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Fully-Convolutional Siamese Networks for Object Tracking
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“Current prevailing trackers, including both the offline Siamese trackers and the online learning models, are almost all build upon convolutional operations [2, 37, 3, 47].”
From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [2]
In the citing paper
“For most of the popular trackers (such as SiamFC [1], SiamRPN [22], and ATOM [9]), correlation plays a critical role in integrating the template or target information into the regions of interest (ROI).”
From Transformer Tracking (2021) · cites this paper as [1]
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542
High-Speed Tracking with Kernelized Correlation Filters
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753
Self-citationParkhi, O. M., Vedaldi, A., & Zisserman, A. (2015) · Procedings of the British Machine Vision Conference 2015 · doi:10.5244/c.29.41 · Cited by 5,479
Self-citationCNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313
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Self-citationVideoMatch: Matching Based Video Object Segmentation
Hu, Y.-T., Huang, J.-B., & Schwing, A. G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01237-3_4 · Cited by 274
Structured Siamese Network for Real-Time Visual Tracking
Zhang, Y., Wang, L., Qi, J., Wang, D., Feng, M., & Lu, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_22 · Cited by 272
Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review
Sahu, C. K., Young, C., & Rai, R. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1859636 · Cited by 286
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Gao, S., Zhou, C., & Zhang, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01792 · Cited by 271
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Wang, G., Luo, C., Xiong, Z., & Zeng, W. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00376 · Cited by 269
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Jiao, L., Zhang, R., Liu, F., Yang, S., Hou, B., Li, L., & Tang, X. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3053249 · Cited by 274
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Le, N., Rathour, V. S., Yamazaki, K., Luu, K., & Savvides, M. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10061-9 · Cited by 271
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Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R. P., Kämäräinen, J., Danelljan, M., Zajc, L. Č., Lukežič, A., Drbohlav, O., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-68238-5_39 · Cited by 262
Self-citationThe Unmanned Aerial Vehicle Benchmark: Object Detection, Tracking and Baseline
Yu, H., Li, G., Zhang, W., Huang, Q., Du, D., Tian, Q. C., & Sebe, N. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01266-1 · Cited by 263
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Song, Z., Yu, J., Chen, Y. P., & Yang, W. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00859 · Cited by 253
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Lu, X., Ma, C., Ni, B., Yang, X., Reid, I. D., & Yang, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_22 · Cited by 251
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Karaev, N., Rocco, I., Graham, B., Neverova, N., Vedaldi, A., & Rupprecht, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73033-7_2 · Cited by 250
Self-citationJoint Monocular 3D Vehicle Detection and Tracking
Hu, H.-N., Cai, Q.-Z., Wang, D., Lin, J., Sun, M., Kraehenbuehl, P., Darrell, T., & Yu, F. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00549 · Cited by 249
RANet: Ranking Attention Network for Fast Video Object Segmentation
Wang, Z., Xu, J., Liu, L., Zhu, F., & Shao, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00408 · Cited by 247
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Yan, B., Zhang, X., Wang, D., Lu, H., & Yang, X. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00525 · Cited by 246
Siamese Network for RGB-D Salient Object Detection and Beyond
Fu, K., Fan, D.-P., Ji, G.-P., Zhao, Q., Shen, J., & Zhu, C. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3073689 · Cited by 248
Deep convolutional neural networks for thermal infrared object tracking
Liu, Q., Lu, X., He, Z., Zhang, C., & Chen, W.-S. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.07.032 · Cited by 242
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Choi, J., Chang, H. J., Fischer, T., Yun, S., Lee, K., Jeong, J., Demiris, Y., & Choi, J. Y. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00057 · Cited by 238
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Huang, C., Lucey, S., & Ramanan, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.21 · Cited by 238
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Liao, Y., Liu, S., Li, G., Wang, F., Chen, Y., Qian, C., & Li, B. H. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01089 · Cited by 242
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Xu, T., Feng, Z., Wu, X., & Kittler, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00804 · Cited by 232
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Zhang, L., Danelljan, M., González-García, A., van de Weijer, J., & Khan, F. S. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00278 · Cited by 234
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Zhang, H., Li, Y., Jiang, Y., Wang, P., Shen, Q., & Shen, C. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2902568 · Cited by 229
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Sun, C., Wang, D., Lu, H., & Yang, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00934 · Cited by 226
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object SegmentationPreprint
Cheng, H. K., Tai, Y., & Tang, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05210 · Cited by 224
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Zheng, Y., Zhong, B., Liang, Q., Mo, Z., Zhang, S., & Li, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i7.28591 · Cited by 248
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Zhu, Y., Li, C., Luo, B., Tang, J., & Wang, X. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350928 · Cited by 219
RelationTrack: Relation-Aware Multiple Object Tracking With Decoupled Representation
Yu, E., Li, Z., Han, S., & Wang, H. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3150169 · Cited by 219
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Ma, C., Huang, J., Yang, X., & Yang, M. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2865311 · Cited by 212
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Wang, G., Luo, C., Sun, X., Xiong, Z., & Zeng, W. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00632 · Cited by 206
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Zhang, J., Dong, B., Zhang, H., Ding, J., Heide, F., Yin, B., & Yang, X. S. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00860 · Cited by 212
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Zeng, A., Florence, P., Tompson, J., Welker, S., Chien, J., Attarian, M., Armstrong, T., Krasin, I., Duong, D., Wahid, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.14406 · Cited by 200
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Chen, H., Qin, Z., Ding, Y., Lan, T., & Qin, Z. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.111 · Cited by 200
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Liu, Q., Li, X., He, Z., Fan, N., Yuan, D., & Wang, H. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3008028 · Cited by 201
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Luo, W., Sun, P., Zhong, F., Liu, W., Zhang, T., & Wang, Y. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2899570 · Cited by 205
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Khan, S., Muhammad, K., Mumtaz, S., Baik, S. W., & de Albuquerque, V. H. C. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2896120 · Cited by 198
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Zhang, R., Tang, S., Zhang, Y., Li, J., & Yan, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.224 · Cited by 195
Huang, S., & Lei, K. (2020) · Ad Hoc Networks · doi:10.1016/j.adhoc.2020.102177 · Cited by 198
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Adversarial example detection for DNN models: a review and experimental comparison
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Zhang, P., Wang, D., Lu, H., & Yang, X. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01495-3 · Cited by 195
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Visual object tracking based on residual network and cascaded correlation filters
Zhang, J., Sun, J., Wang, J., & Yue, G. X.-G. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02572-0 · Cited by 188
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Li, C., Lu, A., Zheng, A. H., Tu, Z., & Tang, J. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00279 · Cited by 187
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Yao, D., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Wang, M., Yang, E., Jiaerken, Y., Luo, N., Yap, P., Liu, M., & Shen, D. G. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3051604 · Cited by 194
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Liu, X., Zhou, Y., Zhao, J., Yao, R., Liu, B., & Zheng, Y. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2894399 · Cited by 181
Lu, X., Lin, P., Cheng, S., Lin, Y., Chen, Z., Wu, L., & Zheng, Q. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.06.062 · Cited by 177
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Pu, S., Song, Y., Ma, C., Zhang, H., & Yang, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.03851 · Cited by 173
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Kato, H., Beker, D., Morariu, M. A., Ando, T., Matsuoka, T., Kehl, W., & Gaidon, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12057 · Cited by 238
SiamMOT: Siamese Multi-Object Tracking
Shuai, B., Berneshawi, A., Li, X., Modolo, D., & Tighe, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01219 · Cited by 178
Efficient Visual Tracking with Exemplar Transformers
Blatter, P., Kanakis, M., Danelljan, M., & Gool, L. V. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00162 · Cited by 183
Re$^3$: Re al-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
Gordon, D., Farhadi, A., & Fox, D. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2792152 · Cited by 169
Locate then Segment: A Strong Pipeline for Referring Image Segmentation
Jing, Y., Kong, T., Wang, W., Wang, L., Li, L., & Tan, T. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00973 · Cited by 173
Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning
Dong, X., Shen, J., Wang, W., Liu, Y., Shao, L., & Porikli, F. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00061 · Cited by 171
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Metadata
- Authors
- Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., & Torr, P. H. S.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Bertinetto, Luca
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 850-865
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2470394683
- Cited by
- 4,546
- References
- 49
- Citations by year
- 2026: 201 · 2025: 434 · 2024: 567 · 2023: 591 · 2022: 663 · 2021: 641 · 2020: 563 · 2019: 507 · 2018: 273 · 2017: 102 · 2016: 3
- FWCI
- 100.19
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Oxford University Research Archive (ORA) (University of Oxford) (acceptedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/L024683/1, EP/N019474/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object tracking, Siamese network, fully convolutional network, end-to-end learning, online learning, appearance modeling, video object detection, real-time tracking
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