Skip to main content
SpringerLink
Log in

Generalizable person re-identification with part-based multi-scale network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Supervised person re-identification (Re-ID) has advanced significantly, but it suffers from the performance drop when the pretrained models are directly deployed to an unseen domain. Meanwhile, domain adaptation methods are widely investigated to decrease the performance degradation caused by domain gaps. However, it still requires training with unlabeled target-domain data and iteratively updating models. In this work, we proposed a generalizable person Re-ID framework named Part-based Multi-scale Network (PMN), which was trained on source domain(s) once and can be directly exploited to target domains with stable performance. To this end, we leveraged a part-based architecture which uniformly partitions feature maps into several horizontal stripes. The stripe features contain fine-grained information of human parts and therefore benefit learning discriminative features. The Scale Adjusting Module (SAM) is also designed to regulate the style differences appearing in lower-level feature maps and helps incorporation of features from different levels. When we integrated the style-adjusted features and fine-grained local features into our improved backbone, the proposed framework becomes generalized to variation of image styles and backgrounds from different datasets. Extensive experiments show the superiority of the proposed PMN over state-of-the-art generalizable methods on multiple popular Re-ID benchmarks with cross-domain setting. Furthermore, we also demonstrate the advantage of using our framework as a backbone for domain adaptation methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Cai H, Wang Z, Cheng J (2019) Multi-scale body-part mask guided attention for person re-identification. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019, pp. 1555–1564. https://doi.org/10.1109/CVPRW.2019.00197

  2. Chen X, Fu C, Zhao Y, Zheng F, Song J, Ji R, Yang Y (2020) Salience-guided cascaded suppression network for person re-identification. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 3297–3307. https://doi.org/10.1109/CVPR42600.2020.00336

  3. Chen Y, Zhu X, Gong S (2017) Person re-identification by deep learning multi-scale representations. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy, 2017, pp 2590–2600. https://doi.org/10.1109/ICCVW.2017.304

  4. Cheng D, Gong Y, Zhou S, Wang J, Zheng N (2016) Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp 1335–1344. https://doi.org/10.1109/CVPR.2016.149

  5. Choi S, Kim T, Jeong M, Park H, Kim C (2021) Meta batch-instance normalization for generalizable person re-identification. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021, pp 3424–3434. https://doi.org/10.1109/CVPR46437.2021.00343

  6. 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-identification. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp 994–1003. https://doi.org/10.1109/CVPR.2018.00110

  7. Deng X, Liao K, Zheng Y, Lin G, Lei H (2021) A deep multi-feature distance metric learning method for pedestrian re-identification. Multimed Tools Appl 80:23113–23131. https://doi.org/10.1007/s11042-020-10458-8

    Article  Google Scholar 

  8. Ding G, Zhang S, Khan S, Tang Z, Zhang J, Porikli F (2019) Feature affinity-based pseudo labeling for semi-supervised person re-identification. IEEE Trans Multimedia 21(11):2891–2902

    Article  Google Scholar 

  9. Ding Y, Fan H, Xu M, Yang Y (2020) Adaptive exploration for unsupervised person re-identification. ACM Trans Multimedia Comput Commun Appl 16(1):1–19. https://doi.org/10.1145/3369393

    Article  Google Scholar 

  10. Diwakar M, Kumar P, Singh AK (2020) CT image denoising using NLM and its method noise thresholding. Multimed Tools Appl 79(21–22):14449–14464

    Article  Google Scholar 

  11. Fan H, Zheng L, Yan C, Yang Y (2018) Unsupervised person re-identification: clustering and fine-tuning. ACM Trans Multimedia Comput Commun Appl 14(4):1–18. https://doi.org/10.1145/3243316

    Article  Google Scholar 

  12. Felzenszwalb P, Girshick R, Mcallester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  13. Fu Y, Wei Y, Wang G, Zhou Y, Shi H, Huang T S (2019) Self-similarity grouping: a simple unsupervised cross domain adaptation approach for person re-identification. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019, pp 6111–6120. https://doi.org/10.1109/ICCV.2019.00621

  14. Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by Backpropagation. In: Proceedings of the 32nd international conference on international conference on machine learning - volume 37 (ICML’15, Lille, France). JMLR.org, 1180–1189

  15. Ghifary M, Balduzzi D, Kleijn WB, Zhang M (2017) Scatter component analysis: a unified framework for domain adaptation and domain generalization. IEEE IEEE Trans Pattern Anal Mach Intell (PAMI) 39(7):1414–1430

    Article  Google Scholar 

  16. Ghifary M, Kleijn WB, Zhang M, Balduzzi D (2015) Domain generalization for object recognition with multi-task autoencoders. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp 2551–2559. https://doi.org/10.1109/ICCV.2015.293

  17. Gray D, Tao H (2008) Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Forsyth D., Torr P., Zisserman A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88682-2_21

  18. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp 2980–2988. https://doi.org/10.1109/ICCV.2017.322

  19. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  20. Hirzer M, Beleznai C, Roth PM, Bischof H (2011) Person re-identification by descriptive and discriminative classification. In: Heyden A, Kahl F (eds) Image analysis. SCIA 2011. Lecture notes in computer science. https://doi.org/10.1007/978-3-642-21227-7_9, vol 6688. Springer, Berlin

  21. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, 2018, pp 7132–7141. https://doi.org/10.1109/CVPR.2018.00745

  22. Huang W-L, Hung C-Y, Lin I-C (2021) Confidence-based 6d object pose estimation. To appear in IEEE Transactions on Multimedia 24:3025–3035. https://doi.org/10.1109/TMM.2021.3092149

    Article  Google Scholar 

  23. Huang X, Belongie S (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp 1510–1519. https://doi.org/10.1109/ICCV.2017.167

  24. Huang Y, Lian S, Hu H, Chen D, Su T (2021) Multiscale omnibearing attention networks for person re-identification. IEEE Trans Circuits Syst Video Technol 31(5):1790–1803

    Article  Google Scholar 

  25. Isobe T, Li D, Tian L, Chen W, Shan Y, Wang S (2021) Towards discriminative representation learning for unsupervised person re-identification. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp 8506–8516. https://doi.org/10.1109/ICCV48922.2021.00841

  26. Jia J, Ruan Q, Hospedales TM (2019) Frustratingly easy person re-identification: generalizing person re-id in practice. in: Proceedings of the British Machine Vision Conference (BMVC), 117

  27. Jin X, Lan C, Zeng W, Chen Z (2020) Global distance-distributions separation for unsupervised person re-identification. In: Vedaldi A, Bischof H, Brox T, Frahm JM (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_43

  28. Jin X, Lan C, Zeng W, Chen Z, Zhang L (2020) Style normalization and restitution for generalizable person re-identification. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp 3140–3149. https://doi.org/10.1109/CVPR42600.2020.00321

  29. Kalayeh M M, Basaran E, Gökmen M, Kamasak M E, Shah M (2018) Human semantic parsing for person re-identification. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, 2018, pp 1062–1071. https://doi.org/10.1109/CVPR.2018.00117

  30. Khosla A, Zhou T, Malisiewicz T, Efros A A, Torralba A (2012) Undoing the damage of dataset bias. In: Fitzgibbon A, Lazebnik S, Perona P, Sato Y, Schmid C (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33718-5_12

  31. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: International Conference on Learning Representations, ICLR 2015, San Diego, CA USA, May 7–9, 2015

  32. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE conference on computer vision and pattern recognition, New York, NY, 2006 pp 2169–2178. https://doi.org/10.1109/CVPR.2006.68

  33. Lee Y-H, Chang Y-K, Chang Y-L, Lin I-C, Wang Y-S, Lin W-C (2018) Enhancing the realism of sketch and painted portraits with adaptable patches. Comput Graphics Forum 37(1):214–225

    Article  Google Scholar 

  34. Li D, Yang Y, Song Y-Z, Hospedales T (2018) Learning to generalize: meta-learning for domain generalization. In: AAAI is a conference: February 2–7, 2018, New Orleans, Louisiana, USA

  35. Li W, Zhao R, Xiao T, Wang X (2014) DeepReID: deep filter pairing neural network for person re-identification. In: 2014 IEEE conference on computer vision and pattern recognition, Columbus, OH, USA, 2014, pp 152–159. https://doi.org/10.1109/CVPR.2014.27

  36. Li X, Loy CC (2018) Video object segmentation with joint re-identification and attention-aware mask propagation. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science(), vol 11207. Springer, Cham. https://doi.org/10.1007/978-3-030-01219-9_6

  37. Li Y, Yao H, Zhang T, Xu C (2021) Part-based structured representation learning for person re-identification. ACM Trans. Multimedia Comput. Commun. Appl. 16(4)

  38. Lin Y, Xie L, Wu Y, Yan C, Tian Q (2020) Unsupervised person re-identification via softened similarity learning. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp 3387–3396. https://doi.org/10.1109/CVPR42600.2020.00345

  39. Liu H, Guo F, Xia D (2021) Domain adaptation with structural knowledge transfer learning for person re-identification. Multimed Tools Appl 80:29321–29337

    Article  Google Scholar 

  40. Liu J, Ni B, Yan Y, Zhou P, Cheng S, Hu J (2018) Pose transferrable person re-identification. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp 4099–4108. https://doi.org/10.1109/CVPR.2018.00431

  41. Liu X, Zhang S (2020) Domain adaptive person re-identification via coupling optimization. In: Proceedings of the 28th ACM International Conference on Multimedia, MM ’20, Association for Computing Machinery, New York, NY, USA, pp 547–555. https://doi.org/10.1145/3394171.3413904

  42. Liu X, Tan H, Tong X, Cao J, Zhou J (2019) Feature preserving GAN and multi-scale feature enhancement for domain adaption person re-identification. Neurocomputing 364:108–118. https://doi.org/10.1016/j.neucom.2019.07.063

    Article  Google Scholar 

  43. Loy CC, Xiang T, Gong S (2010) Time-delayed correlation analysis for multi-camera activity understanding. Int J Comput Vis (IJCV) 90 (1):106–129

    Article  Google Scholar 

  44. Luo C, Song C, Zhang Z (2020) Generalizing person re-identification by camera-aware invariance learning and cross-domain mixup. In: Vedaldi A, Bischof H, Brox T, Frahm JM (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12360. Springer, Cham. https://doi.org/10.1007/978-3-030-58555-6_14

  45. Luo H, Gu Y, Liao X, Lai S, Jiang W (2019) Bag of tricks and a strong baseline for deep person re-identification. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019, pp 1487–1495. https://doi.org/10.1109/CVPRW.2019.00190

  46. Muandet K, Balduzzi D, Schölkopf B (2013) Domain generalization via invariant feature representation. In: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 (ICML’13). JMLR.org, I-10-I-18

  47. Muhammad N, Bibi N, Kamran M, Bashir Y, Park S, Gyoung KD (2020) Image noise reduction based on block matching in wavelet frame domain. Multimed Tools Appl 79:26327–26344

    Article  Google Scholar 

  48. Pan X, Luo P, Shi J, Tang X (2018) Two at once: enhancing learning and generalization capacities via IBN-Net. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision - ECCV 2018. ECCV 2018. Lecture Notes in Computer Science(), vol 11208. Springer, Cham. https://doi.org/10.1007/978-3-030-01225-0_29

  49. Qiao S, Liu C, Shen W, Yuille A (2018) Few-shot image recognition by predicting parameters from activations. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, 2018, pp 7229–7238. https://doi.org/10.1109/CVPR.2018.00755

  50. Ruan W, Liang C, Yu Y, Wang Z, Liu W, Chen J, Ma J (2020) Correlation discrepancy insight network for video re-identification. ACM Trans Multimedia Comput Commun Appl 16(4):1–21. https://doi.org/10.1145/3402666

    Article  Google Scholar 

  51. Shankar S, Piratla V, Chakrabarti S, Chaudhuri S, Jyothi P, Sarawagi S (2018) Generalizing across domains via cross-gradient training. In: International Conference on Learning Representations (ICLR). Vancouver, BC, Canada on April 30–May 3, 2018

  52. Shrivastava A, Gupta A, Girshick R (2016) Training region-based object detectors with online hard example mining. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016 pp 761–769. https://doi.org/10.1109/CVPR.2016.89

  53. Song J, Yang Y, Song Y, Xiang T, Hospedales T (2019) Generalizable person re-identification by domain-invariant mapping network. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019 pp 719–728. https://doi.org/10.1109/CVPR.2019.00081

  54. Su C, Li J, Zhang S, Xing J, Gao W, Tian Q (2017) Pose-driven deep convolutional model for person re-identification. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017 pp 3980–3989. https://doi.org/10.1109/ICCV.2017.427

  55. 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: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science(), vol 11208. Springer, Cham. https://doi.org/10.1007/978-3-030-01225-0_30

  56. Szegedy C, Ioffe S, Vanhoucke V, Alemi A A (2017) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11231

  57. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp 2818–2826. https://doi.org/10.1109/CVPR.2016.308

  58. Tan H, Xiao H, Zhang X, Dai B, Lai S, Liu Y, Zhang M (2020) MSBA: multiple scales, branches and attention network with bag of tricks for person re-identification. IEEE Access 8:63632–63642

    Article  Google Scholar 

  59. Tsai M-H, Liao Y-K, Lin I-C (2014) Human face aging with guided prediction and detail synthesis. Multimed Tools Appl 72(1):801–824

    Article  Google Scholar 

  60. Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: the missing ingredient for fast stylization. arXiv https://doi.org/10.48550/ARXIV.1607.08022

  61. Ulyanov D, Vedaldi A, Lempitsky V (2017) Improved texture networks: maximizing quality and diversity in feed-forward stylization and texture synthesis. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp 4105–4113. https://doi.org/10.1109/CVPR.2017.437

  62. van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(86):2579–2605. http://jmlr.org/papers/v9/vandermaaten08a.html

    MATH  Google Scholar 

  63. Vinyals O, Blundell C, Lillicrap T, kavukcuoglu, Wierstra D (2016) Matching networks for one shot learning. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS), Curran Associates, Inc., Barcelona SPAIN, pp 3630–3638

  64. Volpi R, Namkoong H, Sener O, Duchi J, Murino V, Savarese S (2018) Generalizing to unseen domains via adversarial data augmentation. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS), p 5339–5349

  65. Wang C, Song L, Wang G, Zhang Q, Wang X (2019) Multi-scale multi-patch person re-identification with exclusivity regularized softmax. Neurocomputing 382:64–70

    Article  Google Scholar 

  66. Wang D, Zhang S (2020) Unsupervised person re-identification via multi-label classification. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 10978–10987. https://doi.org/10.1109/CVPR42600.2020.01099

  67. Wang G, Yuan Y, Chen X, Li J, Zhou X (2018) Learning discriminative features with multiple granularities for person re-identification. In: Proceedings of the 26th ACM International Conference on Multimedia (MM ’18). Association for Computing Machinery, New York, NY, USA, 274–282. https://doi.org/10.1145/3240508.3240552

  68. Wang J, Zhang J, Wen X (2020) Non-full multi-layer feature representations for person re-identification. Multimed Tool Appl 80:17205–17221

    Article  Google Scholar 

  69. Wang J, Zhu X, Gong S, Li W (2018) Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp 2275–2284. https://doi.org/10.1109/CVPR.2018.00242

  70. Wei L, Zhang S, Gao W, Tian Q (2018) Person transfer GAN to bridge domain gap for person re-identification. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, Salt Lake City, UT, USA, 2018, pp 79–88. https://doi.org/10.1109/CVPR.2018.00016

  71. Wu D, Wang C, Wu Y, Huang D-S (2021) Attention deep model with multi-scale deep supervision for person re-identification. IEEE Trans Emerg Top Comput Intell 5(1):70–78

    Article  Google Scholar 

  72. Xiang S, Fu Y, Xie M, Yu Z, Liu T (2020) Unsupervised person re-identification by hierarchical cluster and domain transfer. Multimed Tool Appl 79:19769–19786

    Article  Google Scholar 

  73. Yang F, Yan K, Lu S, Jia H, Xie D, Yu Z, Guo X, Huang F, Gao W (2021) Part-aware progressive unsupervised domain adaptation for person re-identification. IEEE Trans Multimedia 23:1681–1695

    Article  Google Scholar 

  74. Yang W, Huang H, Zhang Z, Chen X, Huang K, Zhang S (2019) Towards rich feature discovery with class activation maps augmentation for person re-identification. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp 1389–1398. https://doi.org/10.1109/CVPR.2019.00148

  75. Yu H-X, Zheng W-S, Wu A, Guo X, Gong S, Lai J (2019) Unsupervised person re-identification by Soft Multilabel Learning. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp 2143–2152. https://doi.org/10.1109/CVPR.2019.00225

  76. Zhai Y, Lu S, Ye Q, Shan X, Chen J, Ji R, Tian Y (2020) AD-Cluster: augmented discriminative clustering for domain adaptive person re-identification. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp 9018–9027. https://doi.org/10.1109/CVPR42600.2020.00904

  77. Zhang L, Xiang T, Gong S (2016) Learning a discriminative null space for person re-identification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp 1239–1248. https://doi.org/10.1109/CVPR.2016.139

  78. Zhang X, Yan Y, Xue J-H, Hua Y, Wang H (2021) Semantic-aware occlusion-robust network for occluded person re-identification. IEEE Trans Circuits Syst Video Technol 31(7):2764–2778

    Article  Google Scholar 

  79. Zhang Z, Lan C, Zeng W, Chen Z (2019) Densely semantically aligned person re-identification. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp 667–676. https://doi.org/10.1109/CVPR.2019.00076

  80. Zhang Z, Lan C, Zeng W, Jin X, Chen Z (2020) Relation-aware global attention for person re-identification. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp 3183–3192. https://doi.org/0.1109/CVPR42600.2020.00325

  81. Zhao H, Tian M, Sun S, Shao J, Yan J, Yi S, Wang X, Tang X (2017) Spindle net: person re-identification with human body region guided feature decomposition and fusion. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp 907–915. https://doi.org/10.1109/CVPR.2017.103

  82. Zheng F, Deng C, Sun X, Jiang X, Guo X, Yu Z, Huang F, Ji R (2019) Pyramidal person re-identification via multi-loss dynamic training. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp 8506–8514. https://doi.org/10.1109/CVPR.2019.00871

  83. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: A benchmark. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, pp 1116–1124. https://doi.org/10.1109/ICCV.2015.133

  84. Zheng W-S, Gong S, Xiang T (2009) Associating groups of people. In: Proceedings of the British Machine Vision Conference (BMVC), London

  85. Zheng Z, Yang X, Yu Z, Zheng L, Yang Y, Kautz J (2019) Joint discriminative and generative learning for person re-identification. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp 2133–2142. https://doi.org/10.1109/CVPR.2019.00224

  86. Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by GAN improve the person re-identification baseline in Vitro. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp 3774–3782. https://doi.org/10.1109/ICCV.2017.405

  87. Zhong W, Jiang L, Zhang T, Ji J, Xiong H (2020) A part-based attention network for person re-identification. Multimed Tool Appl 79:22525–22549

    Article  Google Scholar 

  88. Zhong Z, Zheng L, Zheng Z, Li S, Yang Y (2019) Camstyle: a novel data augmentation method for person re-identification. IEEE Trans Image Process 28(3):1176–1190

    Article  MathSciNet  Google Scholar 

  89. Zhou K, Yang Y, Cavallaro A, Xiang T (2019) Learning generalisable omni-scale representations for person re-identification. arXiv:1910.06827

  90. Zhou K, Yang Y, Cavallaro A, Xiang T (2019) Omni-scale feature learning for person re-identification. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019, pp 3701–3711. https://doi.org/10.1109/ICCV.2019.00380

  91. Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp 2242–2251. https://doi.org/10.1109/ICCV.2017.244

  92. Zhu X, Li Y, Sun J, Chen H, Zhu J (2021) Unsupervised domain adaptive person re-identification via camera penalty learning. Multimed Tool Appl 80:15215–15232

    Article  Google Scholar 

Download references

Funding

This work was partially supported by the Ministry of Science and Technology, Taiwan under grant no. MOST 109-2221-E-009-122-MY3.

Author information

Authors and Affiliations

  1. College of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, 30010, Taiwan

    Jia-Jen Wu, Keng-Hao Chang & I-Chen Lin

  2. Mechanical and Mechatronics Systems Research Labs, Industrial Technology Research Institute, 195, Sec. 4, Chung Hsing Rd., Hsinchu, 310401, Taiwan

    Keng-Hao Chang

Authors
  1. Jia-Jen Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Keng-Hao Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I-Chen Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I-Chen Lin.

Ethics declarations

Conflict of Interests

All the authors are affiliated with National Yang Ming Chiao Tung University(NYCU). The first author has financial interests with Cyberlink Corp. and Perfect Corp., Taiwan. The second author is also affiliated with Industrial Technology Research Institute(ITRI). The third author was a visiting scholar in University of California, Davis, US, from August 2017 to July 2018.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, JJ., Chang, KH. & Lin, IC. Generalizable person re-identification with part-based multi-scale network. Multimed Tools Appl 82, 38639–38666 (2023). https://doi.org/10.1007/s11042-023-14718-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14718-1

Keywords

Search

Navigation