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

CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language DescriptionsPreprint

Feng, Q., Ablavsky, V., & Sclaroff, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.04741 · Cited by 44

Global Memory and Local Continuity for Video Object Detection

Han, L., & Yin, Z. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3164253 · Cited by 36

TFTN: A Transformer-Based Fusion Tracking Framework of Hyperspectral and RGB

Zhao, K., Liu, H., Su, N., & Yan, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3215816 · Cited by 39

PromptVT: Prompting for Efficient and Accurate Visual Tracking

Zhang, M., Zhang, Q., Song, W., Huang, D., & He, Q. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3376582 · Cited by 41

Hierarchical Attention Siamese Network for Thermal Infrared Target Tracking

Yuan, D., Liao, D., Huang, F., Qiu, Z., Shu, X., Tian, C., & Liu, Q. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3462973 · Cited by 37

SiamTFA: Siamese Triple-Stream Feature Aggregation Network for Efficient RGBT Tracking

Zhang, J., Qin, Y., Fan, S., Xiao, Z., & Zhang, J. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3512551 · Cited by 36

OCSCNet-Tracker: Hyperspectral Video Tracker Based on Octave Convolution and Spatial–Spectral Capsule Network

Zhao, D., Wang, M., Huang, K., Zhong, W., Arun, P. V., Li, Y., Asano, Y., Wu, L. G., & Zhou, H. (2025) · Remote Sensing · doi:10.3390/rs17040693 · Cited by 36

Event-Guided Structured Output Tracking of Fast-Moving Objects Using a CeleX Sensor

Huang, J., Wang, S., Guo, M., & Chen, S. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2841516 · Cited by 36

Deformable Object Tracking With Gated Fusion

Liu, W., Song, Y., Chen, D., He, S., Yu, Y., Yan, T., Hancke, G. P., & Lau, R. W. H. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2902784 · Cited by 37

EMO

Wu, H., Feng, J., Tian, X., Sun, E., Liu, Y., Dong, B., Xu, F., & Zhong, S. (2020) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3386901.3388917 · Cited by 38

Fast Learning of Spatially Regularized and Content Aware Correlation Filter for Visual Tracking

Han, R., Feng, W., & Wang, S. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2998978 · Cited by 36

Accurate bounding-box regression with distance-IoU loss for visual tracking

Yuan, D., Shu, X., Fan, N., Chang, X., Liu, Q., & He, Z. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103428 · Cited by 38

TRAT: Tracking by attention using spatio-temporal features

Sarıbaş, H., Çevıkalp, H., Köpüklü, O., & Uzun, B. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.043 · Cited by 36

Bilateral Weighted Regression Ranking Model With Spatial-Temporal Correlation Filter for Visual Tracking

Zhu, H., Peng, H., Xu, G., Deng, L., Cheng, Y., & Song, A. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3075876 · Cited by 37

Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation

Liu, J., Ju, C., Xie, W., & Zhang, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548317 · Cited by 36

Contact Shape and Pose Recognition: Utilizing a Multipole Magnetic Tactile Sensor With a Metalearning Model

Xia, Z., Fang, B., Sun, F., Liu, H., Xu, W., Fu, L., & Yang, Y. (2022) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2022.3198372 · Cited by 36

Tracking Holistic Object RepresentationsPreprint

Sauer, A., Aljalbout, E., & Haddadin, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12920 · Cited by 37

Deep Learning for 3D Point Cloud Understanding: A SurveyPreprint

Lu, H., & Shi, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.08920 · Cited by 40

Classifying students based on cognitive state in flipped learning pedagogy

Shaw, R., & Patra, B. K. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.08.018 · Cited by 37

Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion

Zhang, G., Luo, Z., Huang, J., Lu, S., & Xing, E. P. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02005-x · Cited by 36

LiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking

Wei, Q., Zeng, B., Liu, J., He, L., & Zeng, G. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610022 · Cited by 40

Rapid learning with phase-change memory-based in-memory computing through learning-to-learn

Ortner, T., Petschenig, H., Vasilopoulos, A., Renner, R., Brglez, Š., Limbacher, T., Piñero-Fuentes, E., Linares-Barranco, A., Pantazi, A., & Legenstein, R. A. (2025) · Nature Communications · doi:10.1038/s41467-025-56345-4 · Cited by 35

Parallel Tracking and Verifying

Fan, H., & Ling, H. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2904789 · Cited by 35

Robust visual tracking via scale-and-state-awareness

Qi, Y., Qin, L., Zhang, S., Huang, Q., & Yao, H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.10.035 · Cited by 35

Robust visual tracking with channel attention and focal loss

Li, D., Wen, G., Kuai, Y., Zhu, L., & Porikli, F. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.041 · Cited by 35

Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning

Jung, I., You, K., Noh, H., Cho, M., & Han, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6779 · Cited by 35

Visual Tracking by Means of Deep Reinforcement Learning and an Expert Demonstrator

Dunnhofer, M., Martinel, N., Foresti, G. L., & Micheloni, C. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00282 · Cited by 35

Siamese Regression Tracking With Reinforced Template Updating

Zhao, F., Zhang, T., Song, Y., Tang, M., Wang, X., & Wang, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3036723 · Cited by 35

IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound

Chanti, D. A., Duque, V. G., Crouzier, M., Nordez, A., Lacourpaille, L., & Mateus, D. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3058303 · Cited by 35

Towards Universal Physical Attacks on Single Object Tracking

Ding, L., Wang, Y., Yuan, K., Jiang, M., Wang, P., Huang, H., & Wang, Z. J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16211 · Cited by 35

Transparent Object Tracking Benchmark

Fan, H., Miththanthaya, H. A., Harshit, H., Rajan, S. R., Liu, X., Zou, Z., Lin, Y., & Ling, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01056 · Cited by 35

Learning background-aware and spatial-temporal regularized correlation filters for visual tracking

Zhang, J., He, Y., Feng, W., Wang, J., & Xiong, N. N. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03868-8 · Cited by 35

Asymmetric Global–Local Mutual Integration Network for RGBT Tracking

Mei, J., Liu, Y., Wang, C., Zhou, D., Nie, R., & Cao, J. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3193971 · Cited by 35

DeconNet: End-to-End Decontaminated Network for Vision-Based Aerial Tracking

Zuo, H., Fu, C., Li, S., Ye, J., & Zheng, G. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3230043 · Cited by 35

Real‐time surgical instrument tracking in robot‐assisted surgery using multi‐domain convolutional neural network

Qiu, L., Li, C., & Ren, H. (2019) · Healthcare Technology Letters · doi:10.1049/htl.2019.0068 · Cited by 35

Need for Speed: A Benchmark for Higher Frame Rate Object TrackingPreprint

Galoogahi, H. K., Fagg, A., Huang, C., Ramanan, D., & Lucey, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05884 · Cited by 35

Balanced and Hierarchical Relation Learning for One-shot Object Detection

Yang, H., Cai, S., Sheng, H., Deng, B., Huang, J., Hua, X., Tang, Y., & Zhang, Y. (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/cvpr52688.2022.00744 · Cited by 36

Data Driven Feature Selection for Machine Learning Algorithms in Computer Vision

Zhang, F., Li, W., Zhang, Y., & Feng, Z. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2845412 · Cited by 35

When Correlation Filters Meet Siamese Networks for Real-Time Complementary Tracking

Li, D., Porikli, F., Wen, G., & Kuai, Y. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2892759 · Cited by 34

Learning to Update for Object Tracking With Recurrent Meta-Learner

Li, B., Xie, W., Zeng, W., & Liu, W. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2900577 · Cited by 34

Detection based visual tracking with convolutional neural network

Wang, Y., Luo, X., Ding, L., Fu, S., & Wei, X. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.03.012 · Cited by 34

iVR

Xu, J., Chen, H.-J., Qian, K., Dong, E., Sun, M., Wu, C., Zhang, L., & Yang, Z. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3351272 · Cited by 34

Real-Time Multiple Object Visual Tracking for Embedded GPU Systems

Fernández-Sanjurjo, M., Mucientes, M., & Brea, V. M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3056239 · Cited by 34

Visual Tracking via Hierarchical Deep Reinforcement Learning

Zhang, D., Zheng, Z., Jia, R., & Li, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16443 · Cited by 34

Weakly Supervised Person Search with Region Siamese Networks

Han, C., Su, K., Yu, D., Yuan, Z., Gao, C., Sang, N., Yang, Y., & Wang, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01179 · Cited by 34

Automatic multi-plaque tracking and segmentation in ultrasonic videos

Li, L., Hu, Z., Huang, Y., Zhu, W., Wang, Y., Chen, M., & Yu, J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102201 · Cited by 34

Region of Interest Constrained Negative Obstacle Detection and Tracking With a Stereo Camera

Sun, T., Pan, W., Wang, Y., & Liu, Y. K. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3142024 · Cited by 34

Long-term visual tracking algorithm for UAVs based on kernel correlation filtering and SURF features

Fan, J., Yang, X., Lu, R., Li, W., & Huang, Y. (2022) · The Visual Computer · doi:10.1007/s00371-021-02331-y · Cited by 34

Effective Local and Global Search for Fast Long-Term Tracking

Zhao, H., Yan, B., Wang, D., Qian, X., Yang, X., & Lu, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3153645 · Cited by 34

DSCN-net: a deep Siamese capsule neural network model for automatic diagnosis of malaria parasites detection

Madhu, G., Govardhan, A., Ravi, V., Kautish, S., Srinivas, B., Choudhury, T., & Kumar, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13008-6 · Cited by 34

A Joint Siamese Attention-Aware Network for Vehicle Object Tracking in Satellite Videos

Song, W., Jiao, L., Liu, F., Liu, X., Li, L., Yang, S., Hou, B., & Zhang, W. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3184755 · Cited by 37

CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking

Gao, L., Liu, P., Jiang, Y., Xie, W., Lei, J., Li, Y., & Du, Q. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3253173 · Cited by 34

Fast Visual Object Tracking with Rotated Bounding BoxesPreprint

Chen, B. X., & Tsotsos, J. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03892 · Cited by 34

Learning transform-aware attentive network for object tracking

Lu, X., Ni, B., Ma, C., & Yang, X. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.021 · Cited by 33

Fusion-Embedding Siamese Network for Light Field Salient Object Detection

Chen, G., Fu, H., Zhou, T., Xiao, G., Fu, K., Xia, Y., & Zhang, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3274933 · Cited by 35

TATrack: Target-aware transformer for object tracking

Huang, K., Chu, J., Leng, L., & Dong, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107304 · Cited by 33

Novel intelligent grazing strategy based on remote sensing, herd perception and UAVs monitoring

Chen, T., Zheng, H., Chen, J., Zhang, Z., & Huang, X. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108807 · Cited by 33

Object Detection Based on CNN and Vision‐Transformer: A Survey

Cao, J., Peng, B., Gao, M., Hao, H., Li, X., & Mou, H. (2025) · IET Computer Vision · doi:10.1049/cvi2.70028 · Cited by 37

A Memory Model Based on the Siamese Network for Long-Term Tracking

Lee, H., Choi, S., & Kim, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_5 · Cited by 33

Siamese Tracking Network With Informative Enhanced Loss

Tian, S., Liu, X., Liu, M., Li, S., & Yin, B. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2978636 · Cited by 33

Online Multi-object Visual Tracking using a GM-PHD Filter with Deep Appearance Learning

Baisa, N. L. (2019) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion43075.2019.9011441 · Cited by 34

Target-Independent Domain Adaptation for WBC Classification Using Generative Latent Search

Pandey, P., Prathosh, A. P., Kyatham, V., Mishra, D., & Dastidar, T. R. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3009029 · Cited by 36

Video Object Detection via Object-Level Temporal Aggregation

Yao, C.-H., Chen, F., Shen, X., Wan, Y., & Yang, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_10 · Cited by 33

Unsupervised Learning of Optical Flow with Deep Feature Similarity

Im, W., Kim, T., & Yoon, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58586-0_11 · Cited by 33

Design framework for metasurface optics-based convolutional neural networks

Burgos, C. M. V., Yang, T., Zhu, Y., & Vamivakas, A. N. (2021) · Applied Optics · doi:10.1364/ao.421844 · Cited by 33

Learning to Associate Every Segment for Video Panoptic Segmentation

Woo, S., Kim, D., Lee, J., & Kweon, I. S. (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/cvpr46437.2021.00273 · Cited by 35

SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking

Luo, Y., Xu, M., Yuan, C., Cao, X. G., Zhang, L., Xu, Y., Wang, T., & Feng, Q. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86383-8_15 · Cited by 33

Structural target-aware model for thermal infrared tracking

Yuan, D., Shu, X., Liu, Q., & He, Z. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.03.055 · Cited by 33

A Siamese network-based tracking framework for hyperspectral video

Tang, Y., Huang, H. B., Liu, Y., & Li, Y. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07712-5 · Cited by 34

Siamese-Based Twin Attention Network for Visual Tracking

Bao, H., Shu, P., Zhang, H., & Liu, X. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3207202 · Cited by 33

FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction

Wu, J., Ye, X., Mou, C., & Dai, W. (2023) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs58141.2023.10131726 · Cited by 35

OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking

Nie, J., He, Z., Yang, Y., Bao, Z., Gao, M., & Zhang, J. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/143 · Cited by 33

Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object TrackingPreprint

Wang, X., Li, C., Yang, R., Zhang, T., Tang, J., & Luo, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10014 · Cited by 33

Efficient Bird Eye View Proposals for 3D Siamese TrackingPreprint

Zarzar, J., Giancola, S., & Ghanem, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.10168 · Cited by 35

IGZO/WO 3−x ‐Heterostructured Artificial Optoelectronic Synaptic Devices Mimicking Image Segmentation and Motion Capture

Wu, T., Gao, S., & Li, Y. (2024) · Small · doi:10.1002/smll.202309857 · Cited by 32

Progress in artificial intelligence-based visual servoing of autonomous unmanned aerial vehicles (UAVs)

Radi, M. A., AlMallahi, M. N., Al‐Sumaiti, A. S., Semeraro, C., Abdelkareem, M. A., & Olabi, A. G. (2024) · International Journal of Thermofluids · doi:10.1016/j.ijft.2024.100590 · Cited by 32

A review of object tracking methods: From general field to autonomous vehicles

Cao, J., Zhang, H., Jin, L., Lv, J., Hou, G., & Zhang, C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127635 · Cited by 36

Asymmetric Convolution: An Efficient and Generalized Method to Fuse Feature Maps in Multiple Vision Tasks

Han, W., Dong, X., Zhang, Y., Crandall, D., Xu, C., & Shen, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3400873 · Cited by 33

TrackingMamba: Visual State Space Model for Object Tracking

Wang, Q., Zhou, L., Jin, P., Qu, X., Zhong, H., Song, H., & Shen, T. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3458938 · Cited by 32

Hyperspectral Video Tracking With Spectral–Spatial Fusion and Memory Enhancement

Chen, Y., Yuan, Q., Hong, X., Tang, Y., Xiao, Y., He, J., Guan, R., Liu, X., & Zhang, L. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3569479 · Cited by 34

Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey

Zhou, W., Yang, L., Zhao, L., Zhang, R., Cui, Y., Huang, H., Qie, K., & Wang, C. (2025) · ACM Computing Surveys · doi:10.1145/3760525 · Cited by 36

A Deep Similarity Metric Method Based on Incomplete Data for Traffic Anomaly Detection in IoT

Xu, K., Song, B., & Sun, F. (2019) · Applied Sciences · doi:10.3390/app9010135 · Cited by 32

Research on Inception Module Incorporated Siamese Convolutional Neural Networks to Realize Face Recognition

Xu, X., Zhang, L., Chen-dong, D., & Lu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2963211 · Cited by 33

PSENet: Psoriasis Severity Evaluation Network

Li, Y., Wu, Z., Zhao, S., Wu, X., Kuang, Y., Yan, Y., Ge, S., Wang, K., Fan, W., Chen, X., et al. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5424 · Cited by 32

Learning dynamic regression with automatic distractor repression for real-time UAV tracking

Fu, C., Ding, F., Li, Y., Jin, J., & Feng, C. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.104116 · Cited by 32

Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks

Maag, K., Rottmann, M., & Gottschalk, H. (2020) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai50040.2020.00084 · Cited by 32

Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

Huang, W., Zhou, X., Dong, M., & Xu, H. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10427-1 · Cited by 32

Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking

Sun, J., Zhang, L., Zha, Y., González-García, A., Zhang, P., Huang, W., & Zhang, Y. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475387 · Cited by 32

Deep Reinforcement Learning-Based DQN Agent Algorithm for Visual Object Tracking in a Virtual Environmental Simulation

Park, J., Farkhodov, K., Lee, S., & Kwon, K. (2022) · Applied Sciences · doi:10.3390/app12073220 · Cited by 34

Empirical curvelet based fully convolutional network for supervised texture image segmentation

Huang, Y., Zhou, F., & Gilles, J. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.04.021 · Cited by 32

Towards Accurate Pixel-wise Object Tracking by Attention Retrieval

Zhang, Z., Li, B., Hu, W., & Peng, H. (2020) · arXiv · doi:10.48550/arxiv.2008.02745 · Cited by 32

Correlation-Embedded Transformer Tracking: A Single-Branch Framework

Xie, F., Yang, W., Wang, C., Chu, L., Cao, Y., Ma, C., & Zeng, W. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3448254 · Cited by 35

Two-stage modality-graphs regularized manifold ranking for RGB-T tracking

Li, C., Zhu, C., Zheng, S., Luo, B., & Tang, J. P. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.08.004 · Cited by 31

Using FTOC to track shuttlecock for the badminton robot

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