Enlca
This project is official implementation of 'Efficient Non-Local Contrastive Attention for Image Super-Resolution', AAAI2022
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Cross Scale Non Local Attention | 238 | 3 years ago | 9 | Python | ||||||
| PyTorch code for our paper "Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining" (CVPR2020). | ||||||||||
| Han | 118 | 2 years ago | 5 | apache-2.0 | Python | |||||
| PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network" | ||||||||||
| Enlca | 76 | 9 months ago | 4 | Python | ||||||
| This project is official implementation of 'Efficient Non-Local Contrastive Attention for Image Super-Resolution', AAAI2022 | ||||||||||
| Vsr_tga | 69 | 3 years ago | 8 | Python | ||||||
| code for paper "Video Super-resolution with Temporal Group Attention" | ||||||||||
| Eccv2020_cucanet | 68 | a year ago | 1 | Python | ||||||
| Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution, ECCV, 2020. (PyTorch) | ||||||||||
| Rcan Tensorflow | 36 | 5 years ago | 2 | mit | Python | |||||
| Image Super-Resolution Using Very Deep Residual Channel Attention Networks Implementation in Tensorflow | ||||||||||
| San_pytorch | 19 | 2 years ago | mit | Python | ||||||
| Second-order Attention Network for Single Image Super-resolution (CVPR-2019) | ||||||||||
| Rcan Tf | 13 | 5 years ago | Python | |||||||
| TensorFlow code for ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks" | ||||||||||
| Styled Attention Face Super Resolution | 6 | 4 years ago | 2 | Python | ||||||
| Face super resolution | ||||||||||
| Aran | 5 | 5 years ago | mit | Python | ||||||
| The implementation of our paper "Image Super-Resolution using Aggregated Residual Transformation Networks with Spatial Attention ". | ||||||||||
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