Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 5/2024

25.10.2023 | Original Article

Global relational attention with a maximum suppression constraint for vehicle re-identification

verfasst von: Xiyu Pang, Yilong Yin, Xin Tian

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The goal of vehicle re-identification is to identify the same vehicle from multiple cameras, which is a challenging task. There are many solutions to this problem, among which the self-attention mechanism is very popular. It can capture the long-range dependence in an image, thereby suppressing the irrelevant features. Most of the existing designs are based on isolated pairwise query-key interactions to refine a node. They implicitly mine attention patterns without explicitly modeling node weights. In this paper, we propose a global relational attention mechanism, which makes full use of the global dependence of a node to learn and infer its weight value. Global dependence can measure the importance of nodes more robustly and efficiently. To capture more discriminative features, we propose a maximum suppression constraint to adaptively adjust weight values to expand the range of attention. In addition, we design a pair of effective attention modules based on the proposed attention mechanism, that focus on mining the discriminative features related to vehicle identities from the spatial and channel dimensions. We conduct a large number of experiments on the VeRi-776 and VehicleID datasets, and the experimental results demonstrate the effectiveness of our method.
Vorheriger Artikel Fine-tuning pretrained transformer encoders for sequence-to-sequence learning
Nächster Artikel Recursive residual Fourier transformation for single image deraining

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Weitere Produktempfehlungen anzeigen
Literatur
1.
Chen B, Deng W, Hu J (2019) Mixed high-order attention network for person re-identification. In: ICCV, pp 371–381
2.
Xu J, Zhao R, Zhu F, Wang H, Ouyang W (2018) Attention-aware compositional network for person re-identification. In: CVPR, pp 2119–2128
3.
Fang P, Zhou J, Roy SK, Petersson L, Harandi M (2019) Bilinear attention networks for person retrieval. In: ICCV, pp 8030–8039
4.
Zhang Z, Lan C, Zeng W, Xin J, Zhibo C (2020) Relation-aware global attention for person re-identification. In: CVPR, pp 3183–3192
5.
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, October, pp 274–282
6.
Lai S, Chai Z, Wei X (2021) Transformer meets part model: adaptive part division for person re-identification. In: ICCVW, pp 4133–4140
7.
8.
Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: ECCV, pp 3–19
9.
Wang F, Jiang M, Qian C, Yang S, Cheng L, Zhang H, Wang X, Tang X (2017) Residual attention network for image classification. In: CVPR, pp 3156–3164
10.
Luo W, Li Y, Urtasun R, Zemel R (2016) Understanding the effective receptive field in deep convolutional neural networks. In: NeurIPS, pp 4898–4906
11.
Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: CVPR, pp 7794–7803
12.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Lukasz K, Illia P (2017) Attention is all you need. In: NIPS, pp 5998–6008
13.
Zhang G, Zhang P, Qi J, Lu H (2021) HAT: hierarchical aggregation transformers for person re-identification. In: ACM multimedia, pp 516–525
14.
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:​2010.​11929
15.
Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision
16.
Wang W, Pei Y, Wang S, Gorrz JM, Zhang Y (2023) PSTCNN: explainable COVID-19 diagnosis using PSO-guided self-tuning CNN. Biocell 47(2):373–384CrossRef
17.
Wang W, Zhang X, Wang S-H, Zhang Y-D (2022) Covid-19 diagnosis by WE-SAJ. Syst Sci Control Eng 10(1):325–335CrossRef
18.
Chen S-Z, Guo C-C, Lai J-H (2016) Deep ranking for person re-identification via joint representation learning. IEEE Trans Image Process 25:2353–2367
19.
Luo H, Gu Y, Liao X, Lai S, Wei J (2019) Bag of tricks and a strong baseline for deep person re-identification. In: CVPR workshop
20.
Li W, Zhao R, Xiao T, Xiaogang W (2014) Deep filter pairing neural network for person reidentification. In: CVPR, Deepreid
21.
Li W, Zhu X, Gong S (2017) Person reidentification by deep joint learning of multi-loss classification. In: IJCAI
22.
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: Proceedings of the European Conference on Computer Vision (ECCV), pp 480–496
23.
Fu X, Peng J, Jiang G et al (2022) Learning latent features with local channel drop network for vehicle re-identification. Eng Appl Artif Intell 107:104540CrossRef
24.
He B, Li J, Zhao Y, Tian Y (2019) Part-regularized near-duplicate vehicle re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3997–4005
25.
Meng D, Li L, Liu X, Li Y, Yang S, Zha Z, Gao X, Wang S, Huang Q (2020) Parsing-based view-aware embedding network for vehicle reidentification. In: IEEE CVPR, pp 7101–7110
26.
Khorramshahi P, Kumar A, Peri N, Rambhatla SS, Chen J-C, Chellappa R (2019) Adual-path model with adaptive attention for vehicle reidentification. In: ICCV, pp 6132–6141
27.
Wang Z, Tang L, Liu X, Yao Z, Yi S, Shao J, Yan J, Wang S, Li H, Wang X (2017) Orientation invariant feature embedding and spatial temporal regularization for vehicle reidentification. In: IEEE ICCV, pp 379–387
28.
Zhang J, Chen J, Cao J et al (2022) Dual attention granularity network for vehicle re-identification. Neural Comput Appl 34(4):2953–2964CrossRef
29.
Liu X, Liu W, Zheng J, Yan C, Mei T (2020) Beyond the parts: learning multi-view crosspart correlation for vehicle re-identification. In: Proceedings of the 28th ACM international conference on multimedia, pp 907–915
30.
Wang C, Zhang Q, Huang C, Liu W, Wang X (2018) Mancs: a multi-task attentional network with curriculum sampling for person re-identification. In: ECCV
31.
Zhao L, Li X, Zhuang Y, Wang J (2017) Deeply-learned part-aligned representations for person reidentification. In: ICCV, pp 3239–3248
32.
Li W, Zhu X, Gong S (2018) Harmonious attention network for person re-identification. In: CVPR, pp 2285–2294
33.
Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:​1810.​04805
34.
Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, Jégou H (2020) Training data-efficient image transformers and distillation through attention. arXiv preprint arXiv:​2012.​12877
35.
Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2020) Deformable detr: deformable transformers for end-to-end object detection. arXiv preprint arXiv:​2010.​04159
36.
Nicolas C, Francisco M, Gabriel S, Nicolas U, Alexander K, Sergey Z (2020) End-to end object detection with transformers. In: ECCV 1:2
37.
Zhu K, Guo H, Zhang S, Wang Y, Huang G, Qiao H, Liu J, Wang J, Tang M (2021) AAformer: auto-aligned transformer for person re-identification. CoRR arXiv:​2104.​00921
38.
Huang P, Huang R, Huang J, Yangchen R, He Z, Junzhou C, Xiying L (2019) Deep feature fusion with multiple granularity for vehicle re-identification. In: CVPR workshops, pp 80–88
39.
Chen H, Lagadec B, Brémond F (2019) Partition and reunion: a two-branch neural network for vehicle re-identification. In: CVPR workshops, pp 184–192
40.
41.
Gao C, Yi H, Zhang Y, Yao R, Yong Z, Jiaqi Z (2020) Vehicle re-identification based on complementary features. In: CVPR workshops, pp 2520–2526
42.
Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 815–823
43.
Zhuge C, Peng Y, Li Y, Ai J, Chen J (2020) Attribute-guided feature extraction and augmentation robust learning for vehicle re-identification. In: CVPR workshops, pp 2632–2637
44.
Tieleman T, Hinton G (2012) Lecture 6.5—RMSProp, COURSERA: neural networks for machine learning. Technical report
45.
John D, Elad H, Yoram S (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159MathSciNet
46.
Kingma DP, Jimmy B (2015) Adam: a method for stochastic optimization. In: ICLR (poster)
47.
Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch
48.
Liu X, Liu W, Tao M, Huadong M (2016) A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: ECCV (2), pp 869–884
49.
Liu H, Tian Y, Wang Y, Pang L, Huang T (2016) Deep relative distance learning: tell the difference between similar vehicles. In: CVPR, pp 2167–2175
50.
Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009). Imagenet: a large-scale hierarchical image database. In: CVPR, pp 248–255
51.
Yang L, Luo P, Loy CC, Tang X (2015) A large-scale car dataset for fine-grained categorization and verification. In: IEEE CVPR, pp 3973–3981
52.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE CVPR, pp 1–9
53.
Xinchen Liu W, Liu TM, Ma H (2018) PROVID: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans Multimed 20(3):645–658CrossRef
54.
Yi Z, Ling S (2018) Viewpoint-aware attentive multiview inference for vehicle re-identification. In: IEEE CVPR, pp 6489–6498
55.
Lou Y, Bai Y, Liu J, Wang S, Duan LY (2019) Embedding adversarial learning for vehicle reidentification. IEEE Trans Image Process 28(8):3794–3807MathSciNetCrossRef
56.
Liu X, Zhang S, Huang Q, Gao W (2018) RAM: a region-aware deep model for vehicle reidentification. In: ICME, pp 1–6
57.
Khorramshahi P, Kumar A, Peri N, Rambhatla SS, Chen J-C, Chellappa R (2019) Adual path model with adaptive attention for vehicle reidentification. arXiv preprint arXiv:​1905.​03397
58.
59.
Li K, Ding Z, Zhang Y et al (2022) Vehicle and person re-identification with support neighbor loss. IEEE Trans Neural Netw Learn Syst 33(2):826–838CrossRef
60.
Gu J, Wang K, Luo H, Chen C, Jiang W, Fang Y, Zhang S, You Y, Zhao J (2023) MSINet: twins contrastive search of multi-scale interaction for object ReID. CoRR arXiv:​2303.​07065
61.
Huang F, Lv X, Zhang L (2023) Coarse-to-fine sparse self-attention for vehicle re-identification. Knowl Based Syst 270:110526CrossRef
62.
Tumrani S, Ali W, Kumar R et al (2023) View-aware attribute-guided network for vehicle re-identification. Multimed Syst 29:1853–1863CrossRef
63.
Zhu W, Wang Z, Wang X, Ruimin H, Liu H, Liu C, Wang C, Li D (2023) A dual self-attention mechanism for vehicle re-identification. Pattern Recognit 137:109258CrossRef
64.
Guo H, Zhu K, Tang M, Wang J (2019) Two-level attention network with multi-grain ranking loss for vehicle re-identification. IEEE Trans Image Process 28(9):4328–4338MathSciNetCrossRef
65.
Huynh SV (2021) A strong baseline for vehicle re-identification. In: CVPR workshops, pp 4147–4154
66.
Zhedong Z, Tao R, Yunchao W, Yi Y, Tao M (2021) VehicleNet: learning robust visual representation for vehicle re-identification. IEEE Trans Multim 23:2683–2693CrossRef
67.
Rodolfo Q, Cuiling L, Wenjun Z, Hélio P (2021) AttributeNet: attribute enhanced vehicle re-identification. Neurocomputing 465:84–92CrossRef
68.
Zhou W, Lian JW, Zhu SZ, Wu Y, Wang DH (2023) Vehicle re-identification by separating representative spatial features. Cogn Comput 15(5):1640–1655CrossRef
Metadaten
Titel
Global relational attention with a maximum suppression constraint for vehicle re-identification
verfasst von
Xiyu Pang
Yilong Yin
Xin Tian
Publikationsdatum
25.10.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2024
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-023-01993-5

Weitere Artikel der Ausgabe 5/2024

International Journal of Machine Learning and Cybernetics 5/2024 Zur Ausgabe

Original Article

Filtering level-set model based on saliency and gradient information for sonar image segmentation

Original Article

Fine-tuning pretrained transformer encoders for sequence-to-sequence learning

Original Article

Feature selection for label distribution learning under feature weight view

Original Article

A new robust contrastive learning for unsupervised person re-identification

Original Article

Skeleton-based 3D human pose estimation with low-resolution infrared array sensor using attention based CNN-BiGRU

Original Article

RDMA: low-light image enhancement based on retinex decomposition and multi-scale adjustment

Neuer Inhalt

    Bildnachweise