Fetching the paper…
Reading the bibliography…
Transformer-based methods have achieved impressive image restoration performance due to their capacities to model long-range dependency compared to CNN-based methods.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
Earlier work this paper cites.
High-resolution stereo datasets with subpixel-accurate ground truth
Daniel Scharstein, Heiko Hirschmüller, York Kitajima, Greg Krathwohl, Nera Nešić, Xi Wang, and Porter Westling · 2014
Earlier work this paper cites.
Image super-resolution using deep convolutional networks
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 2015
Earlier work this paper cites.
Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 2015
Earlier work this paper cites.
Accelerating the super-resolution convolutional neural network
Chao Dong, Chen Change Loy, and Xiaoou Tang · 2016
Earlier work this paper cites.
Deeply-recursive convolutional network for image super-resolution
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 2016
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Earlier work this paper cites.
Deep laplacian pyramid networks for fast and accurate super-resolution
Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang · 2017
Earlier work this paper cites.
Enhanced deep residual networks for single image super-resolution
Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
Earlier work this paper cites.
Enhancenet: Single image super-resolution through automated texture synthesis
Mehdi SM Sajjadi, Bernhard Scholkopf, and Michael Hirsch · 2017
Earlier work this paper cites.
Image super-resolution via deep recursive residual network
Ying Tai, Jian Yang, and Xiaoming Liu · 2017
Earlier work this paper cites.
Ntire 2017 challenge on single image super-resolution: Methods and results
Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, and Lei Zhang · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Earlier work this paper cites.
Fast, accurate, and lightweight super-resolution with cascading residual network: 15th european conference, munich, germany, september 8-14, 2018, proceedings, part x
Namhyuk Ahn, Byungkon Kang, and Kyung Ah Sohn · 2018
Earlier work this paper cites.
Enhancing the spatial resolution of stereo images using a parallax prior
D. S. Jeon, S. H. Baek, I. Choi, and H. K. Min · 2018
Cited alongside, same era.
Fast and accurate image super-resolution with deep laplacian pyramid networks
Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang · 2018
Cited alongside, same era.
Image super-resolution using very deep residual channel attention networks
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu · 2018
Cited alongside, same era.
Residual dense network for image super-resolution
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu · 2018
Cited alongside, same era.
Second-order attention network for single image super-resolution
Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, and Lei Zhang · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Single image super-resolution via a holistic attention network
Ben Niu, Weilei Wen, Wenqi Ren, Xiangde Zhang, Lianping Yang, Shuzhen Wang, Kaihao Zhang, Xiaochun Cao, and Haifeng Shen · 2020
Later among the works it cites.
A stereo attention module for stereo image super-resolution
X. Ying, Y. Wang, L. Wang, W. Sheng, and Y. Guo · 2020
Later among the works it cites.
Cross-scale internal graph neural network for image super-resolution
Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, and Chen Change Loy · 2020
Later among the works it cites.
Pre-trained image processing transformer
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao · 2021
Later among the works it cites.
Feedback network for mutually boosted stereo image super-resolution and disparity estimation
Q. Dai, J. Li, Q. Yi, F. Fang, and G. Zhang · 2021
Later among the works it cites.
Anchor-based plain net for mobile image super-resolution
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dan Hendrycks and Thomas Dietterich · 2019
Cited alongside, same era.
Lightweight image super-resolution with information multi-distillation network
Z. Hui, X. Gao, Y. Yang, and X. Wang · 2019
Cited alongside, same era.
Feedback network for image super-resolution
Z. Li, J. Yang, Z. Liu, X. Yang, and W. Wu · 2019
Cited alongside, same era.
Learning parallax attention for stereo image super-resolution
L. Wang, Y. Wang, Z. Liang, Z. Lin, and Y. Guo · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Cited alongside, same era.
Residual non-local attention networks for image restoration
Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, and Yun Fu · 2019
Cited alongside, same era.
End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
Cited alongside, same era.
Zongcai Du, Jie Liu, Jie Tang, and Gangshan Wu · 2021
Later among the works it cites.
Interpreting super-resolution networks with local attribution maps
Jinjin Gu and Chao Dong · 2021
Later among the works it cites.
Sotr: Segmenting objects with transformers
Ruohao Guo, Dantong Niu, Liao Qu, and Zhenbo Li · 2021
Later among the works it cites.
On efficient transformer and image pre-training for low-level vision
Wenbo Li, Xin Lu, Jiangbo Lu, Xiangyu Zhang, and Jiaya Jia · 2021
Later among the works it cites.
Swinir: Image restoration using swin transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021
Later among the works it cites.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Later among the works it cites.
Image super-resolution with non-local sparse attention
Yiqun Mei, Yuchen Fan, and Yuqian Zhou · 2021
Later among the works it cites.
Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
Later among the works it cites.
Activating more pixels in image super-resolution transformer
Xiangyu Chen, Xintao Wang, Jiantao Zhou, and Chao Dong · 2022
Closest in time.
Nafssr: Stereo image super-resolution using nafnet
Xiaojie Chu, Liangyu Chen, and Wenqing Yu · 2022
Closest in time.
Resolution-robust large mask inpainting with fourier convolutions
Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, and Victor Lempitsky · 2022
Closest in time.