Activating More Pixels in Image Super-Resolution Transformer Xiangyu Chen, Xintao Wang, Jiantao Zhou and Chao Dong BibTeX @article{chen2022activating, title={Activating More Pixels in Image Super-Resolution Transformer}, author={Chen, Xiangyu and Wang, Xintao and Zhou, Jiantao and Dong, Chao}, ...
CVPR2023 - Activating More Pixels in Image Super-Resolution Transformer Arxiv - HAT: Hybrid Attention Transformer for Image Restoration - XPixelGroup/HAT
[3] Li, W., Lu, X., Lu, J., Zhang, X., Jia, J.: On efficient transformer and image pre-training for low-level vision. arXiv preprint, arXiv:2112.10175 (2021)[4] Gu, J., Dong, C.: Interpreting super-resolution networks with local attribution maps. In: Proceedings of the IEE...
Title: Activating More Pixels in Image Super-Resolution Transformer Paper:https://arxiv.org/pdf/2205.04437.pdf Code:https://github.com/XPixelGroup/HAT 导读 本文提出了一种名为Hybrid Attention Transformer (HAT)的方法,旨在通过结合深度学习技术和注意力机制来改进图像超分辨率任务。 单图像超分辨率(SR)任...
[4] Gu, J., Dong, C.: Interpreting super-resolution networks with local attribution maps. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9199–9208 (2021) 代码语言:javascript
[4] Gu, J., Dong, C.: Interpreting super-resolution networks with local attribution maps. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9199–9208 (2021)
项目链接:https://github.com/chxy95/HAT 实验结果显示,本文提出的方法在图像超分辨率任务上大幅超越了当前最先进方法的性能(超过1dB),如图1所示。 图1. HAT与当前SOTA方法SwinIR和EDT的性能对比 HAL-L表示在HAT的基础上深度增加一倍的更大容量的模型。
[4] Gu, J., Dong, C.: Interpreting super-resolution networks with local attribution maps. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9199–9208 (2021) 编辑:王菁 校对:林亦霖
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:https://github.com/keycloak/keycloak/tree/master/examples/cordova 替代模式 cordova-native 采用不同的方法。它使用系统浏览器打开登录页面。用户通过身份验证后,浏览器使用特殊 url 重新重定向到应用。在那里,red hat single sign-on 适配器可以通过从 url 读取代码或令牌来完成登录。 您可以通过...