本文主要有两点创新,一个是水平垂直window-attention,相较于swin在一个local-window上做self-attention,本文通过将输入特征等分为两份,一份做水平window-attention,一份做垂直window-attention,以在同一个module中获得全局注意力。另一个是局部增强位置编码,通过利用3*3深度卷积于V上,并将该结果直接添加到attention的...
CSwin的block有两个部分,一个是做LayerNorm和Cross-shaped window self-attention并接一个shortcut,另一个则是做LayerNorm和MLP,相比于Swin和Twins来说,block的计算量大大的降低了(swin,twins则是有两个attention+两个MLP堆叠一个block)。公式如下: \hat{X}^{l}=\text { CSWin-Attention }\left(\mathrm{LN...
CSWin 总体框架如上图所示,主要是一个四阶段的网络,只是 attention 部分替换为了 cross-shaped window 注意力。 **(1)cross-shaped window self-attention。 ** 在四个阶段中,stripe的宽度依次为[1,2,7,7],可以看出这样设计,一开始感受野较小,后面感受野较大,和之前一些网络的原理也类似,一开始提取纹理等细节...
To address this issue, we develop the Cross-Shaped Window self-attention mechanism for computing self-attention in the horizontal and vertical stripes in parallel that form a cross-shaped window, with each stripe obtained by splitting the input feature into stripe...
3.2、Cross-Shaped Window Self-Attention 尽管具有强大的长期上下文建模能力,但原始的全自注意机制的计算复杂度是特征地图大小的二次函数。 因此,对于以高分辨率特征地图为输入的视觉任务,如目标检测和分割,将会产生巨大的计算代价。 为了缓解这一问题,现有的研究建议在局部注意窗口中进行自我注意,并应用halo或shift窗口...
To address this issue, we develop the Cross-Shaped Window self-attention mechanism for computing self-attention in the horizontal and vertical stripes in parallel that form a cross-shaped window, with each stripe obtained by splitting the input feature into stripes of equal width. We provide a ...
CSWin Transformer(the nameCSWinstands forCross-ShapedWindow) is introduced inarxiv, which is a new general-purpose backbone for computer vision. It is a hierarchical Transformer and replaces the traditional full attention with our newly proposed cross-shaped window self-attention. The cross-shaped...
we develop the Cross-Shaped Window self-attention mechanism for computing self-attention in the horizontal and vertical stripes in parallel that form a cross-shaped window, with each stripe obtained by splitting the input feature into stripes of equal width. We provide...
1. self-attention被cross-shaped window self-attention替代。 2. 为了引入局部归纳偏置(local inductive bias),LePE(Locally-Enhanced Positional Encoding)作为与self-attention平行的模块被加入到了结构中。下文会具体介绍。 Cross-Shaped Window Self-Attention 在计算机视觉任务中(目标检测,分割等),原先的模型计算量...
we develop the Cross-Shaped Window self-attention mechanism for computing self-attention in the horizontal and vertical stripes in parallel that form a cross-shaped window, with each stripe obtained by splitting the input feature into stripes of equal width. We provide a detailed mathematical analysi...