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Triplet Ratio Loss for Robust Person Re-identification

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13534))

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Abstract

Triplet loss has been proven to be useful in the task of person re-identification (ReID). However, it has limitations due to the influence of large intra-pair variations and unreasonable gradients. In this paper, we propose a novel loss to reduce the influence of large intra-pair variations and improve optimization gradients via optimizing the ratio of intra-identity distance to inter-identity distance. As it also requires a triplet of pedestrian images, we call this new loss as triplet ratio loss. Experimental results on four widely used ReID benchmarks, i.e., Market-1501, DukeMTMC-ReID, CUHK03, and MSMT17, demonstrate that the triplet ratio loss outperforms the previous triplet loss.

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Notes

  1. 1.

    Repelling gradient denotes the gradient that pushes the features away from each other, while attracting gradient indicates the gradient that pulls the features closer.

  2. 2.

    0.4 is an empirical value for both \(\alpha \) [31] and \(\beta \). Please refer to Sect. 4.2 for extensive evaluation on the value of \(\beta \).

References

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

    Google Scholar 

  2. Chen, J., et al.: Learning 3D shape feature for texture-insensitive person re-identification. In: CVPR, pp. 8146–8155 (2021)

    Google Scholar 

  3. Cheng, D., Gong, Y., Zhou, S., Wang, J., Zheng, N.: Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: CVPR, pp. 1335–1344 (2016)

    Google Scholar 

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

    Google Scholar 

  5. 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 

  6. Ding, C., Wang, K., Wang, P., Tao, D.: Multi-task learning with coarse priors for robust part-aware person re-identification. TPAMI 44(3), 1474–1488 (2022)

    Article  Google Scholar 

  7. 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 

  8. Ha, M.L., Blanz, V.: Deep ranking with adaptive margin triplet loss. arXiv preprint arXiv:2107.06187 (2021)

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

    Google Scholar 

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

  11. Ho, K., Keuper, J., Pfreundt, F.J., Keuper, M.: Learning embeddings for image clustering: an empirical study of triplet loss approaches. In: ICPR, pp. 87–94 (2021)

    Google Scholar 

  12. Hou, R., Chang, H., Ma, B., Huang, R., Shan, S.: BiCnet-TKS: learning efficient spatial-temporal representation for video person re-identification. In: CVPR, pp. 2014–2023 (2021)

    Google Scholar 

  13. 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 

  14. Huang, T., Qu, W., Zhang, J.: Continual representation learning via auto-weighted latent embeddings on person ReID. In: PRCV, pp. 593–605 (2021)

    Google Scholar 

  15. Li, J., Zhang, S., Tian, Q., Wang, M., Gao, W.: Pose-guided representation learning for person re-identification. TPAMI 44(2), 622–635 (2022)

    Article  Google Scholar 

  16. 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 

  17. Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: ICML, vol. 2, p. 7 (2016)

    Google Scholar 

  18. Liu, X., Yu, L., Lai, J.: Group re-identification based on single feature attention learning network (SFALN). In: PRCV, pp. 554–563 (2021)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  21. Qian, X., Fu, Y., Xiang, T., Jiang, Y.G., Xue, X.: Leader-based multi-scale attention deep architecture for person re-identification. TPAMI 42(2), 371–385 (2020)

    Article  Google Scholar 

  22. 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 

  23. Rao, Y., Chen, G., Lu, J., Zhou, J.: Counterfactual attention learning for fine-grained visual categorization and re-identification. In: ICCV, pp. 1025–1034 (2021)

    Google Scholar 

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

    Google Scholar 

  25. Shu, X., Yuan, D., Liu, Q., Liu, J.: Adaptive weight part-based convolutional network for person re-identification. Multimedia Tools Appl. 79, 23617–23632 (2020). https://doi.org/10.1007/s11042-020-09018-x

    Article  Google Scholar 

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

    Google Scholar 

  27. 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 

  28. Tao, D., Guo, Y., Yu, B., Pang, J., Yu, Z.: Deep multi-view feature learning for person re-identification. TCSVT 28(10), 2657–2666 (2017)

    Google Scholar 

  29. Wang, F., Xiang, X., Cheng, J., Yuille, A.L.: Normface: L2 hypersphere embedding for face verification. In: ACM MM, pp. 1041–1049 (2017)

    Google Scholar 

  30. 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 

  31. Wang, K., Ding, C., Maybank, S.J., Tao, D.: CDPM: convolutional deformable part models for semantically aligned person re-identification. TIP 29, 3416–3428 (2019)

    MATH  Google Scholar 

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

    Google Scholar 

  33. Wang, P., Ding, C., Shao, Z., Hong, Z., Zhang, S., Tao, D.: Quality-aware part models for occluded person re-identification. arXiv preprint arXiv:2201.00107 (2022)

  34. Wang, W., Pei, W., Cao, Q., Liu, S., Lu, G., Tai, Y.W.: Push for center learning via orthogonalization and subspace masking for person re-identification. TIP 30, 907–920 (2020)

    Google Scholar 

  35. Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer GAN to bridge domain gap for person re-identification. In: CVPR, pp. 79–88 (2018)

    Google Scholar 

  36. Wu, C.Y., Manmatha, R., Smola, A.J., Krahenbuhl, P.: Sampling matters in deep embedding learning. In: ICCV, pp. 2840–2848 (2017)

    Google Scholar 

  37. Wu, Y., Lin, Y., Dong, X., Yan, Y., Ouyang, W., Yang, Y.: Exploit the unknown gradually: one-shot video-based person re-identification by stepwise learning. In: CVPR, pp. 5177–5186 (2018)

    Google Scholar 

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

    MATH  Google Scholar 

  39. Ye, M., Li, J., Ma, A.J., Zheng, L., Yuen, P.C.: Dynamic graph co-matching for unsupervised video-based person re-identification. TIP 28(6), 2976–2990 (2019)

    MathSciNet  MATH  Google Scholar 

  40. Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014)

  41. Yu, B., Liu, T., Gong, M., Ding, C., Tao, D.: Correcting the triplet selection bias for triplet loss. In: ECCV, pp. 71–87 (2018)

    Google Scholar 

  42. Yu, S., et al.: Multiple domain experts collaborative learning: multi-source domain generalization for person re-identification. arXiv preprint arXiv:2105.12355 (2021)

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

    Google Scholar 

  44. Zhao, X., Qi, H., Luo, R., Davis, L.: A weakly supervised adaptive triplet loss for deep metric learning. In: ICCV W (2019)

    Google Scholar 

  45. 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 

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

    Google Scholar 

  47. 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 

  48. Zhong, Y., Wang, X., Zhang, S.: Robust partial matching for person search in the wild. In: CVPR, pp. 6827–6835 (2020)

    Google Scholar 

  49. 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 

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

    Google Scholar 

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

    Google Scholar 

  52. Zhou, Z., Li, Y., Gao, J., Xing, J., Li, L., Hu, W.: Anchor-free one-stage online multi-object tracking. In: PRCV, pp. 55–68 (2020)

    Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant U2013601, and the Program of Guangdong Provincial Key Laboratory of Robot Localization and Navigation Technology, under Grant 2020B121202011 and Key-Area Research and Development Program of Guangdong Province, China, under Grant 2019B010154003.

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Authors and Affiliations

  1. UBTech Robotics Corp Ltd, Shenzhen, China

    Shuping Hu, Kan Wang, Huan Tan & Jianxin Pang

  2. SIAT, Chinese Academy of Sciences, Beijing, China

    Kan Wang

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

    Jun Cheng

Authors
  1. Shuping Hu
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  2. Kan Wang
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  3. Jun Cheng
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  4. Huan Tan
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  5. Jianxin Pang
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Corresponding author

Correspondence to Jianxin Pang .

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Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi’an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Hu, S., Wang, K., Cheng, J., Tan, H., Pang, J. (2022). Triplet Ratio Loss for Robust Person Re-identification. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_4

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  • DOI: https://doi.org/10.1007/978-3-031-18907-4_4

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