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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
Cited by
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Items 401–500 of 1,007
FuCoLoT – A Fully-Correlational Long-Term Tracker
Lukežič, A., Zajc, L. Č., Vojíř, T., Matas, J., & Kristan, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20890-5_38 · Cited by 75
Frame-Event Alignment and Fusion Network for High Frame Rate Tracking
Zhang, J., Wang, Y., Liu, W., Li, M., Bai, J., Yin, B., & Yang, X. S. (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/cvpr52729.2023.00943 · Cited by 78
UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
Yan, X.-C., Fu, T., Lin, H., Xuan, F., Huang, Y., Cao, Y., Hu, H. R., & Liu, P. (2023) · Applied Sciences · doi:10.3390/app132011320 · Cited by 75
Learning Background-Aware Correlation Filters for Visual TrackingPreprint
Galoogahi, H. K., Fagg, A., & Lucey, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.04590 · Cited by 75
Dual Model Learning Combined With Multiple Feature Selection for Accurate Visual Tracking
Zhang, J., Jin, X., Sun, J., Wang, J., & Li, K. (2019) · IEEE Access · doi:10.1109/access.2019.2908668 · Cited by 74
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
Zeng, X., Liao, R., Gu, L., Xiong, Y., Fidler, S., & Urtasun, R. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00403 · Cited by 74
3D Siamese Transformer Network for Single Object Tracking on Point Clouds
Hui, L., Wang, L., Tang, L., Lan, K., Xie, J., & Yang, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20086-1_17 · Cited by 77
Hierarchical Tracking by Reinforcement Learning-Based Searching and Coarse-to-Fine Verifying
Zhong, B., Bai, B., Li, J., Zhang, Y., & Fu, Y. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2885238 · Cited by 73
Learning Target-aware Representation for Visual Tracking via Informative Interactions
Guo, M., Zhang, Z., Fan, H., Jing, L., Lyu, Y., Li, B., & Hu, W. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/130 · Cited by 73
Reliable Re-Detection for Long-Term Tracking
Wang, N., Zhou, W., & Li, H. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2816570 · Cited by 73
Efficient Adversarial Attacks for Visual Object Tracking
Liang, S., Wei, X., Yao, S., & Cao, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58574-7_3 · Cited by 77
Adaptive Channel Selection for Robust Visual Object Tracking with Discriminative Correlation Filters
Xu, T., Feng, Z., Wu, X., & Kittler, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01435-1 · Cited by 72
Supervised Contrastive Learning for RFF Identification With Limited Samples
Peng, Y., Hou, C., Zhang, Y., Lin, Y., Gui, G., Gacanin, H., Mao, S., & Adachi, F. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3272628 · Cited by 72
Oyelade, O. N., Irunokhai, E. A., & Wang, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-51329-8 · Cited by 72
Visual object tracking with adaptive structural convolutional network
Yuan, D., Li, X., He, Z., Liu, Q., & Lu, S. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105554 · Cited by 71
Procontext: Exploring Progressive Context Transformer for Tracking
Lan, J.-P., Cheng, Z.-Q., He, J.-Y., Li, C., Luo, B., Bao, X., Xiang, W., Geng, Y., & Xie, X. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094971 · Cited by 72
Zhao, D., Zhang, H., Huang, K., Zhu, X., Arun, P. V., Jiang, W., Li, S., Pei, X., & Zhou, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126721 · Cited by 71
Chen, Y., Tang, Y., Xiao, Y., Yuan, Q., Zhang, Y., Liu, F., He, J., & Zhang, L. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.03.013 · Cited by 73
Efficient Visual Tracking Based on Fuzzy Inference for Intelligent Transportation Systems
Liu, S., Huang, S., Xu, X., Lloret, J., & Muhammad, K. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3232242 · Cited by 70
A Low-Shot Object Counting Network With Iterative Prototype Adaptation
Ðukić, N., Lukežič, A., Zavrtanik, V., & Kristan, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01730 · Cited by 69
A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods
Wang, B., Li, Q., Mao, Q., Wang, J., Chen, C. L. P., Shangguan, A., & Zhang, H. (2024) · Drones · doi:10.3390/drones8090518 · Cited by 69
Discriminative and Robust Online Learning for Siamese Visual Tracking
Zhou, J., Wang, P., & Sun, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7002 · Cited by 68
Fang, B., Chen, G., Pan, L., Kou, R., & Wang, L. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.2979693 · Cited by 68
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
Xu, J., & Wang, X. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00992 · Cited by 71
SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking
Yao, L., Fu, C., Li, S., Zheng, G., & Ye, J. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161487 · Cited by 68
CoMIR: Contrastive Multimodal Image Representation for RegistrationPreprint
Pielawski, N., Wetzer, E., Öfverstedt, J., Lu, J., Wählby, C., Lindblad, J., & Sladoje, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06325 · Cited by 68
Utkin, L. V., & Ryabinin, M. A. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.10.006 · Cited by 67
Hu, Z., Yang, D., Zhang, K., & Chen, Z. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2971657 · Cited by 67
Robust Visual Object Tracking Via Adaptive Attribute-Aware Discriminative Correlation Filters
Zhu, X., Wu, X., Xu, T., Feng, Z., & Kittler, J. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3050073 · Cited by 67
Object matching of visible–infrared image based on attention mechanism and feature fusion
Li, W., Chen, Q., Gu, G., & Sui, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110972 · Cited by 68
Similarity-Guided Layer-Adaptive Vision Transformer for UAV Tracking
Xue, C., Zhong, B., Liang, Q., Zheng, Y., Li, N., Xue, Y., & Song, S. (2025) · 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/cvpr52734.2025.00631 · Cited by 78
Multiview 2D/3D Rigid Registration via a Point-Of-Interest Network for Tracking and Triangulation
Liao, H., Lin, W.-A., Zhang, J., Zhang, J., Luo, J., & Zhou, S. K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01292 · Cited by 66
Video Instance Segmentation Tracking With a Modified VAE Architecture
Lin, C.-C., Hung, Y., Feris, R. S., & He, L. (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/cvpr42600.2020.01316 · Cited by 66
Multi-Encoder Towards Effective Anomaly Detection in Videos
Fang, Z., Zhou, J. T., Xiao, Y., Li, Y., & Yang, F. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3037538 · Cited by 70
Diaz, D. J., Gong, C., Ouyang-Zhang, J., Loy, J. M., Wells, J., Yang, D. K., Ellington, A. D., Dimakis, A. G., & Klivans, A. R. (2024) · Nature Communications · doi:10.1038/s41467-024-49780-2 · Cited by 69
Fang, B., Pan, L., & Kou, R. (2019) · Remote Sensing · doi:10.3390/rs11111292 · Cited by 66
Deep mutual learning for visual object tracking
Zhao, H., Yang, G., Wang, D., & Lu, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107796 · Cited by 65
One More Check: Making “Fake Background” Be Tracked Again
Liang, C., Zhang, Z., Xue, Z., Li, B., & Hu, W. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20045 · Cited by 65
Real-Time 3D Single Object Tracking With Transformer
Shan, J., Zhou, S., Cui, Y., & Fang, Z. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3146714 · Cited by 66
A multichannel optical computing architecture for advanced machine vision
Xu, Z., Yuan, X., Zhou, T., & Fang, L. (2022) · Light Science & Applications · doi:10.1038/s41377-022-00945-y · Cited by 66
RPformer: A Robust Parallel Transformer for Visual Tracking in Complex Scenes
Gu, F., Lu, J., & Cai, C. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3170972 · Cited by 65
Transformer Meets Remote Sensing Video Detection and Tracking: A Comprehensive Survey
Jiao, L., Zhang, X., Liu, X., Liu, F., Yang, S., Ma, W., Li, L., Chen, P., Feng, Z., Guo, Y., et al. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3289293 · Cited by 68
Domain Adaptation-Aware Transformer for Hyperspectral Object Tracking
Wu, Y., Jiao, L., Liu, X., Liu, F., Yang, S., & Li, L. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3385273 · Cited by 71
Automatic Lumbar MRI Detection and Identification Based on Deep Learning
Zhou, Y., Liu, Y., Chen, Q., Gu, G., & Sui, X. (2018) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-0130-7 · Cited by 65
Visual Tracking via Dynamic Memory Networks
Yang, T., & Chan, A. B. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2929034 · Cited by 65
Manifold Siamese Network: A Novel Visual Tracking ConvNet for Autonomous Vehicles
Gao, M., Jin, L., Jiang, Y., & Guo, B. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2930337 · Cited by 65
End-to-End Learning of Object Motion Estimation from Retinal Events for Event-Based Object Tracking
Chen, H., Suter, D., Wu, Q., & Wang, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6625 · Cited by 66
CNN-based single object detection and tracking in videos and its application to drone detection
Lee, D. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09924-0 · Cited by 65
A survey on online learning for visual tracking
Abbass, M. Y., Kwon, K.-C., Kim, N., Abdelwahab, S. A., El-Samie, F. E. A., & Khalaf, A. A. M. (2021) · The Visual Computer · doi:10.1007/s00371-020-01848-y · Cited by 64
MLVSNet: Multi-level Voting Siamese Network for 3D Visual Tracking
Wang, Z., Xie, Q., Lai, Y., Wu, J., Long, K., & Wang, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00309 · Cited by 64
Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks
Cui, Y., Guo, D., Shao, Y., Wang, Z., Shen, C., Zhang, L., & Chen, S. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01559-4 · Cited by 64
Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey
Altini, N., Prencipe, B., Cascarano, G. D., Brunetti, A., Brunetti, G., Triggiani, V., Carnimeo, L., Marino, F., Guerriero, A., Villani, L., et al. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.08.157 · Cited by 66
Cross-modal Target Retrieval for Tracking by Natural Language
Li, Y., Yu, J., Cai, Z., & Pan, Y. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00540 · Cited by 69
Enhanced robust spatial feature selection and correlation filter learning for UAV tracking
Wen, J., Chu, H., Lai, Z., Xu, T., & Shen, L. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.01.003 · Cited by 64
Transformer RGBT Tracking With Spatio-Temporal Multimodal Tokens
Sun, D., Pan, Y., Lu, A., Li, C., & Luo, B. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3425455 · Cited by 70
Siamese Networks: The Tale of Two Manifolds
Roy, S. K., Harandi, M. T., Nock, R., & Hartley, R. I. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00314 · Cited by 67
Fully convolutional online tracking
Cui, Y., Jiang, C., Wang, L., & Wu, G. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103547 · Cited by 65
Barman, A., Inam, M. E., Lee, S., Savitz, S. I., Sheth, S. A., & Giancardo, L. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759475 · Cited by 63
Remote Sensing Object Tracking With Deep Reinforcement Learning Under Occlusion
Cui, Y., Hou, B., Wu, Q., Ren, B., Wang, S., & Jiao, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3096809 · Cited by 63
Learning to Adversarially Blur Visual Object Tracking
Guo, Q., Cheng, Z., Juefei-Xu, F., Ma, L., Xie, X., Liu, Y., & Zhao, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01066 · Cited by 63
Learning to Track Objects from Unlabeled Videos
Zheng, J., Ma, C., Peng, H., & Yang, X. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01329 · Cited by 65
Bess, A., Berglind, F., Mukhopadhyay, S., Bryliński, M., Griggs, N. W., Cho, T., Galliano, C., & Wasan, K. M. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.10.022 · Cited by 64
Unifying Visual and Vision-Language Tracking via Contrastive Learning
Ma, Y., Tang, Y., Yang, W., Zhang, T., Zhang, J., & Kang, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28205 · Cited by 81
Robust Object Tracking Based on Temporal and Spatial Deep Networks
Zhu, T., Xing, J., Wang, Q., Lang, C., Feng, S., & Jin, Y. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.130 · Cited by 62
Video Object Segmentation by Learning Location-Sensitive Embeddings
Ci, H., Wang, C., & Wang, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_31 · Cited by 62
Extreme Low-Light Image Enhancement for Surveillance Cameras Using Attention U-Net
Ai, S., & Kwon, J.-W. (2020) · Sensors · doi:10.3390/s20020495 · Cited by 62
Spatially Attentive Visual Tracking Using Multi-Model Adaptive Response Fusion
Zhang, J., Wu, Y. D., Feng, W., & Wang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2924944 · Cited by 61
Learning target-focusing convolutional regression model for visual object tracking
Yuan, D., Fan, N., & He, Z. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105526 · Cited by 61
Farm Parcel Delineation Using Spatio-temporal Convolutional Networks
Aung, H. L., Uzkent, B., Burke, M., Lobell, D. B., & Ermon, S. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00046 · Cited by 66
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Li, Z., Ye, X., Xiong, F., Lu, J., Zhou, J. W., & Qian, Y. (2021) · Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing · doi:10.1109/whispers52202.2021.9484032 · Cited by 61
Siamese infrared and visible light fusion network for RGB-T tracking
Peng, J., Zhao, H., Hu, Z., Zhuang, Y., & Wang, B. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-01833-6 · Cited by 63
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Feng, M., & Su, J. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3310295 · Cited by 63
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Han, Y., Deng, C., Zhao, B., & Tao, D. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2905984 · Cited by 61
Towards a Better Match in Siamese Network Based Visual Object Tracker
He, A., Luo, C., Tian, X., & Zeng, W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_7 · Cited by 61
Learning reliable modal weight with transformer for robust RGBT tracking
Feng, M., & Su, J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108945 · Cited by 61
Deep learning to frame objects for visual target tracking
Pang, S., del Coz, J. J., Yu, Z., Luaces, O., & Díez, J. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.08.010 · Cited by 60
Face recognition based on convolution siamese networks
Wu, H., Xu, Z., Zhang, J., Yan, W. Q., & Ma, X. (2017) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2017.8302003 · Cited by 62
Detect or Track: Towards Cost-Effective Video Object Detection/Tracking
Luo, H., Xie, W., Wang, X., & Zeng, W. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018803 · Cited by 60
Siamese capsule networks with global and local features for text classification
Wu, Y., Li, J., Wu, J. X., & Chang, J. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.064 · Cited by 59
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Zhou, J., & Pun, C.-M. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3029913 · Cited by 59
Li, S., Liu, F., Jiao, L., Chen, P., & Li, L. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3185638 · Cited by 59
Zhu, E., Li, E., Wei, Z., & Yin, W.-Y. (2022) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2022.3184545 · Cited by 59
Leveraging Local and Global Cues for Visual Tracking via Parallel Interaction Network
Zheng, Y., Zhong, B., Liang, Q., Tang, Z., Ji, R., & Li, X. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3212987 · Cited by 62
Siamese Neural Networks for Wireless Positioning and Channel Charting
Lei, E., Castañeda, O., Tirkkonen, O., Goldstein, T., & Studer, C. (2019) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2019.8919897 · Cited by 62
Visual Tracking via Spatially Aligned Correlation Filters Network
Zhang, M., Wang, Q., Xing, J., Gao, J. G., Peng, P., Hu, W., & Maybank, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_29 · Cited by 58
EDP: An Efficient Decomposition and Pruning Scheme for Convolutional Neural Network Compression
Ruan, X., Liu, Y., Yuan, C., Li, B., Hu, W., Li, Y., & Maybank, S. J. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3018177 · Cited by 63
Deep Learning-Based Object Tracking in Satellite Videos: A comprehensive survey with a new dataset
Li, Y., Jiao, L., Huang, Z., Zhang, X., Zhang, R., Song, X., Tian, C., Zhang, Z., Liu, F., Yang, S., et al. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2022.3198643 · Cited by 62
Foreground-Background Distribution Modeling Transformer for Visual Object Tracking
Yang, D., He, J., Ma, Y., Yu, Q., & Zhang, T. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00928 · Cited by 60
Xu, T., Feng, Z., Wu, X., & Kittler, J. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2945068 · Cited by 58
Animal Detection in Man-made Environments
Singh, A. K., Pietrasik, M., Natha, G., Ghouaiel, N., Brizel, K., & Ray, N. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093504 · Cited by 58
Real-Time Correlation Tracking Via Joint Model Compression and Transfer
Wang, N., Zhou, W., Song, Y., Ma, C., & Li, H. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2989544 · Cited by 58
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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