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:
Activating More Pixels in Image Super-Resolution Transformer Xiangyu Chen,Xintao Wang,Jiantao ZhouandChao 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}, journal...
classHAT(nn.Module):r"""混合注意力变换器 (Hybrid Attention Transformer) 该PyTorch实现基于 `Activating More Pixels in Image Super-Resolution Transformer`。 部分代码基于SwinIR。 参数: img_size (int | tuple(int)): 输入图像大小。默认值64 patch_size (int | tuple(int)): Patch大小。默认值1 in_...
[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
该PyTorch实现基于 `Activating More Pixels in Image Super-Resolution Transformer`。 部分代码基于SwinIR。 参数: img_size (int | tuple(int)): 输入图像大小。默认值64 patch_size (int | tuple(int)): Patch大小。默认值1 in_chans (int): 输入图像通道数。默认值3 ...
[4] Gu, J., Dong, C.: Interpreting super-resolution networks with local attributionmaps. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9199–9208 (2021) —分割线— 【元宇宙·新人类 第3期直播重磅来袭!】 ...
[4] Gu, J., Dong, C.: Interpreting super-resolution networks with local attributionmaps. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9199–9208 (2021)
arXiv preprint, arXiv:2112.10175 (2021) [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)...
Default: nn.LayerNorm """ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): super().__init__() self.input_resolution = input_resolution self.dim = dim self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) self.norm = norm_layer(4 * dim) def forward(...