Details of Research Outputs

TitleLearning siamese network with top-down modulation for visual tracking
Author (Name in English or Pinyin)
Yao, Y.1; Wu, X.1; Zuo, W.1; Zhang, D.1,2
Date Issued2018
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN03029743
DOI10.1007/978-3-030-02698-1_33
Indexed BySCOPUS
Firstlevel Discipline信息科学与系统科学
Education discipline科技类
Published range国外学术期刊
Volume Issue Pages卷: 11266 LNCS 页: 378-388
References
[1] Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.: Staple: complementary learners for real-time tracking. In: CVPR, pp. 1401–1409 (2016)
[2] Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi. org/10.1007/978-3-319-48881-3 56
[3] Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: ICCV, pp. 4310–4318 (2015)
[4] Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)
[5] Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi. org/10.1007/978-3-319-10593-2_13
[6] Held, D., Thrun, S., Savarese, S.: Learning to track at 100 FPS with deep regression networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 749–765. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0 45
[7] Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. TPAMI 37(3), 583–596 (2015)
[8] Kristan, M., et al.: The Visual Object Tracking VOT2016 Challenge Results, October 2016. http://www.springer.com/gp/book/9783319488806
[9] Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)
[10] Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: ICCV, pp. 3074–3082 (2015)
[11] Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: CVPR, pp. 4293–4302 (2015)
[12] Qi, Y., et al.: Hedged deep tracking. In: CVPR, pp. 4303–4311 (2016)
[13] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)
[14] Shrivastava, A., Sukthankar, R., Malik, J., Gupta, A.: Beyond skip connections: top-down modulation for object detection. arXiv:1612.06851 (2016)
[15] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
[16] Tao, R., Gavves, E., Smeulders, A.W.: Siamese instance search for tracking. In: CVPR, pp. 1420–1429 (2016)
[17] Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.: End-to-End representation learning for correlation filter based tracking. In: CVPR, pp. 5000– 5008 (2017)
[18] Vedaldi, A., Lenc, K.: MatConvNet: convolutional neural networks for MATLAB. In: ICM, pp. 689–692. ACM (2015)
[19] Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: ICCV, pp. 3119–3127 (2015)
[20] Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: CVPR, pp. 2411–2418 (2013)
[21] Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. TPAMI 37(9), 1834– 1848 (2015)
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Identifierhttps://irepository.cuhk.edu.cn/handle/3EPUXD0A/1150
CollectionSchool of Data Science
Corresponding AuthorWu, X.
Affiliation
1.Harbin Institute of Technology, Harbin, China
2.The Chinese University of Hong Kong (Shenzhen), Shenzhen, China
Recommended Citation
GB/T 7714
Yao, Y.,Wu, X.,Zuo, W.et al. Learning siamese network with top-down modulation for visual tracking[J]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2018.
APA Yao, Y., Wu, X., Zuo, W., & Zhang, D. (2018). Learning siamese network with top-down modulation for visual tracking. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
MLA Yao, Y.,et al."Learning siamese network with top-down modulation for visual tracking".Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2018).
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