Swin Transformer是通过把Transformer block中标准的多头自注意力(multi-head self attention,MSA)模块替换为基于shifted windows的模块 而构建的,其他层保持不变。如fig3(b)所示,一个Swin Transformer block包含a shifted window based MSA module,followed by a 2-layer MLP with GELU nonlinearity in between. layer...
The Devil Is in the Details: Window-based Attention for Image Compression 一、全文概览 研究领域:深度图像压缩(Learnable Image Compressing) 简单总结:基于卷积神经网络的深度图像压缩(LIC)方法难以捕…
作者们由此引入多尺度窗口attention,并对不同尺度的窗口分支加权组合,提升多尺度表征能力。 核心内容 single-scale window multi-head attention (SSW-MSA) -> dynamic window module (DWM) = multi-scale window multi-head selfattention module+dynamic multi-scale window module。基于此构建的DW-ViT可以动态地改善...
Based on that, ViTDet sets the size of each window to 14×14 in the interpolated model. Thus, if we want attention to perform the same operation it did during pretraining, we simply need to ensure that each 14×14 window has the same position embedding—i.e., by tiling the position ...
Hey authors, great repo for boosting training of Attention-based models. I wonder how the code can be ported to support (shifted)WindowAttention? To my knowledge, the (S)WindowAttention differs from traditional Attention on: SWAttention has a relative position bias term inside softmax:Softmax(...
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self-attention layers目前被学者热衷与替换ResNet中的某个卷积,这里主要是基于局部窗口优化,它们确实是提高了性能。但是提高性能的同时,也增加了计算复杂度。我们使用shift windows替换原始的滑动窗口,它允许在一般硬件中更有效地实现。 2.3 Self-attention/Transformers 作为 CNNs 的补充 ...
25, where the Swin transformer Block is composed of two sub-modules: window multi-head self-attention (W-MSA) and shifted window multi-head self-attention (SW-MSA), which replace the multi-head attention mechanism in ViT. With the remarkable computational efficiency of Swin transformer, Liu ...
总结 众所周知,self-attention的时间复杂度是O(n^2),一种减轻self-attention时间复杂度的方法是利用sparse attention(稀疏注意力机制),sliding window attention(swa,滑动窗口注意力机制) 就是其中一种。 最近…
接下来主要讲讲Swin Transformer中最重要的模块:SW-MAA(Shifted Window Multi-head Attention)。 Patch是图像的小块,比如4 x 4的像素。每个Patch最后会变成1,或者Visual Token。它的维度是embed_dim。Visual Tokens(编码后的特征)会进入Tansformer中。Vit,是把所有的Visual Tokens全部拉直,送入Transformer中。