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In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking.
Distractor-aware siamese networks for visual object tracking
Z. Zhu, Q. Wang, B. Li, W. Wu, J. Yan, and W. Hu · 1907
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
Earlier work this paper cites.
Statistical learning theory, 1998
V. Vapnik · 1998
Earlier work this paper cites.
Model compression
C. Bucilu, R. Caruana, and A. Niculescu-Mizil · 2006
Earlier work this paper cites.
Visual object tracking using adaptive correlation filters
Bolme, David S and Beveridge, J Ross and Draper, Bruce A and Lui, Yui Man · 2010
Earlier work this paper cites.
Exploiting the circulant structure of tracking-by-detection with kernels
J. F. Henriques, C. Rui, M. Pedro and B. Jorge · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Multi-channel correlation filters
K. Galoogahi, Hamed, S. Terence and L. Simon · 2013
Earlier work this paper cites.
Correlation filters for object alignment
N. Boddeti, V., Kanade, T., and Vijaya Kumar, B. V. K · 2013
Earlier work this paper cites.
Learning a deep compact image representation for visual tracking
N. Wang and D. Yeung · 2013
Earlier work this paper cites.
Do deep nets really need to be deep?
J. Ba and R. Caruana · 2014
Earlier work this paper cites.
Accurate scale estimation for robust visual tracking
M. Danelljan, G. Häger, F. Khan, and M. Felsberg · 2014
Earlier work this paper cites.
Adaptive color attributes for real-time visual tracking
M. Danelljan, G. Bhat, F. S. Khan, M. Felsberg, et al · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2014
Earlier work this paper cites.
A scale adaptive kernel correlation filter tracker with feature integration
Y. Li and J. Zhu · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
S. Karen and Z. Andrew · 2014
Earlier work this paper cites.
Meem: Robust tracking via multiple experts using entropy minimization
J. Zhang, S. Ma, and S. Sclaroff · 2014
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio and J. P. David · 2015
Earlier work this paper cites.
Learning spatially regularized correlation filters for visual tracking
M. Danelljan, G. Hager, F. Shahbaz Khan, and M. Felsberg · 2015
Earlier work this paper cites.
Deep learning with limited numerical precision
S. Gupta, A. Agrawal, K. Gopalakrishnan and P. Narayanan · 2015
Earlier work this paper cites.
S. Han, H. Mao and D. William J
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding · 2015
Earlier work this paper cites.
High-speed tracking with kernelized correlation filters
J. F. Henriques, C. Rui, M. Pedro and B. Jorge · 2015
Earlier work this paper cites.
Long-term correlation tracking
C. Ma, X. Yang, C. Zhang, M. Yang · 2015
Earlier work this paper cites.
In defense of color-based model-free tracking
H. Possegger, T. Mauthner, and H. Bischof · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Earlier work this paper cites.
Deep learning, dark knowledge, and dark matter
P. Sadowski, J. Collado, D. Whiteson, and P. Baldi · 2015
Earlier work this paper cites.
Data-free parameter pruning for deep neural networks
S. Srinivas and R V. Babu · 2015
Cited alongside, same era.
Transferring rich feature hierarchies for robust visual tracking
N. Wang, S. Li, A. Gupta, and D.-Y. Yeung · 2015
Cited alongside, same era.
Understanding and diagnosing visual tracking systems
N. Wang, J. Shi, D.-Y. Yeung, and J. Jia · 2015
Cited alongside, same era.
Object tracking benchmark
W. Yi, L. Jongwoo, and M.-H. Yang · 2015
Cited alongside, same era.
Fully-convolutional siamese networks for object tracking
L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. Torr · 2016
Cited alongside, same era.
Learning feed-forward one-shot learners
High performance visual tracking with siamese region proposal network
B. Li, J. Yan, W. Wu, Z. Zhu, and X. Hu · 2018
Later among the works it cites.
Trackingnet: A large-scale dataset and benchmark for object tracking in the wild
M. Muller, A. Bibi, S. Giancola, S. Alsubaihi, and B. Ghanem · 2018
Later among the works it cites.
Deep mutual learning
Y. Zhang, T. Xiang, T. M. Hospedales, and H. Lu · 2018
Later among the works it cites.
Deep meta learning for real-time target-aware visual tracking
J. Choi, J. Kwon and K. Lee · 2019
Closest in time.
Visual Tracking via Adaptive Spatially-Regularized Correlation Filters
Dai, Kenan and Wang, Dong and Lu, Huchuan and Sun, Chong and Li, Jianhua · 2019
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Lasot: A high-quality benchmark for large-scale single object tracking
H. Fan, L. Lin, F. Yang, P. Chu, G. Deng, S. Yu, H. Bai, Y. Xu, C. Liao, and H. Ling · 2019
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L. Bertinetto, J. F. Henriques, J. Valmadre, P. H. Torr and A. Vedaldi · 2016
Cited alongside, same era.
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv and Y. Bengio · 2016
Cited alongside, same era.
Beyond correlation filters: Learning continuous convolution operators for visual tracking
M. Danelljan, A. Robinson, G. Bhat, F. S. Khan, M. Felsberg, et al · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Unifying distillation and privileged information
D. Lopez-Paz, L. Bottou, B. Schölkopf, and V. Vapnik · 2016
Cited alongside, same era.
Learning multi-domain convolutional neural networks for visual tracking
H. Nam and B. Han · 2016
Cited alongside, same era.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon and A. Farhadi · 2016
Cited alongside, same era.
Closest in time.
Siamese cascaded region proposal networks for real-time visual tracking
H. Fan, H. Ling · 2019
Closest in time.
Graph convolutional tracking
J. Gao, T. Zhang and C. Xu · 2019
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Bridging the gap between detection and tracking: A unified approach
L. Huang, X. Zhao and K. Huang · 2019
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Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters
K. Ugur, L. Alan, K. Matej, K. Joni-Kristian and M. Jiri · 2019
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The seventh visual object tracking vot2019 challenge results
M. Kristan, A. Leonardis, J. Matas, M. Felsberg, R. Pfugfelder, L. C. Zajc, T. Vojir, G. Bhat, A. Lukezic, A. Eldesokey, G. Fernandez, and et al · 2019
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Siamrpn++: Evolution of siamese visual tracking with very deep networks
B. Li, W. Wu, Q. Wang, F. Zhang, J. Xing, and J. Yan · 2019
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Target-aware deep tracking
X. Li, C. Ma, B. Wu, Z. He and M. Yang · 2019
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Structured Knowledge Distillation for Semantic Segmentation
Y. Liu, K. Chen, C. Liu, Z. Qin, Z. Luo and J. Wang · 2019
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Hedging deep features for visual tracking
Y. Qi, S. Zhang, L. Qin, Q. Huang, H. Yao, J. Lim and M. Yang · 2019
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ROI Pooled Correlation Filters for Visual Tracking
Y. Sun, C. Sun, D. Wang, Y. He and H. Lu · 2019
Closest in time.
X. Dong, J. Shen, D. Wu, K. Guo, X. Jin, F. Porikli,
2019
Closest in time.
Deeper and wider siamese networks for real-time visual tracking
Z. Zhang and H. Peng · 2019
Closest in time.
Visual object tracking by hierarchical attention Siamese network,
J. Shen, X. Tang, X. Dong, and L. Shao, · 2020
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Siamese local and global networks for robust face tracking
Y. Qi, S. Zhang, J. Feng, H. Zhou, D. Tao and X. Li · 2020
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Local Semantic Siamese Networks for Fast Tracking,
Z. Liang, and J. Shen, · 2020
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Real-Time Correlation Tracking Via Joint Model Compression and Transfer
N. Wang, W. Zhou, Y. Song, C. Ma and H. Li · 2020
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Deep Object Tracking with Shrinkage Loss,
X. Lu, C. Ma, J. Shen, X. Yang, I. Reid, and M.-H. Yang, · 2020
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Release the Power of Online-Training for Robust Visual Tracking
Y. Yang, G. Li, Y. Qi and Q. Huang · 2020
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ROAM: Recurrently Optimizing Tracking Model
T. Yang, P. Xu, R. Chai and A. B. Chan · 2020
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Ocean: Object-aware anchor-free tracking
Z. Zhang and H. Peng · 2020
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Learning To Fuse Asymmetric Feature Maps in Siamese Trackers,
W. Han, X. Dong, F. S. Khan, L. Shao, J. Shen, · 2021
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Dynamical Hyperparameter Optimization via Deep Reinforcement Learning in Tracking,
X. Dong, J. Shen, W. Wang, L. Shao, H. Ling, and F. Porikli, · 2021
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