N-Gram in Swin Transformers for Efficient Lightweight Image
Super-Resolution

N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution

21 November 2022

Papers citing "N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution"

12 / 12 papers shown

Title
HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution Xiang Zhang Yulun Zhang Fisher Yu 37 15 0 08 Jul 2024
Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution Long Peng Yang Cao Renjing Pei Wenbo Li Jiaming Guo Xueyang Fu Yang Wang Zheng-Jun Zha 34 12 0 11 May 2024
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features Yuming Huang Yingpin Chen Changhui Wu Hanrong Xie Binhui Song Hui Wang SupR ViT 29 0 0 30 Dec 2023
Efficient Mixed Transformer for Single Image Super-Resolution Ling Zheng Jinchen Zhu Jinpeng Shi Shizhuang Weng 35 19 0 19 May 2023
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration Marcos V. Conde Ui-Jin Choi Maxime Burchi Radu Timofte ViT 49 135 0 22 Sep 2022
Revisiting RCAN: Improved Training for Image Super-Resolution Zudi Lin Prateek Garg Atmadeep Banerjee Salma Abdel Magid Deqing Sun Yulun Zhang Luc Van Gool D. Wei Hanspeter Pfister SupR 45 57 0 27 Jan 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 290 7,434 0 11 Nov 2021
ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders Yan Song Tong Zhang Yonggang Wang Kai-Fu Lee 46 43 0 04 May 2021
Lightweight Image Super-Resolution with Information Multi-distillation Network Zheng Hui Xinbo Gao Yunchu Yang Xiumei Wang SupR 66 865 0 26 Sep 2019
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi Jose Caballero Ferenc Huszár J. Totz Andrew P. Aitken Rob Bishop Daniel Rueckert Zehan Wang SupR 190 5,173 0 16 Sep 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 247 36,356 0 25 Aug 2016
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 245 7,633 0 03 Jul 2012