Fully-Convolutional Siamese Networks for Object Tracking

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

Deep Face Recognition

Parkhi, 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

CNN 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

Robust Structural Sparse Tracking

Zhang, T., Xu, C., & Yang, M. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2797082 · Cited by 100

Cooling-Shrinking Attack: Blinding the Tracker With Imperceptible Noises

Yan, B., Wang, D., Lu, H., & Yang, X. (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/cvpr42600.2020.00107 · Cited by 100

One-Shot Adversarial Attacks on Visual Tracking With Dual Attention

Chen, X., Yan, X., Zheng, F., Jiang, Y., Xia, S., Zhao, Y. J., & Ji, R. (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/cvpr42600.2020.01019 · Cited by 104

PG-Net: Pixel to Global Matching Network for Visual Tracking

Liao, B., Wang, C., Wang, Y., Wang, Y., & Yin, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58542-6_26 · Cited by 100

Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing

Kim, Y., & Panda, P. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.09.022 · Cited by 104

Object Tracking by Reconstruction With View-Specific Discriminative Correlation Filters

Kart, U., Lukežič, A., Kristan, M., Kämäräinen, J., & Matas, J. (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.2019.00143 · Cited by 99

Learning reinforced attentional representation for end-to-end visual tracking

Gao, P., Zhang, Q., Wang, F., Xiao, L., Fujita, H., & Zhang, Y. (2019) · Information Sciences · doi:10.1016/j.ins.2019.12.084 · Cited by 101

Temporal Adaptive RGBT Tracking with Modality Prompt

Wang, H., Liu, X., Li, Y., Sun, M., Yuan, D., & Liu, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i6.28352 · Cited by 105

Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation

Fu, C., Li, B., Ding, F., Lin, F., & Lu, G. (2021) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3072992 · Cited by 98

DepthTrack: Unveiling the Power of RGBD Tracking

Yan, S., Yang, J., Käpylä, J., Zheng, F., Leonardis, A., & Kämäräinen, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01055 · Cited by 98

Unsupervised Pre-Training for Detection Transformers

Dai, Z., Cai, B., Lin, Y., & Chen, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3216514 · Cited by 103

Recurrent Filter Learning for Visual Tracking

Yang, T., & Chan, A. B. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.235 · Cited by 97

DSiamMFT: An RGB-T fusion tracking method via dynamic Siamese networks using multi-layer feature fusion

Zhang, X., Ye, P., Peng, S., Liu, J., & Xiao, G. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2019.115756 · Cited by 97

CLNet: A Compact Latent Network for Fast Adjusting Siamese Trackers

Dong, X., Shen, J., Shao, L., & Porikli, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58565-5_23 · Cited by 97

A comprehensive experiment-based review of low-light image enhancement methods and benchmarking low-light image quality assessment

Rasheed, M. T., Shi, D., & Khan, H. (2022) · Signal Processing · doi:10.1016/j.sigpro.2022.108821 · Cited by 97

Learning Feature Embeddings for Discriminant Model Based Tracking

Zheng, L., Tang, M., Chen, Y., Wang, J., & Lu, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_45 · Cited by 99

Target-Aware Tracking with Long-Term Context Attention

He, K., Zhang, C., Xie, S., Li, Z., & Wang, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25155 · Cited by 96

Toward Unified Token Learning for Vision-Language Tracking

Zheng, Y., Zhong, B., Liang, Q., Li, G., Ji, R., & Li, X. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3301933 · Cited by 101

Trajectory Predictor by Using Recurrent Neural Networks in Visual Tracking

Wang, L., Zhang, L., & Zhang, Y. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2705345 · Cited by 95

Robust Estimation of Similarity Transformation for Visual Object Tracking

Li, Y., Zhu, J., Hoi, S. C. H., Song, W., Wang, Z., & Liu, H. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018666 · Cited by 95

Scale Equivariance Improves Siamese Tracking

Sosnovik, I., Moskalev, A., & Smeulders, A. W. M. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00281 · Cited by 96

PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds

Shan, J., Zhou, S., Fang, Z., & Cui, Y. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636821 · Cited by 98

Visual tracking in complex scenes: A location fusion mechanism based on the combination of multiple visual cognition flows

Liu, S., Huang, S., Wang, S., Muhammad, K., Bellavista, P., & Ser, J. D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.02.005 · Cited by 95

Efficient Visual Tracking via Hierarchical Cross-Attention Transformer

Chen, X., Kang, B., Wang, D., Li, D., & Lu, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25085-9_26 · Cited by 101

Siamese object tracking for unmanned aerial vehicle: a review and comprehensive analysis

Fu, C., Lu, K., Zheng, G., Ye, J., Cao, Z., Li, B., & Lu, G. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10558-5 · Cited by 99

Multi-Object Tracking with Multiple Cues and Switcher-Aware ClassificationPreprint

Feng, W., Hu, Z., Wu, W., Yan, J., & Ouyang, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.06129 · Cited by 95

Once for All: A Two-Flow Convolutional Neural Network for Visual Tracking

Chen, K., & Tao, W. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2757061 · Cited by 94

FEAR: Fast, Efficient, Accurate and Robust Visual Tracker

Borsuk, V., Vei, R., Kupyn, O., Martyniuk, T., Krashenyi, I., & Matas, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_37 · Cited by 102

3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud

Fang, Z., Zhou, S., Cui, Y., & Scherer, S. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3033034 · Cited by 93

Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning

Ahmed, I., Din, S., Jeon, G., Piccialli, F., & Fortino, G. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003453 · Cited by 92

Discriminative deep multi-task learning for facial expression recognition

Zheng, H., Wang, R., Ji, W., Zong, M., Wong, W. K., Lai, Z., & Lv, H. (2020) · Information Sciences · doi:10.1016/j.ins.2020.04.041 · Cited by 91

SPARK: Spatial-Aware Online Incremental Attack Against Visual Tracking

Guo, Q., Xie, X., Juefei-Xu, F., Ma, L., Li, Z., Xue, W., Feng, W., & Liu, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58595-2_13 · Cited by 92

HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking

Shao, J., Du, B., Wu, C., Gong, M., & Liu, T. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3045634 · Cited by 90

Computer vision and long short-term memory: Learning to predict unsafe behaviour in construction

Kong, T., Fang, W., Love, P. E. D., Luo, H., Xu, S., & Li, H. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101400 · Cited by 90

SatSOT: A Benchmark Dataset for Satellite Video Single Object Tracking

Zhao, M., Li, S., Xuan, S., Kou, L., Gong, S., & Zhou, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3140809 · Cited by 94

Robust Visual Tracking by Segmentation

Paul, M., Danelljan, M., Mayer, C., & Gool, L. V. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_33 · Cited by 90

Learning Spatial-Frequency Transformer for Visual Object Tracking

Tang, C., Wang, X., Bai, Y., Wu, Z., Zhang, J., & Huang, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3249468 · Cited by 93

Deep 6-DOF Tracking

Garon, M., & Lalonde, J. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2734599 · Cited by 89

Tracker Meets Night: A Transformer Enhancer for UAV Tracking

Ye, J., Fu, C., Cao, Z., An, S., Zheng, G., & Li, B. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3146911 · Cited by 89

MixFormer: End-to-End Tracking With Iterative Mixed Attention

Cui, Y., Jiang, C., Wu, G., & Wang, L. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3349519 · Cited by 94

SiamFPN: A Deep Learning Method for Accurate and Real-Time Maritime Ship Tracking

Shan, Y., Zhou, X., Liu, S., Zhang, Y., & Huang, K. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2978194 · Cited by 89

Recent advances of single-object tracking methods: A brief survey

Zhang, Y., Wang, T., Liu, K., Zhang, B., & Chen, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.011 · Cited by 90

Moving Target Tracking by Unmanned Aerial Vehicle: A Survey and Taxonomy

Sun, N., Zhao, J., Shi, Q., Liu, C., & Liu, P. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3363084 · Cited by 88

Long-Term Visual Object Tracking Benchmark

Moudgil, A., & Gandhi, V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20890-5_40 · Cited by 87

Object Tracking in Vary Lighting Conditions for Fog Based Intelligent Surveillance of Public Spaces

Liu, G., Liu, S., Muhammad, K., Sangaiah, A. K., & Doctor, F. (2018) · IEEE Access · doi:10.1109/access.2018.2834916 · Cited by 88

Distribution-Oriented Aesthetics Assessment With Semantic-Aware Hybrid Network

Cui, C., Liu, H., Lian, T., Nie, L., Zhu, L., & Yin, Y. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2875357 · Cited by 87

Grounding-Tracking-Integration

Yang, Z., Kumar, T., Chen, T., Su, J., & Luo, J. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3038720 · Cited by 88

Multilevel Graph Matching Networks for Deep Graph Similarity Learning

Ling, X., Wu, L., Wang, S., Ma, T., Xu, F., Liu, A. X., Wu, C., & Ji, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3102234 · Cited by 91

Rapid antibiotic susceptibility testing and species identification for mixed samples

Kandavalli, V. K., Karempudi, P., Larsson, J., & Elf, J. (2022) · Nature Communications · doi:10.1038/s41467-022-33659-1 · Cited by 89

Representation Learning for Visual Object Tracking by Masked Appearance Transfer

Zhao, H., Wang, D., & Lu, H. (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/cvpr52729.2023.01793 · Cited by 92

Transformers in Single Object Tracking: An Experimental Survey

Kugarajeevan, J., Thanikasalam, K., Ramanan, A., & Fernando, S. (2023) · IEEE Access · doi:10.1109/access.2023.3298440 · Cited by 89

Real-Time Index Authentication for Event-Oriented Surveillance Video Query using Blockchain

Nikouei, S. Y., Xu, R., Nagothu, D., Chen, Y., Aved, A. J., & Blasch, E. (2018) · IEEE International Smart Cities Conference (ISC2) · doi:10.1109/isc2.2018.8656668 · Cited by 85

LLR: Learning learning rates by LSTM for training neural networks

Yu, C., Qi, X., Ma, H., He, X., Wang, C., & Zhao, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.106 · Cited by 85

Multi-Drone-Based Single Object Tracking With Agent Sharing Network

Zhu, P., Zheng, J., Du, D., Wen, L., Sun, Y., & Hu, Q. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3045747 · Cited by 87

MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking

Wang, X., Shu, X., Zhang, S. S., Jiang, B., Wang, Y., Tian, Y., & Wu, F. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3174341 · Cited by 87

Learning Tracking Representations via Dual-Branch Fully Transformer Networks

Xie, F., Wang, C., Wang, G., Yang, W., & Zeng, W. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00303 · Cited by 87

Deep Continuous Conditional Random Fields With Asymmetric Inter-Object Constraints for Online Multi-Object Tracking

Zhou, H., Ouyang, W., Cheng, J., Wang, X., & Li, H. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2825679 · Cited by 84

The Spectral-Spatial Joint Learning for Change Detection in Multispectral Imagery

Zhang, W., & Lu, X. (2019) · Remote Sensing · doi:10.3390/rs11030240 · Cited by 84

M5L: Multi-Modal Multi-Margin Metric Learning for RGBT Tracking

Tu, Z., Lin, C., Zhao, W., Li, C., & Tang, J. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3125504 · Cited by 85

Real-time Visual Object Tracking with Natural Language Description

Feng, Q., Ablavsky, V., Bai, Q., Li, G., & Sclaroff, S. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093425 · Cited by 83

Learning Multi-Object Tracking and Segmentation From Automatic Annotations

Porzi, L., Hofinger, M., Ruiz, I., Serrat, J., Bulo, S. R., & Kontschieder, P. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00688 · Cited by 82

Deep Relative Tracking

Gao, J., Zhang, T., Yang, X., & Xu, C. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2656628 · Cited by 82

Real-time visual tracking by deep reinforced decision making

Choi, J., Kwon, J., & Lee, K. M. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.05.009 · Cited by 90

UCT: Learning Unified Convolutional Networks for Real-Time Visual Tracking

Zhu, Z. D., Huang, G., Zou, W., Du, D., & Huang, C. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.231 · Cited by 82

Two-stream convolutional neural network for non-destructive subsurface defect detection via similarity comparison of lock-in thermography signals

Cao, Y., Dong, Y., Cao, Y., Yang, J., & Yang, M. Y. (2020) · NDT & E International · doi:10.1016/j.ndteint.2020.102246 · Cited by 82

Pose-Guided Tracking-by-Detection: Robust Multi-Person Pose Tracking

Bao, Q., Liu, W., Cheng, Y., Zhou, B., & Mei, T. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2980194 · Cited by 82

Exploring the Effects of Blur and Deblurring to Visual Object Tracking

Guo, Q., Feng, W., Gao, R., Liu, Y., & Wang, S. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3045630 · Cited by 82

Siamese Visual Object Tracking: A Survey

Ondrašovič, M., & Tarábek, P. (2021) · IEEE Access · doi:10.1109/access.2021.3101988 · Cited by 85

Indoor Localization Based on CSI Fingerprint by Siamese Convolution Neural Network

Li, Q., Liao, X., Liu, M., & Valaee, S. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3107936 · Cited by 82

Online Multiple Object Tracking with Cross-Task Synergy

Guo, S., Wang, J., Wang, X., & Tao, D. (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/cvpr46437.2021.00804 · Cited by 81

CiteTracker: Correlating Image and Text for Visual Tracking

Li, X., Huang, Y., He, Z., Wang, Y., Lu, H., & Yang, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00915 · Cited by 88

RATM: Recurrent Attentive Tracking Model

Kahou, S. E., Michalski, V., Memisevic, R., Pal, C., & Vincent, P. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.206 · Cited by 85

Real-time visual detection and tracking system for traffic monitoring

Fernández-Sanjurjo, M., Bosquet, B., Mucientes, M., & Brea, V. M. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.07.005 · Cited by 80

Physical Adversarial Textures That Fool Visual Object Tracking

Wiyatno, R. R., & Xu, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00492 · Cited by 83

Deep Object Tracking with Shrinkage Loss

Lu, X., Ma, C., Shen, J., Yang, X., Reid, I. D., & Yang, M. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3041332 · Cited by 80

Unsupervised Learning of Accurate Siamese Tracking

Shen, Q., Qiao, L., Guo, J., Li, P., Li, X., Li, B., Feng, W., Gan, W., Wu, W., & Ouyang, 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/cvpr52688.2022.00793 · Cited by 80

Dynamic Kernel Distillation for Efficient Pose Estimation in Videos

Nie, X., Li, Y., Luo, L., Zhang, N., & Feng, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00704 · Cited by 79

Multi-Task Driven Feature Models for Thermal Infrared Tracking

Liu, Q., Li, X., He, Z., Fan, N., Yuan, D., Liu, W., & Liang, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6828 · Cited by 79

DUT: Learning Video Stabilization by Simply Watching Unstable Videos

Xu, Y., Zhang, J., Maybank, S. J., & Tao, D. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3182887 · Cited by 79

Fast Visual Tracking With Siamese Oriented Region Proposal Network

Zhu, H. B., Xue, M., Wang, Y., Yuan, G., & Li, X. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3178656 · Cited by 79

Computer Vision – ECCV 2016 Workshops

Lee, B., Erdenee, E., Jin, S., & Rhee, P. K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3 · Cited by 79

VisDrone-SOT2019: The Vision Meets Drone Single Object Tracking Challenge Results

Du, D., Zhu, P., Wen, L., Bian, X., Ling, H., Hu, Q., Zheng, J., Peng, T., Wang, X., Zhang, Y., et al. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00029 · Cited by 78

Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images

Dunnhofer, M., Antico, M., Sasazawa, F., Takeda, Y., Camps, S., Martinel, N., Micheloni, C., Carneiro, G., & Fontanarosa, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101631 · Cited by 80

Siamese Earthquake Transformer: A Pair‐Input Deep‐Learning Model for Earthquake Detection and Phase Picking on a Seismic Array

Xiao, Z., Wang, J., Liu, C., Li, J., Zhao, L., & Yao, Z. (2021) · Journal of Geophysical Research Solid Earth · doi:10.1029/2020jb021444 · Cited by 81

SiamBAG: Band Attention Grouping-Based Siamese Object Tracking Network for Hyperspectral Videos

Li, W., Hou, Z., Zhou, J. W., & Tao, R. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3285802 · Cited by 78

Event Stream-Based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline

Wang, X., Wang, S., Tang, C., Zhu, L., Jiang, B., Tian, Y., & Tang, J. (2024) · 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/cvpr52733.2024.01821 · Cited by 91

Nnmamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

Gong, H., Kang, L., Wang, Y., Wang, Y., Wan, X., Wu, X., & Li, H. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980694 · Cited by 110

CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark

Lukežič, A., Kart, U., Käpylä, J., Durmush, A., Kämäräinen, J., Matas, J., & Kristan, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01011 · Cited by 77

End-to-End Learning Deep CRF Models for Multi-Object Tracking Deep CRF Models

Xiang, J., Xu, G., Ma, C., & Hou, J. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2975842 · Cited by 78

Robust visual tracking with correlation filters and metric learning

Yuan, D., Kang, W. M., & He, Z. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105697 · Cited by 77

Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting

Wang, Q., Han, T., Gao, J., & Yuan, Y. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3051371 · Cited by 77

CSART: Channel and spatial attention-guided residual learning for real-time object tracking

Zhang, D., Zheng, Z., Li, M., & Liu, R. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.11.046 · Cited by 77

RPT: Learning Point Set Representation for Siamese Visual Tracking

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