Skip to main content
SpringerLink
Log in

RA Loss: Relation-Aware Loss for Robust Person Re-identification

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

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13842))

Included in the following conference series:

  • 248 Accesses

Abstract

Previous relation-based losses in person re-identification (ReID) typically comprise two sequential steps: they firstly sample both positive pair and negative pair and then deploy constraints to simultaneously improve intra-identity compactness and inter-identity separability. However, existing relation-based losses usually place emphasis on exploring the relation between images and therefore consider only several pairs during each optimization. This inevitably leads to different convergence status for pairs of the same kind and brings about the intra-pair variance problem. Accordingly, we propose a novel Relation-Aware (RA) loss to address the intra-pair variance via exploring the informative relation across pairs. In brief, we introduce a macro-constraint and a micro-constraint. The macro-constraint encourages the separation of positive pair and negative pair via pushing far apart the two “centers” of the positive pair and the negative pair. The “center” of each kind of pair are obtained via averaging all the pairs of the same kind. The micro-constraint further enhances the compactness by minimizing the discrepancies among pairs of the same kind. The two constraints work cooperatively to relieve the intra-pair variance and improve the quality of pedestriansąŕ representation. Results of extensive experiments on three widely used ReID benchmarks, i.e., Market-1501, DukeMTMC-ReID and CUHK03, demonstrate that the RA loss brings improvements over existing relation-based losses.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Notes

  1. 1.

    In this paper, the distance of one pair denotes the distance between the two pedestrian images contained in this pair. In comparison, the distance between two pairs indicates the difference value in the two distances of the two pairs.

References

  1. Carvalho, M., Cadene, R., Picard, D., Soulier, L., Thome, N., Cord, M.: Cross-modal retrieval in the cooking context: learning semantic text-image embeddings. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 35–44 (2018)

    Google Scholar 

  2. Chen, B., Deng, W., Hu, J.: Mixed high-order attention network for person re-identification. In: ICCV, pp. 371–381 (2019)

    Google Scholar 

  3. Chen, T., et al.: Abd-net: attentive but diverse person re-identification. In: ICCV, pp. 8350–8360 (2019)

    Google Scholar 

  4. Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadruplet network for person re-identification. In: CVPR, pp. 403–412 (2017)

    Google Scholar 

  5. Chen, X., et al.: Salience-guided cascaded suppression network for person re-identification. In: CVPR, pp. 3300–3310 (2020)

    Google Scholar 

  6. Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: CVPR, vol. 1, pp. 539–546 (2005)

    Google Scholar 

  7. Dai, Z., Chen, M., Gu, X., Zhu, S., Tan, P.: Batch dropblock network for person re-identification and beyond. In: ICCV, pp. 3690–3700 (2019)

    Google Scholar 

  8. Ding, C., Wang, K., Wang, P., Tao, D.: Multi-task learning with coarse priors for robust part-aware person re-identification. IEEE Trans. Pattern Anal. Mach. Intell. 44, 1474–1488 (2020)

    Article  Google Scholar 

  9. Fang, P., Zhou, J., Roy, S.K., Petersson, L., Harandi, M.: Bilinear attention networks for person retrieval. In: ICCV, pp. 8029–8038 (2019)

    Google Scholar 

  10. Fu, Y., et al.: Horizontal pyramid matching for person re-identification. In: AAAI, pp. 8295–8302 (2019)

    Google Scholar 

  11. Gu, X., Ma, B., Chang, H., Shan, S., Chen, X.: Temporal knowledge propagation for image-to-video person re-identification. In: ICCV, pp. 9647–9656 (2019)

    Google Scholar 

  12. Guo, J., Yuan, Y., Huang, L., Zhang, C., Yao, J.G., Han, K.: Beyond human parts: dual part-aligned representations for person re-identification. In: ICCV, pp. 3641–3650 (2019)

    Google Scholar 

  13. Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: CVPR, vol. 2, pp. 1735–1742 (2006)

    Google Scholar 

  14. He, L., Wang, Y., Liu, W., Zhao, H., Sun, Z., Feng, J.: Foreground-aware pyramid reconstruction for alignment-free occluded person re-identification. In: ICCV, pp. 8449–8458 (2019)

    Google Scholar 

  15. He, S., Luo, H., Wang, P., Wang, F., Li, H., Jiang, W.: Transreid: transformer-based object re-identification. In: ICCV, pp. 15013–15022 (2021)

    Google Scholar 

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

  17. Hou, R., Ma, B., Chang, H., Gu, X., Shan, S., Chen, X.: Interaction-and-aggregation network for person re-identification. In: CVPR, pp. 9317–9326 (2019)

    Google Scholar 

  18. Hou, R., Ma, B., Chang, H., Gu, X., Shan, S., Chen, X.: Vrstc: occlusion-free video person re-identification. In: CVPR, pp. 7183–7192 (2019)

    Google Scholar 

  19. Kalayeh, M.M., Basaran, E., Gökmen, M., Kamasak, M.E., Shah, M.: Human semantic parsing for person re-identification. In: CVPR, pp. 1062–1071 (2018)

    Google Scholar 

  20. Kim, S., Kim, D., Cho, M., Kwak, S.: Proxy anchor loss for deep metric learning. In: CVPR, pp. 3238–3247 (2020)

    Google Scholar 

  21. Li, J., Zhang, S., Tian, Q., Wang, M., Gao, W.: Pose-guided representation learning for person re-identification. IEEE Trans. Pattern Anal. Mach. Intell. 44, 622–635 (2019)

    Article  Google Scholar 

  22. Li, W., Zhao, R., Xiao, T., Wang, X.: Deepreid: deep filter pairing neural network for person re-identification. In: CVPR, pp. 152–159 (2014)

    Google Scholar 

  23. Li, W., Zhu, X., Gong, S.: Harmonious attention network for person re-identification. In: CVPR, pp. 2285–2294 (2018)

    Google Scholar 

  24. Luo, C., Chen, Y., Wang, N., Zhang, Z.: Spectral feature transformation for person re-identification. In: ICCV, pp. 4975–4984 (2019)

    Google Scholar 

  25. Maaten, L.V.D., Hinton, G.: Visualizing data using t-sne. JMLR 9, 2579–2605 (2008)

    MATH  Google Scholar 

  26. Miao, J., Wu, Y., Liu, P., Ding, Y., Yang, Y.: Pose-guided feature alignment for occluded person re-identification. In: ICCV, pp. 542–551 (2019)

    Google Scholar 

  27. Nguyen, B., De Baets, B.: Kernel distance metric learning using pairwise constraints for person re-identification. IEEE Trans. Image Process. 28(2), 589–600 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  28. Qian, X., Fu, Y., Xiang, T., Jiang, Y.G., Xue, X.: Leader-based multi-scale attention deep architecture for person re-identification. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 371–385 (2020)

    Article  Google Scholar 

  29. Quan, R., Dong, X., Wu, Y., Zhu, L., Yang, Y.: Auto-reid: searching for a part-aware convnet for person re-identification. In: ICCV, pp. 3749–3758 (2019)

    Google Scholar 

  30. Saquib Sarfraz, M., Schumann, A., Eberle, A., Stiefelhagen, R.: A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking. In: CVPR, pp. 420–429 (2018)

    Google Scholar 

  31. Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815–823 (2015)

    Google Scholar 

  32. Si, J., et al.: Dual attention matching network for context-aware feature sequence based person re-identification. In: CVPR, pp. 5363–5372 (2018)

    Google Scholar 

  33. Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: NIPS, vol. 29 (2016)

    Google Scholar 

  34. Suh, Y., Wang, J., Tang, S., Mei, T., Lee, K.M.: Part-aligned bilinear representations for person re-identification. In: ECCV, pp. 402–419 (2018)

    Google Scholar 

  35. Sun, Y., et al.: Circle loss: a unified perspective of pair similarity optimization. In: CVPR, pp. 6398–6407 (2020)

    Google Scholar 

  36. Sun, Y., Zheng, L., Deng, W., Wang, S.: Svdnet for pedestrian retrieval. In: ICCV, pp. 3800–3808 (2017)

    Google Scholar 

  37. Sun, Y., Zheng, L., Li, Y., Yang, Y., Tian, Q., Wang, S.: Learning part-based convolutional features for person re-identification. IEEE Trans. Pattern Anal. Mach. Intell. 43, 902–917 (2019)

    Article  Google Scholar 

  38. 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: ECCV, pp. 480–496 (2018)

    Google Scholar 

  39. Sutskever, I., Martens, J., Dahl, G.E., Hinton, G.E.: On the importance of initialization and momentum in deep learning. In: ICML, pp. 1139–1147 (2013)

    Google Scholar 

  40. Tang, Z., Huang, J.: Branch interaction network for person re-identification. In: ACCV (2020)

    Google Scholar 

  41. Tang, Z., Huang, J.: Harmonious multi-branch network for person re-identification with harder triplet loss. ACM Trans. Multimedia Comput. Commun. Appl. 18(4), 1–21 (2022)

    Article  Google Scholar 

  42. Tao, D., Guo, Y., Yu, B., Pang, J., Yu, Z.: Deep multi-view feature learning for person re-identification. IEEE Trans. Circ. Syst. Video Technol. 28(10), 2657–2666 (2017)

    Article  Google Scholar 

  43. Wang, G., Yuan, Y., Chen, X., Li, J., Zhou, X.: Learning discriminative features with multiple granularities for person re-identification. In: ACM MM, pp. 274–282 (2018)

    Google Scholar 

  44. Wang, H., Shen, J., Yongtuo, L., Gao, Y., Gavves, E.: Nformer: robust person re-identification with neighbor transformer. In: CVPR, pp. 7297–7307 (2022)

    Google Scholar 

  45. Wang, K., Ding, C., Maybank, S.J., Tao, D.: CDPM: convolutional deformable part models for semantically aligned person re-identification. IEEE Trans. Image Process. 29, 3416–3428 (2020)

    Article  MATH  Google Scholar 

  46. Wang, K., Ding, C., Pang, J., Xu, X.: Context sensing attention network for video-based person re-identification. arXiv preprint arXiv:2207.02631 (2022)

  47. Wang, K., Wang, P., Ding, C., Tao, D.: Batch coherence-driven network for part-aware person re-identification. IEEE Trans. Image Process. 30, 3405–3418 (2021)

    Article  Google Scholar 

  48. Wang, L., Fan, B., Guo, Z., Zhao, Y., Zhang, R., Li, R., Gong, W.: Dense-scale feature learning in person re-identification. In: ACCV (2020)

    Google Scholar 

  49. Wang, P., Ding, C., Shao, Z., Hong, Z., Zhang, S., Tao, D.: Quality-aware part models for occluded person re-identification. IEEE Trans. Multimedia (2022)

    Google Scholar 

  50. Wang, P., Ding, C., Tan, W., Gong, M., Jia, K., Tao, D.: Uncertainty-aware clustering for unsupervised domain adaptive object re-identification. IEEE Trans. Multimedia (2022)

    Google Scholar 

  51. Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31

    Chapter  Google Scholar 

  52. Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A comprehensive study on center loss for deep face recognition. IJCV 127(6), 668–683 (2019)

    Article  Google Scholar 

  53. Xia, B.N., Gong, Y., Zhang, Y., Poellabauer, C.: Second-order non-local attention networks for person re-identification. In: ICCV, pp. 3759–3768 (2019)

    Google Scholar 

  54. Yao, H., Zhang, S., Hong, R., Zhang, Y., Xu, C., Tian, Q.: Deep representation learning with part loss for person re-identification. IEEE Trans. Image Process. 28(6), 2860–2871 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  55. Yu, B., Tao, D.: Deep metric learning with tuplet margin loss. In: ICCV, pp. 6490–6499 (2019)

    Google Scholar 

  56. Zhang, A., Gao, Y., Niu, Y., Liu, W., Zhou, Y.: Coarse-to-fine person re-identification with auxiliary-domain classification and second-order information bottleneck. In: CVPR, pp. 598–607 (2021)

    Google Scholar 

  57. Zhang, Z., Lan, C., Zeng, W., Chen, Z.: Densely semantically aligned person re-identification. In: CVPR, pp. 667–676 (2019)

    Google Scholar 

  58. Zhao, L., Li, X., Zhuang, Y., Wang, J.: Deeply-learned part-aligned representations for person re-identification. In: ICCV, pp. 3219–3228 (2017)

    Google Scholar 

  59. Zheng, L., Huang, Y., Lu, H., Yang, Y.: Pose invariant embedding for deep person re-identification. IEEE Trans. Image Process. 28(9), 4500–4509 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  60. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: ICCV, pp. 1116–1124 (2015)

    Google Scholar 

  61. Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., Tian, Q.: Person re-identification in the wild. In: ICCV, pp. 1367–1376 (2017)

    Google Scholar 

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

    Google Scholar 

  63. Zheng, Z., Zheng, L., Yang, Y.: A discriminatively learned CNN embedding for person reidentification. ACM Trans. Multimedia Comput. Commun. Appl. 14(1), 1–20 (2017)

    Article  Google Scholar 

  64. Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: ICCV, pp. 3754–3762 (2017)

    Google Scholar 

  65. Zhong, Z., Zheng, L., Cao, D., Li, S.: Re-ranking person re-identification with k-reciprocal encoding. In: CVPR, pp. 1318–1327 (2017)

    Google Scholar 

  66. Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation, pp. 13001–13008 (2020)

    Google Scholar 

  67. Zhou, K., Yang, Y., Cavallaro, A., Xiang, T.: Omni-scale feature learning for person re-identification. In: ICCV, pp. 3701–3711 (2019)

    Google Scholar 

  68. Zhou, S., Wang, F., Huang, Z., Wang, J.: Discriminative feature learning with consistent attention regularization for person re-identification. In: ICCV, pp. 8039–8048 (2019)

    Google Scholar 

  69. Zhu, H., Ke, W., Li, D., Liu, J., Tian, L., Shan, Y.: Dual cross-attention learning for fine-grained visual categorization and object re-identification. In: CVPR, pp. 4692–4702 (2022)

    Google Scholar 

  70. Zhu, K., Guo, H., Liu, Z., Tang, M., Wang, J.: Identity-guided human semantic parsing for person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 346–363. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58580-8_21

    Chapter  Google Scholar 

  71. Zhu, K., et al.: Aaformer: auto-aligned transformer for person re-identification. arXiv preprint arXiv:2104.00921 (2021)

Download references

Acknowledgements

This research is supported by the National Natural Science Foundation of China (U2013601), and Key-Area Research and Development Program of Guangdong Province, China (2019B010154003), and the Program of Guangdong Provincial Key Laboratory of Robot Localization and Navigation Technology (2020B121202011), and the Natural Science Foundation of China (U21A20487), and Shenzhen Technology Project (JCYJ20180507182610734, KCXFZ20201221173411032, Y795001001), and CAS Key Technology Talent Program, and Guangdong Technology Project (No. 2016B010125003).

Author information

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Kan Wang & Jun Cheng

  2. Guangdong Provincial Key Laboratory of Robot Localization and Navigation Technology, UBTech Robotics Corp Ltd., Shenzhen, China

    Kan Wang, Shuping Hu, Jianxin Pang & Huan Tan

  3. Institute for Infocomm Research, A*STAR, Singapore, Singapore

    Jun Cheng

Authors
  1. Kan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuping Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianxin Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Huan Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianxin Pang .

Editor information

Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Wang, K., Hu, S., Cheng, J., Cheng, J., Pang, J., Tan, H. (2023). RA Loss: Relation-Aware Loss for Robust Person Re-identification. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13842. Springer, Cham. https://doi.org/10.1007/978-3-031-26284-5_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26284-5_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26283-8

  • Online ISBN: 978-3-031-26284-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation