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…
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...
受MobileNet中深度可分卷积的启发重新设计了Self-Attention模块,并提出了深度可分离Self-Attention,它由Depthwise Self-Attention和Pointwise Self-Attention组成,分别对应于MobileNet中的Depthwise和PointWise卷积。Depthwise Self-Attention用于捕获每个Window内的局部特征,Pointwise Self-Attention用于构建Window间的连接,提高表达能力。
Firstly, the polarized self-attention (PSA) mechanism is introduced to preserve... W Li,C Ning,Y Fang,... - 《Journal of Marine Science & Engineering》 被引量: 0发表: 2024年 A Ship Detection Method in Infrared Remote Sensing Images Based on Image Generation and Causal Inference Aiming at...
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 ...
Self-attention with relative position representations. arXiv:1803.02155 [cs.CL], 2018. Srivastava et al. (2014) Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. JMLR, 2014. Su...
This article argues that an enhanced understanding of the dynamics of language change can be gained by uniting two perspectives whose intimate relationship has not previously been subject to linguists' attention: language change as a his... G Sankoff - 《Language》 被引量: 0发表: 2019年 Anomalo...
如实例分割我们需要在像素级处理、计算,这样 self-attention 的计算复杂度就非常高了。为了克服这个问题,我们提出了通用Transformer backbone: (Swin Transformer),该算法构造层次化特征映射,计算复杂度与图像大小呈线性关系。如下图所示:Swin-Transformer构造了一个层次表示,从小尺寸的像素块(用灰色表示)开始,逐渐合并更...
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; ...
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。