Fetching the paper…
Reading the bibliography…
Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik · 2001
Earlier work this paper cites.
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.
On single image scale-up using sparse-representations
Roman Zeyde, Michael Elad, and Matan Protter · 2010
Earlier work this paper cites.
Low-complexity single-image super-resolution based on nonnegative neighbor embedding
Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie Line Alberi-Morel · 2012
Earlier work this paper cites.
Learning a deep convolutional network for image super-resolution
Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang · 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.
Single image super-resolution from transformed self-exemplars
Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja · 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.
Gaussian error linear units (gelus), 2016
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Accurate image super-resolution using very deep convolutional networks
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee · 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.
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
Earlier work this paper cites.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 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.
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.
Sketch-based manga retrieval using manga109 dataset
Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa · 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.
Non-local recurrent network for image restoration
Ding Liu, Bihan Wen, Yuchen Fan, Chen Change Loy, and Thomas S Huang · 2018
Earlier work this paper cites.
Esrgan: Enhanced super-resolution generative adversarial networks
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy · 2018
Earlier work this paper cites.
Image super-resolution using very deep residual channel attention networks
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu · 2018
Earlier work this paper cites.
Residual dense network for image super-resolution
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2018
Earlier work this paper cites.
Second-order attention network for single image super-resolution
Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, and Lei Zhang · 2019
Earlier work this paper cites.
Studying stand-alone self-attention in vision models
Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, and Jon Shlens · 2019
Cited alongside, same era.
Ranksrgan: Generative adversarial networks with ranker for image super-resolution
Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao · 2019
Cited alongside, same era.
Residual non-local attention networks for image restoration, 2019
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.
An image is worth 16x16 words: Transformers for image recognition at scale, 2020
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
Later among the works it cites.
Scaling local self-attention for parameter efficient visual backbones
Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, and Jonathon Shlens · 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.
Real-esrgan: Training real-world blind super-resolution with pure synthetic data
Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan · 2021
Later among the works it cites.
Cvt: Introducing convolutions to vision transformers
Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, and Matti Pietikäinen · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Visual transformers: Token-based image representation and processing for computer vision, 2020
Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Zhicheng Yan, Masayoshi Tomizuka, Joseph Gonzalez, Kurt Keutzer, and Peter Vajda · 2020
Cited alongside, same era.
Cross-scale internal graph neural network for image super-resolution
Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, and Chen Change Loy · 2020
Cited alongside, same era.
Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, and Furu Wei · 2021
Cited alongside, same era.
Swin-unet: Unet-like pure transformer for medical image segmentation, 2021
Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang · 2021
Cited alongside, same era.
Video super-resolution transformer, 2021
Jiezhang Cao, Yawei Li, Kai Zhang, and Luc Van Gool · 2021
Cited alongside, same era.
Early convolutions help transformers see better
Tete Xiao, Piotr Dollar, Mannat Singh, Eric Mintun, Trevor Darrell, and Ross Girshick · 2021
Later among the works it cites.
Finding discriminative filters for specific degradations in blind super-resolution
Liangbin Xie, Xintao Wang, Chao Dong, Zhongang Qi, and Ying Shan · 2021
Later among the works it cites.
Incorporating convolution designs into visual transformers
Kun Yuan, Shaopeng Guo, Ziwei Liu, Aojun Zhou, Fengwei Yu, and Wei Wu · 2021
Later among the works it cites.
Hrformer: High-resolution vision transformer for dense predict
Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, and Jingdong Wang · 2021
Later among the works it cites.
A battle of network structures: An empirical study of cnn, transformer, and mlp, 2021
Yucheng Zhao, Guangting Wang, Chuanxin Tang, Chong Luo, Wenjun Zeng, and Zheng-Jun Zha · 2021
Later among the works it cites.
Cswin transformer: A general vision transformer backbone with cross-shaped windows
Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, and Baining Guo · 2022
Closest in time.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
Closest in time.
Glance and focus networks for dynamic visual recognition, 2022
Gao Huang, Yulin Wang, Kangchen Lv, Haojun Jiang, Wenhui Huang, Pengfei Qi, and Shiji Song · 2022
Closest in time.
Reflash dropout in image super-resolution
Xiangtao Kong, Xina Liu, Jinjin Gu, Yu Qiao, and Chao Dong · 2022
Closest in time.
Uniformer: Unifying convolution and self-attention for visual recognition, 2022
Kunchang Li, Yali Wang, Junhao Zhang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, and Yu Qiao · 2022
Closest in time.
Blueprint separable residual network for efficient image super-resolution
Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Jinjin Gu, Yu Qiao, and Chao Dong · 2022
Closest in time.
Vrt: A video restoration transformer, 2022
Jingyun Liang, Jiezhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, and Luc Van Gool · 2022
Closest in time.
Revisiting rcan: Improved training for image super-resolution, 2022
Zudi Lin, Prateek Garg, Atmadeep Banerjee, Salma Abdel Magid, Deqing Sun, Yulun Zhang, Luc Van Gool, Donglai Wei, and Hanspeter Pfister · 2022
Closest in time.
Evaluating the generalization ability of super-resolution networks
Yihao Liu, Hengyuan Zhao, Jinjin Gu, Yu Qiao, and Chao Dong · 2022
Closest in time.
Aggregating global features into local vision transformer, 2022
Krushi Patel, Andres M Bur, Fengjun Li, and Guanghui Wang · 2022
Closest in time.
Maxim: Multi-axis mlp for image processing
Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li · 2022
Closest in time.
Uformer: A general u-shaped transformer for image restoration
Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li · 2022
Closest in time.
Pale transformer: A general vision transformer backbone with pale-shaped attention
Sitong Wu, Tianyi Wu, Haoru Tan, and Guodong Guo · 2022
Closest in time.
Restormer: Efficient transformer for high-resolution image restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang · 2022
Closest in time.