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Video super-resolution (VSR), with the aim to restore a high-resolution video from its corresponding low-resolution version, is a spatial-temporal sequence prediction problem.
Long short-term memory
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On bayesian adaptive video super resolution
C. Liu and D. Sun · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2016
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Real-time video super-resolution with spatio-temporal networks and motion compensation
J. Caballero, C. Ledig, A. Aitken, A. Acosta, J. Totz, Z. Wang, and W. Shi · 2017
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Deformable convolutional networks
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei · 2017
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Optical flow estimation using a spatial pyramid network
A. Ranjan and M. J. Black · 2017
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Detail-revealing deep video super-resolution
X. Tao, H. Gao, R. Liao, J. Wang, and J. Jia · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Adversarial learning with local coordinate coding
J. Cao, Y. Guo, Q. Wu, C. Shen, J. Huang, and M. Tan · 2018
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Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation
Y. Jo, S. W. Oh, J. Kang, and S. J. Kim · 2018
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Frame-recurrent video super-resolution
M. S. Sajjadi, R. Vemulapalli, and M. Brown · 2018
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Image super-resolution using very deep residual channel attention networks
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu · 2018
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Multi-marginal wasserstein gan
J. Cao, L. Mo, Y. Zhang, K. Jia, C. Shen, and M. Tan · 2019
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Recurrent back-projection network for video super-resolution
M. Haris, G. Shakhnarovich, and N. Ukita · 2019
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Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study
S. Nah, S. Baik, S. Hong, G. Moon, S. Son, R. Timofte, and K. Mu Lee · 2019
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Deformable non-local network for video super-resolution
H. Wang, D. Su, C. Liu, L. Jin, X. Sun, and X. Peng · 2019
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Video super-resolution with temporal group attention
T. Isobe, S. Li, X. Jia, S. Yuan, G. Slabaugh, C. Xu, Y.-L. Li, S. Wang, and Q. Tian · 2020
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Revisiting temporal modeling for video super-resolution
T. Isobe, F. Zhu, X. Jia, and S. Wang · 2020
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Tdan: Temporally-deformable alignment network for video super-resolution
Y. Tian, Y. Zhang, Y. Fu, and C. Xu · 2020
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End-to-end video instance segmentation with transformers
Y. Wang, Z. Xu, X. Wang, C. Shen, B. Cheng, H. Shen, and H. Xia · 2020
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Adversarial sparse transformer for time series forecasting
S. Wu, X. Xiao, Q. Ding, P. Zhao, Y. Wei, and J. Huang · 2020
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Learning texture transformer network for image super-resolution
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Edvr: Video restoration with enhanced deformable convolutional networks
X. Wang, K. C. Chan, K. Yu, C. Dong, and C. Change Loy · 2019
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Video enhancement with task-oriented flow
T. Xue, B. Chen, J. Wu, D. Wei, and W. T. Freeman · 2019
Cited alongside, same era.
Edvr: Video Restoration with Enhanced Deformable Convolutional Networks
Wang, Xintao and Chan, Kelvin CK and Yu, Ke and Dong, Chao and Change Loy, Chen · 2019
Cited alongside, same era.
Basicvsr: The search for essential components in video super-resolution and beyond
K. C. Chan, X. Wang, K. Yu, C. Dong, and C. C. Loy · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2020
Cited alongside, same era.
Closed-loop matters: Dual regression networks for single image super-resolution
Y. Guo, J. Chen, J. Wang, Q. Chen, J. Cao, Z. Deng, Y. Xu, and M. Tan · 2020
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F. Yang, H. Yang, J. Fu, H. Lu, and B. Guo · 2020
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Learning Parities with Neural Networks
A. Daniely and E. Malach · 2020
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BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
Chan, Kelvin CK and Wang, Xintao and Yu, Ke and Dong, Chao and Loy, Chen Change · 2020
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Pre-trained image processing transformer
H. Chen, Y. Wang, T. Guo, C. Xu, Y. Deng, Z. Liu, S. Ma, C. Xu, C. Xu, and W. Gao · 2021
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Localvit: Bringing locality to vision transformers
Y. Li, K. Zhang, J. Cao, R. Timofte, and L. Van Gool · 2021
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Computational separation between convolutional and fully-connected networks
E. Malach and S. Shalev-Shwartz · 2021
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Cola-net: Collaborative attention network for image restoration
C. Mou, J. Zhang, X. Fan, H. Liu, and R. Wang · 2021
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Computational Separation Between Convolutional and Fully-Connected Networks
eran malach and Shai Shalev-Shwartz · 2021
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