Home Computational Visual Media Article
PDF (6.3 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Figures (9)

Tables (10)
Table 1
Table 2
Table 3
Table 4
Table 5
Show 5 more tables Hide 5 tables
Research Article | Open Access

SAM-driven MAE pre-training and background-aware meta-learning for unsupervised vehicle re-identification

Dong Wang1, Qi Wang2,3,4, Weidong Min2,3,4(), Di Gai2,3,4, Qing Han2,3,4, Longfei Li2, Yuhan Geng5
School of Software, Nanchang University, Nanchang 330047, China
School of Mathematics and Computer Science, Nanchang University, Nanchang 330031, China
Institute of Metaverse, Nanchang University, Nanchang 330031, China
Jiangxi Key Laboratory of Smart City, Nanchang 330031, China
School of Public Health, University of Michigan, Ann Arbor 48109, USA
Show Author Information

Graphical Abstract

View original image Download original image

Abstract

Distinguishing identity-unrelated background information from discriminative identity information poses a challenge in unsupervised vehicle re-identification (Re-ID). Re-ID models suffer from varying degrees of background interference caused by continuous scene variations. The recently proposed segment anything model (SAM) has demonstrated exceptional performance in zero-shot segmentation tasks. The combination of SAM and vehicle Re-ID models can achieve efficient separation of vehicle identity and background information. This paper proposes a method that combines SAM-driven mask autoencoder (MAE) pre-training and background-aware meta-learning for unsupervised vehicle Re-ID. The method consists of three sub-modules. First, the segmentation capacity of SAM is utilized to separate the vehicle identity region from the background. SAM cannot be robustly employed in exceptional situations, such as those with ambiguity or occlusion. Thus, in vehicle Re-ID downstream tasks, a spatially-constrained vehicle background segmentation method is presented to obtain accurate background segmentation results. Second, SAM-driven MAE pre-training utilizes the aforementioned segmentation results to select patches belonging to the vehicle and to mask other patches, allowing MAE to learn identity-sensitive features in a self-supervised manner. Finally, we present a background-aware meta-learning method to fit varying degrees of background interference in different scenarios by combining different background region ratios. Our experiments demonstrate that the proposed method has state-of-the-art performance in reducing background interference variations.

Keywords

unsupervisedre-identification (Re-ID)vehiclessegmentationautoencodermeta-learning

References

[1]

Lei, J.; Qin, T.; Peng, B.; Li, W.; Pan, Z.; Shen, H.; Kwong, S. Reducing background induced domain shift for adaptive person re-identification. IEEE Transactions on Industrial Informatics Vol. 19, No. 6, 7377–7388, 2023.

Crossref Google Scholar
[2]

Zhang, G.; Zhang, H.; Lin, W.; Chandran, A. K.; Jing, X. Camera contrast learning for unsupervised person re-identification. IEEE Transactions on Circuits and Systems for Video Technology Vol. 33, No. 8, 4096–4107, 2023.

Crossref Google Scholar
[3]

Zhu, K.; Guo, H.; Liu, S.; Wang, J.; Tang, M. Learning semantics-consistent stripes with self-refinement for person re-identification. IEEE Transactions on Neural Networks and Learning Systems Vol. 34, No. 11, 8531–8542, 2023.

Crossref Google Scholar
[4]
Wu, M.; Zhang, Y.; Zhang, T.; Zhang, W. Background segmentation for vehicle re-identification. In: MultiMedia Modeling. Lecture Notes in Computer Science, Vol. 11962. Springer Cham, 88–99, 2020.
Crossref
[5]
Munir, A.; Martinel, N.; Micheloni, C. Oriented splits network to distill background for vehicle re-identification. In: Proceedings of the 17th IEEE International Conference on Advanced Video and Signal Based Surveillance, 1–8, 2021.
Crossref
[6]

Lu, Z.; Lin, R.; Hu, H. MART: Mask-aware reasoning transformer for vehicle re-identification. IEEE Transactions on Intelligent Transportation Systems Vol. 24, No. 2, 1994–2009, 2023.

Crossref Google Scholar
[7]

Ning, X.; Gong, K.; Li, W.; Zhang, L.; Bai, X.; Tian, S. Feature refinement and filter network for person re-identification. IEEE Transactions on Circuits and Systems for Video Technology Vol. 31, No. 9, 3391–3402, 2021.

Crossref Google Scholar
[8]
Li, Z.; Deng, Y.; Tang, Z.; Huang, J. SFMNet: Self-guided feature mining network for vehicle re-identification. In: Proceedings of the International Joint Conference on Neural Networks, 1–8, 2023.
Crossref
[9]
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; Girshick, R. Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 15979–15988, 2022.
Crossref
[10]
Kirillov, A.; Mintun, E.; Ravi, N.; Mao, H.; Rolland, C.; Gustafson, L.; Xiao, T.; Whitehead, S.; Berg, A. C.; Lo, W. Y.; et al. Segment anything. arXiv preprint arXiv:2304.02643, 2023.
Crossref
[11]

Lin, Y.; Wu, Y.; Yan, C.; Xu, M.; Yang, Y. Unsupervised person re-identification via cross-camera similarity exploration. IEEE Transactions on Image Processing Vol. 29, 5481–5490, 2020.

Crossref Google Scholar
[12]

Wang, H.; Lu, J.; Pang, F.; Zhou, J.; Zhang, K. Bi-directional style adaptation network for person re-identification. IEEE Sensors Journal Vol. 22, No. 12, 12339–12347, 2022.

Crossref Google Scholar
[13]
Lou, Y.; Bai, Y.; Liu, J.; Wang, S.; Duan, L. VERI-wild: A large dataset and a new method for vehicle re-identification in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3230–3238, 2019.
Crossref
[14]
Kamenou, E.; Del Rincón, J. M.; Miller, P.; Devlin-Hill, P. A meta-learning approach for domain generalisation across visual modalities in vehicle re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 385–393, 2023.
Crossref
[15]

Zhang, L.; Liu, Z.; Zhang, W.; Zhang, D. Style uncertainty based self-paced meta learning for generalizable person re-identification. IEEE Transactions on Image Processing Vol. 32, 2107–2119, 2023.

Crossref Google Scholar
[16]
Ni, H.; Song, J.; Luo, X.; Zheng, F.; Li, W.; Shen, H. T. Meta distribution alignment for generalizable person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2477–2486, 2022.
Crossref
[17]

Zheng, Z.; Ruan, T.; Wei, Y.; Yang, Y.; Mei, T. VehicleNet: Learning robust visual representation for vehicle re-identification. IEEE Transactions on Multimedia Vol. 23, 2683–2693, 2020.

Crossref Google Scholar
[18]
Yu, J.; Oh, H. Unsupervised vehicle re-identification via self-supervised metric learning using feature dictionary. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 3806–3813, 2021.
Crossref
[19]

Lu, Z.; Lin, R.; He, Q.; Hu, H. Mask-aware pseudo label denoising for unsupervised vehicle re-identification. IEEE Transactions on Intelligent Transportation Systems Vol. 24, No. 4, 4333–4347, 2023.

Crossref Google Scholar
[20]

He, Z.; Zhao, H.; Wang, J.; Feng, W. Multi-level progressive learning for unsupervised vehicle re-identification. IEEE Transactions on Vehicular Technology Vol. 72, No. 4, 4357–4371, 2023.

Crossref Google Scholar
[21]

Wang, P.; Ding, C.; Tan, W.; Gong, M.; Jia, K.; Tao, D. Uncertainty-aware clustering for unsupervised domain adaptive object re-identification. IEEE Transactions on Multimedia Vol. 25, 2624–2635, 2022.

Crossref Google Scholar
[22]

Dai, P.; Chen, P.; Wu, Q.; Hong, X.; Ye, Q.; Tian, Q.; Lin, C. W.; Ji, R. Disentangling task-oriented representations for unsupervised domain adaptation. IEEE Transactions on Image Processing Vol. 31, 1012–1026, 2022.

Crossref Google Scholar
[23]

Wei, R.; Gu, J.; He, S.; Jiang, W. Transformer-based domain-specific representation for unsupervised domain adaptive vehicle re-identification. IEEE Transactions on Intelligent Transportation Systems Vol. 24, No. 3, 2935–2946, 2023.

Crossref Google Scholar
[24]
Wu, C.; Lin, Z.; Cohen, S.; Bui, T.; Maji, S. PhraseCut: Language-based image segmentation in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10213–10222, 2020.
Crossref
[25]
Yang, Z.; Wang, J.; Tang, Y.; Chen, K.; Zhao, H.; Torr, P. H. S. LAVT: Language-aware vision transformer for referring image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18134–18144, 2022.
Crossref
[26]
Xie, J.; Hou, X.; Ye, K.; Shen, L. CLIMS: Cross language image matching for weakly supervised semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4473–4482, 2022.
Crossref
[27]
Wang, X.; Zhang, X.; Cao, Y.; Wang, W.; Shen, C.; Huang, T. SegGPT: Segmenting everything in context. arXiv preprint arXiv:2304.03284, 2023.
Crossref
[28]

Peng, J.; Jiang, G.; Chen, D.; Zhao, T.; Wang, H.; Fu, X. Eliminating cross-camera bias for vehicle re-identification. Multimedia Tools and Applications Vol. 81, No. 24, 34195–34211, 2022.

Crossref Google Scholar
[29]
Khorramshahi, P.; Peri, N.; Chen, J. C.; Chellappa, R. The devil is in the details: Self-supervised attention for vehicle re-identification. In: Computer Vision – ECCV 2020. Lecture Notes in Computer Science, Vol. 12359. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 369–386, 2020.
Crossref
[30]
Zhu, X.; Luo, Z.; Fu, P.; Ji, X. VOC-RelD: Vehicle re-identification based on vehicle-orientation-camera. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2566–2573, 2020.
Crossref
[31]
Ester, M.; Kriegel, H. P.; Sander, J.; Xu, X. Adensity-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 226–231, 1996.
[32]
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning, 8748–8763, 2021.
[33]
He, S.; Luo, H.; Wang, P.; Wang, F.; Li, H.; Jiang, W. TransReID: Transformer-based object re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 14993–15002, 2021.
Crossref
[34]
Li, J.; Wang, M.; Gong, X. Transformer based multi-grained features for unsupervised person re-identification. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 1–9, 2023.
Crossref
[35]

Li, M.; Li, C. G.; Guo, J. Cluster-guided asymmetric contrastive learning for unsupervised person re-identification. IEEE Transactions on Image Processing Vol. 31, 3606–3617, 2022.

Crossref Google Scholar
[36]

Han, X.; Yu, X.; Li, G.; Zhao, J.; Pan, G.; Ye, Q.; Jiao, J.; Han, Z. Rethinking sampling strategies for unsupervised person re-identification. IEEE Transactions on Image Processing Vol. 32, 29–42, 2023.

Crossref Google Scholar
[37]
Hu, Z.; Zhu, C.; He, G. Hard-sample guided hybrid contrast learning for unsupervised person re-identification. In: Proceedings of the 7th IEEE International Conference on Network Intelligence and Digital Content, 91–95, 2021.
Crossref
[38]
Zhang, X.; Li, D.; Wang, Z.; Wang, J.; Ding, E.; Shi, J. Q.; Zhang, Z.; Wang, J. Implicit sample extension for unsupervised person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7359–7368, 2022.
Crossref
[39]
Yang, F.; Zhong, Z.; Luo, Z.; Cai, Y.; Lin, Y.; Li, S.; Sebe, N. Joint noise-tolerant learning and meta camera shift adaptation for unsupervised person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4853–4862, 2021.
Crossref
[40]
Ge, Y.; Zhu, F.; Chen, D.; Zhao, R.; Li, H. Self-paced contrastive learning with hybrid memory for domain adaptive object re-ID. arXiv preprint arXiv:2006.02713, 2020.
[41]
Ge, Y.; Chen, D.; Li, H. Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification. arXiv preprint arXiv:2001.01526, 2020.
[42]
Liu, X.; Zhang, S. Graph consistency based mean-teaching for unsupervised domain adaptive person re-identification. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence, 874–880, 2021.
Crossref
[43]

Ding, Y.; Fan, H.; Xu, M.; Yang, Y. Adaptive exploration for unsupervised person re-identification. ACM Transactions on Multimedia Computing, Communications, and Applications Vol. 16, No. 1, Article No. 3, 2020.

Crossref Google Scholar
[44]

Wang, W.; Zhao, F.; Liao, S.; Shao, L. Attentive WaveBlock: Complementarity-enhanced mutual networks for unsupervised domain adaptation in person re-identification and beyond. IEEE Transactions on Image Processing Vol. 31, 1532–1544, 2022.

Crossref Google Scholar
[45]
Zheng, K.; Liu, W.; He, L.; Mei, T.; Luo, J.; Zha, Z. J. Group-aware label transfer for domain adaptive person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5306–5315, 2021.
Crossref
[46]
Wang, D.; Zhang, S. Unsupervised person re-identification via multi-label classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10978–10987, 2020.
Crossref
[47]

Lin, Y.; Dong, X.; Zheng, L.; Yan, Y.; Yang, Y. A bottom-up clustering approach to unsupervised person re-identification. Proceedings of the AAAI Conference on Artificial Intelligence Vol. 33, No. 1, 8738–8745, 2019.

Crossref Google Scholar
[48]
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/CVF Conference on Computer Vision and Pattern Recognition, 598–607, 2019.
Crossref
[49]
Yu, H. X.; Zheng, W. S. Weakly supervised discriminative feature learning with state information for person identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 5527–5537, 2020.
Crossref
[50]
Lin, Y.; Xie, L.; Wu, Y.; Yan, C.; Tian, Q. Unsupervised person re-identification via softened similarity learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3387–3396, 2020.
Crossref
[51]
Li, J.; Zhang, S. Joint visual and temporal consistency for unsupervised domain adaptive person re-identification. In: Computer Vision – ECCV 2020. Lecture Notes in Computer Science, Vol. 12369. Vedaldi, A.; Bischof, H.; Brox, T.; Frahm, J. M. Eds. Springer Cham, 483–499, 2020.
Crossref
[52]
Zeng, K.; Ning, M.; Wang, Y.; Guo, Y. Hierarchical clustering with hard-batch triplet loss for person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13654–13662, 2020.
Crossref
[53]

Jin, X.; Lan, C.; Zeng, W.; Chen, Z. Uncertainty-aware multi-shot knowledge distillation for image-based object re-identification. Proceedings of the AAAI Conference on Artificial Intelligence Vol. 34, No. 7, 11165–11172, 2020.

Crossref Google Scholar
[54]
Zhouy, Y.; Shao, L. Viewpoint-aware attentive multi-view inference for vehicle re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 6489–6498, 2018.
Crossref
[55]
Rao, Y.; Chen, G.; Lu, J.; Zhou, J. Counterfactual attention learning for fine-grained visual categorization and re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 1005–1014, 2021.
Crossref
[56]

Jin, X.; Lan, C.; Zeng, W.; Wei, G.; Chen, Z. Semantics-aligned representation learning for person re-identification. Proceedings of the AAAI Conference on Artificial Intelligence Vol. 34, No. 7, 11173–11180, 2020.

Crossref Google Scholar
[57]

Yan, C.; Pang, G.; Bai, X.; Liu, C.; Ning, X.; Gu, L.; Zhou, J. Beyond triplet loss: Person re-identification with fine-grained difference-aware pairwise loss. IEEE Transactions on Multimedia Vol. 24, 1665–1677, 2021.

Crossref Google Scholar
[58]
Bai, Y.; Lou, Y.; Dai, Y.; Liu, J.; Chen, Z.; Duan, L.-Y.; Pillar, I. Disentangled feature learning network for vehicle re-identification. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence, 474–480, 2020.
Crossref
[59]

Lou, Y.; Bai, Y.; Liu, J.; Wang, S.; Duan, L. Y. Embedding adversarial learning for vehicle re-identification. IEEE Transactions on Image Processing Vol. 28, No. 8, 3794–3807, 2019.

Crossref Google Scholar
Computational Visual Media
Volume 10 Issue 4,
August 2024
Pages 771-789
DOI: 10.1007/s41095-024-0424-2
Cite this article:
Wang D, Wang Q, Min W, et al. SAM-driven MAE pre-training and background-aware meta-learning for unsupervised vehicle re-identification. Computational Visual Media, 2024, 10(4): 771-789. https://doi.org/10.1007/s41095-024-0424-2
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return

SAM-driven MAE pre-training performance using different preservation rates and VeRi-776

Preservation rate Rank-1 Rank-5 mAP
75% 72.0 77.9 26.3
50% 78.0 84.6 32.9
25% 83.3 88.0 36.7

Performance comparison of meta-learning strategies based on different attributes in VeRi-776. Bg ratio refers to background region ratio

Split attribute Rank-1 Rank-5 mAP
Vehicle Model 82.4 86.5 36.7
Color 81.2 86.1 36.5
Bg ratio (Pixel Level) 83.8 87.4 37.9
Bg ratio (Ours) 86.8 90.6 38.4

Using different baselines with the proposed method on VeRi-776 and VeRi-Wild datasets. TransReID-I and TransReID-D respectively refer to the TransReID baseline pre-trained on ImageNet and DeiT, TMGF-L refers to the TMGF baseline pre-trained on Luperson, and MAE-Random represents the CLIP-based baseline constructed in this paper

Module VeRi-776 VeRi-Wild
Test3000 Test5000 Test10000
Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP
Ours (TransReID -I) 78.9 83.8 36.6 58.6 80.1 31.1 53.0 75.6 28.7 40.7 64.0 21.8
Ours (TransReID-D) 79.8 85.8 36.9 61.4 81.5 32.8 50.7 74.1 27.3 43.3 66.3 23.2
Ours (TMGF-L) 79.5 85.7 33.7 60.6 82.0 33.1 53.2 74.9 28.4 43.4 65.7 23.1
Ours (MAE-Random) 86.8 90.6 38.4 63.3 83.6 34.0 54.5 77.1 29.4 44.4 67.4 23.9

Ablation study: using different modules on VeRi-776 and VeRi-Wild datasets

Different module VeRi-776 VeRi-Wild
Test3000 Test5000 Test10000
Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP
MAE (Random) 68.2 74.8 24.5 55.7 75.2 30.5 48.0 67.9 26.0 39.1 59.4 20.9
Ours (without Bg-Meta) 83.3 88.0 36.7 56.5 75.1 30.6 48.5 68.7 26.2 39.8 60.4 21.1
Ours (without SAM-driven MAE) 83.4 87.8 37.9 52.7 71.6 29.2 44.7 64.6 24.7 36.3 56.0 19.7
Ours (with Patch-Seg) 72.6 78.4 29.9 54.9 75.9 29.4 47.4 68.8 25.1 38.3 59.3 20.0
Ours 86.8 90.6 38.4 63.3 83.6 34.0 54.5 77.1 29.4 44.4 67.4 23.9

Effects of different training strategies for UDA tasks

Strategy VeRi-Wild→VeRi-776 VeRi-776→VeRi-Wild
Test3000 Test5000 Test10000
Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP
MAE (S) 88.1 94.0 58.8 61.7 83.1 34.7 54.2 76.4 29.9 43.7 67.2 24.1
MAE (T) 88.9 93.7 56.1 63.3 84.0 34.7 54.5 77.1 30.0 44.0 67.7 24.2
MAE (S+T) 88.7 93.4 52.3 62.7 82.8 34.3 52.9 76.2 29.4 42.7 66.8 23.6

Comparison of the proposed method to state-of-the-art methods on VeRi-776 and VeRi-Wild datasets

Method VeRi-776 VeRi-Wild
Test3000 Test5000 Test10000
Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP
SSML 74.5 80.3 26.7 49.6 71.0 23.7 43.9 64.9 20.4 34.7 55.4 15.8
CACL 57.0 69.7 24.5 57.5 80.7 30.1 49.3 73.8 26.0 39.3 64.6 20.5
GroupSampling 77.8 83.8 35.0 58.8 80.9 30.9 51.7 74.1 26.9 41.2 64.8 21.5
HHCL 65.2 70.9 30.2 57.5 78.5 30.4 49.1 72.6 26.1 39.1 62.2 20.6
ISE 66.0 72.5 27.7 61.2 81.8 32.9 54.2 75.6 29.0 44.3 66.7 23.5
MetaCam 72.6 80.3 29.6 58.8 79.9 31.2 51.3 74.2 27.2 41.0 64.7 21.6
SpCL 65.6 74.0 28.3 59.8 81.1 31.4 50.9 74.7 27.1 40.8 65.1 21.7
Strong Baseline 58.5 69.0 23.8 50.1 74.9 25.6 42.4 67.9 21.8 32.7 57.2 16.8
MMT 60.9 69.0 25.4 60.6 82.5 31.8 52.1 76.8 27.6 42.4 66.8 22.2
GCMT 80.6 87.2 35.2 54.9 78.3 29.3 45.7 71.3 25.0 36.1 60.3 19.5
AE 32.4 48.9 9.0 12.8 23.5 4.2 10.0 19.1 3.3 7.3 14.9 2.4
Ours 86.8 90.6 38.4 63.3 83.6 34.0 54.5 77.1 29.4 44.4 67.4 23.9

Comparison of the proposed method to state-of-the-art UDA vehicle Re-ID methods on source dataset → target dataset tasks

Method VeRi-Wild→VeRi-776 VeRi-776→VeRi-Wild
Test3000 Test5000 Test10000
Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP Rank-1 Rank-5 mAP
HHCL 31.5 42.7 11.6 41.7 65.0 18.5 35.6 58.2 15.7 26.8 47.7 11.8
MetaCam 65.3 76.6 26.7 25.8 41.7 10.2 22.3 36.2 8.5 16.9 30.1 6.4
SpCL 73.7 81.7 35.5 60.8 82.2 32.2 52.7 76.1 28.1 42.3 66.8 22.4
Strong Baseline 65.3 75.1 26.5 57.6 80.8 30.4 50.2 74.5 26.3 39.7 63.6 20.7
MMT 70.1 78.5 31.5 61.9 83.0 32.4 53.2 76.9 28.1 42.8 67.2 22.6
AE 75.9 86.6 40.2 34.0 55.9 15.1 29.0 51.1 12.2 21.6 41.0 8.7
AWB 81.2 86.4 38.2 59.0 80.4 31.9 51.0 74.0 27.4 40.9 64.8 21.9
GLT 78.1 83.5 36.7 56.4 78.9 29.9 48.0 71.9 25.5 38.0 62.3 20.1
Ours 88.9 93.7 56.1 63.3 84.0 34.7 54.5 77.1 30.0 44.0 67.7 24.2

Comparison of complexity and performance on the VeRi-776 dataset. B-A indicates background awareness

Method Stage B-A mAP Rank-1
ISE 1 27.7 66.0
MetaCam 1 29.6 72.6
SSML 1 26.7 74.5
GroupSampling 1 35.0 77.8
GCMT 1 35.2 80.6
Ours 2 38.4 86.8

Comparison to state-of-the-art unsupervised person Re-ID methods on the Market-1501 dataset

Method Rank-1 Rank-5 mAP
MMCL 80.3 89.4 45.5
BUC 66.2 79.6 38.3
ECN 49.0 61.7 24.5
WFDR 62.0 75.1 42.4
SSL 71.7 83.8 37.8
JVTC 79.5 89.2 47.5
AE 63.2 75.4 39.0
MetaCam 83.9 92.3 61.7
HCT 80.0 91.6 56.4
Ours 87.7 93.2 81.2

Comparison to state-of-the-art supervised vehicle Re-ID methods on the VeRi-776 dataset

Method Rank-1 Rank-5 mAP
UMTS 95.8 — 75.9
AAMI 85.9 91.8 61.3
CAL 95.4 97.9 74.3
FDA-NeT 84.3 92.4 55.5
SAN 93.3 97.1 72.5
FIDI 95.7 — 77.6
DFLNet 93.2 97.6 73.3
EALN 84.4 94.1 57.4
Ours 96.1 97.9 87.8