Window里是4 x 4的Visual Tokens。Swin是在Window当中单独去做Window Attention。与Vit不同,本Window内的Visual Tokens去算自己内部的attention,这和Vit的Multi-head attention没有本质区别。但这里Windows之间是没有交互的。Window 1中的元素,看不到Window 4的信息。 Only W-MSA 注意:如果windows之间不交互信息,即w...
如果说Shifted Window是Swin Transformer的精华，那么Attention Mask则可以算作是Shifted Window的精华。Attention Mask主要干的事就是设置合理的mask，使得Shifted Window Attention在与Window Attention相同的窗口个数下，得到等价的计算结果。如下图所示，分别给SWA和WA加上index后，再计算window attention的时候，希望有相...
# num_windows*B, window_size*window_size, C attn_windows = self.attn(x_windows, mask=self.attn_mask) 1. 2. 其中x_windows 是 shifted_x 的窗口划分,self.attn 是WindowAttention的的实例。W-MSA/SW-MSA的实现区别主要为是否使用shifted。 SwinTransformerBlock主要就是W-MSA/SW-MSA的实现,其结构为...
这里的attn_mask会传给WindowAttention用于窗口内的多头注意力计算。实际就是在WindowAttention中的softmax之前将添加偏置的QKT/√d+BQKT/d+B再加一个mask信息。如最后依据所示,不等于0的那些点全部将mask值置为−100−100。这样实现了对移动拼接产生的window注意力输出产生一个偏置。
To my knowledge, the (S)WindowAttention differs from traditional Attention on: SWAttention has a relative position bias term inside softmax:Softmax(QK^T/sqrt(dim) + Bias)V^T; The mask pattern is different; The head dims are different; ...
Window-based patch self-attention can use the local connectivity of the image features, and the shifted window-based patch self-attention enables the communication of information between different patches in the entire image scope. Through in-depth research on the effects of different sizes of ...
2. Shifted Window based Self-Attention Swin Transformer是通过将Transformer Block中的标准多头自注意(MSA)模块替换为基于移位窗口的模块其他层保持不变。如图4所示。 图4 标准的Transformer体系结构及其对图像分类的适应都进行全局自注意力,其中一个token和所有其他token之间的关系被计算。全局计算导致了关于token数量的...
Window-based patch self-attention can use the local connectivity of the image features, and the shifted window-based patch self-attention enables the communication of information between different patches in the entire image scope. Through in-depth research on the effects of different sizes of ...
Lightweight Video Denoising using Aggregated Shifted Window Attention Lydia Lindner 1 Alexander Effland 2 Filip Ilic 1 lydia.lindner@icg.tugraz.at effland@iam.uni-bonn.de filip.ilic@icg.tugraz.at Thomas Pock 1 thomas.pock@tugraz.at Erich Kobler 2 erich.kobler@...
A shifted window mechanism is crucial in Swin Transformer, alternating between standard window attention and shifted window attention in consecutive layers. This not only maintains local attention within windows but also introduces cross-window connections, enhancing the model's ability to ...