang, B.H., Lei, Z.: Domain adaptive person re-identification via camera style generation and label propagation. IEEE Trans. Inf. Forensics Secur. 15, 1290–1302 (2019)

    Article  Google Scholar 

  32. Sun, D., Yao, A., Zhou, A., Zhao, H.: Deeply-supervised knowledge synergy. In: CVPR, pp. 6997–7006 (2019)

    Google Scholar 

  33. Sun, X., Zheng, L.: Dissecting person re-identification from the viewpoint of viewpoint. In: CVPR, pp. 608–617 (2019)

    Google Scholar 

  34. 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_30

    Chapter  Google Scholar 

  35. Wang, G.A., et al.: Cross-modality paired-images generation for RGB-infrared person re-identification. In: AAAI, pp. 12144–12151 (2020)

    Google Scholar 

  36. Wang, G., et al.: High-order information matters: learning relation and topology for occluded person re-identification. In: CVPR, pp. 6449–6458 (2020)

    Google Scholar 

  37. Wang, G., Zhang, T., Cheng, J., Liu, S., Yang, Y., Hou, Z.: RGB-infrared cross-modality person re-identification via joint pixel and feature alignment. In: ICCV, pp. 3623–3632 (2019)

    Google Scholar 

  38. Wang, J., Zhu, X., Gong, S., Li, W.: Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: CVPR, pp. 2275–2284 (2018)

    Google Scholar 

  39. Wang, Y., Chen, Z., Feng, W., Gang, W.: Person re-identification with cascaded pairwise convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  40. Wang, Z., Wang, Z., Zheng, Y., Chuang, Y.Y., Satoh, S.: Learning to reduce dual-level discrepancy for infrared-visible person re-identification. In: CVPR, pp. 618–626 (2019)

    Google Scholar 

  41. Wei, Z., Yang, X., Wang, N., Gao, X.: Syncretic modality collaborative learning for visible infrared person re-identification. In: ICCV, pp. 225–234, October 2021

    Google Scholar 

  42. Wu, A., Zheng, W.-S., Gong, S., Lai, J.: RGB-IR person re-identification by cross-modality similarity preservation. IJCV 128(6), 1765–1785 (2020). https://doi.org/10.1007/s11263-019-01290-1

    Article  MathSciNet  Google Scholar 

  43. Wu, A., Zheng, W.S., Yu, H.X., Gong, S., Lai, J.: RGB-infrared cross-modality person re-identification. In: ICCV, pp. 5380–5389 (2017)

    Google Scholar 

  44. Wu, D., Ye, M., Lin, G., Gao, X., Shen, J.: Person re-identification by context-aware part attention and multi-head collaborative learning. IEEE Trans. Inf. Forensics Secur. 17, 115–126 (2021)

    Article  Google Scholar 

  45. Wu, Q., et al.: Discover cross-modality nuances for visible-infrared person re-identification. In: CVPR, pp. 4330–4339, June 2021

    Google Scholar 

  46. Xuan, S., Zhang, S.: Intra-inter camera similarity for unsupervised person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11926–11935 (2021)

    Google Scholar 

  47. Ye, M., Lan, X., Leng, Q., Shen, J.: Cross-modality person re-identification via modality-aware collaborative ensemble learning. IEEE TIP 29, 9387–9399 (2020)

    MATH  Google Scholar 

  48. Ye, M., Lan, X., Li, J., Yuen, P.C.: Hierarchical discriminative learning for visible thermal person re-identification. In: AAAI, pp. 7501–7508 (2018)

    Google Scholar 

  49. Ye, M., Lan, X., Wang, Z., Yuen, P.C.: Bi-directional center-constrained top-ranking for visible thermal person re-identification. IEEE TIFS 15, 407–419 (2019)

    Google Scholar 

  50. Ye, M., Shen, J., J. Crandall, D., Shao, L., Luo, J.: Dynamic dual-attentive aggregation learning for visible-infrared person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 229–247. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58520-4_14

    Chapter  Google Scholar 

  51. Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., Hoi, S.C.H.: Deep learning for person re-identification: a survey and outlook. arXiv preprint arXiv:2001.04193 (2020)

  52. Ye, M., Shen, J., Shao, L.: Visible-infrared person re-identification via homogeneous augmented tri-modal learning. IEEE TIFS 16, 728–739 (2020)

    Google Scholar 

  53. Yu, S., Li, S., Chen, D., Zhao, R., Yan, J., Qiao, Y.: COCAS: a large-scale clothes changing person dataset for re-identification. In: CVPR, pp. 3400–3409 (2020)

    Google Scholar 

  54. Zhang, X., Ge, Y., Qiao, Y., Li, H.: Refining pseudo labels with clustering consensus over generations for unsupervised object re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3436–3445 (2021)

    Google Scholar 

  55. Zhang, Z., Lan, C., Zeng, W., Chen, Z.: Multi-granularity reference-aided attentive feature aggregation for video-based person re-identification. In: CVPR, pp. 10407–10416 (2020)

    Google Scholar 

  56. Zhang, Z., Lan, C., Zeng, W., Jin, X., Chen, Z.: Relation-aware global attention for person re-identification. In: CVPR, pp. 3186–3195 (2020)

    Google Scholar 

  57. Zheng, F., et al.: Pyramidal person re-identification via multi-loss dynamic training. In: CVPR, pp. 8514–8522 (2019)

    Google Scholar 

  58. Zheng, M., Karanam, S., Wu, Z., Radke, R.J.: Re-identification with consistent attentive Siamese networks. In: CVPR, pp. 5735–5744 (2019)

    Google Scholar 

  59. Zhong, Z., Zheng, L., Zheng, Z., Li, S., Yang, Y.: Camera style adaptation for person re-identification. In: CVPR, pp. 5157–5166 (2018)

    Google Scholar 

  60. Zhu, X., Jing, X.Y., You, X., Zuo, W., Shan, S., Zheng, W.S.: Image to video person re-identification by learning heterogeneous dictionary pair with feature projection matrix. IEEE Trans. Inf. Forensics Secur. 13, 717–732 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61902027, and the Start-up Research Grant (SRG) of University of Macau.

Author information

Authors and Affiliations

  1. School of Computer Science, Beijing Institute of Technology, Beijing, China

    Yiyuan Zhang, Sanyuan Zhao, Yuhao Kang & Jianbing Shen

  2. SKL-IOTSC, Department of Computer and Information Science, University of Macau, Taipa, Macau

    Jianbing Shen

Authors
  1. Yiyuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanyuan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuhao Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianbing Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanyuan Zhao .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 1151 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, Y., Zhao, S., Kang, Y., Shen, J. (2022). Modality Synergy Complement Learning with Cascaded Aggregation for Visible-Infrared Person Re-Identification. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13674. Springer, Cham. https://doi.org/10.1007/978-3-031-19781-9_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19781-9_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19780-2

  • Online ISBN: 978-3-031-19781-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation