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 norm 层用在每个MSA和每个MLP前面,a residual connection is applied after each module. 3.2 Shifted Window based Self-Attention Self-attention in non-ov...
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…
代码:https://github.com/pzhren/DW-ViT 动机:将多尺度和分支注意力引入window-based attention。现有窗口注意力仅使用单窗口设定,这可能会限制窗口配置对模型性能影响的上限。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 mult...
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(...
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 ...
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According to the types of windows that can be supported, these solutions can be divided into three categories: count-based sliding window, time-based sliding window, and count & time based sliding window. The count-based sliding window only counts the most recent N items,1 while the time-...
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…
首先是self-attention,它的计算公式如下: Q=xW_Q,K=xW_K,V=xW_V\\ x_{out}=softmax(\frac{QK^T}{\sqrt{h}})\cdot V\cdot W_O+x\\对于Q,K,V ,需要保存它们共同的输入 x ,输入 x 的形状为 [b,s,h] ,显存占用为 2*bsh=2bsh。 对于QK^T 矩阵乘法,需要保存 Q,K ,两个张量的形状均...