CSWin Transformer最核心的部分就是cross-shaped window self-attention,如下所示,首先将self-attention的mutil-heads均分成两组,一组做horizontal stripes self-attention,另外一组做vertical stripes self-attention。 所谓horizontal stripes self-attention就是沿着H维度将tokens分成水平条状windows,对于输入为HxW的tokens,...
In this paper, the parallel network structure of the local-window self-attention mechanism and the equivalent large convolution kernel is used to realize the spatial-channel modeling of the network so that the network has better local and global feature extraction performance. Experiments on the RSS...
Transformer落地Bayesian思想的时候权衡多种因素而实现最大程度的近似估计Approximation,例如使用了计算上相对CNN、RNN等具有更高CPU和内存使用性价比的Multi-head self-attention机制来完成更多视角信息集成的表达,在Decoder端训练时候一般也会使用多维度的Prior信息完成更快的训练速度及更高质量的模型训练,在正常的工程落地...
Thus, this paper proposes a novel Local Self-Attention in Transformer (LSAT) for a visual question answering model to address these issues. The LSAT model simultaneously models intra-window and inter-window attention by setting local windows for visual features. Therefore, the LSAT model can ...
1. Local Attention是什么? 2020年的ViT横空出世,席卷了模型设计领域,铺天盖地的各种基于Transformer的结构开始被提出,一些在卷积神经网络中取得成功的先验知识,如local operation、多尺度、shuffled等等各种操作和inductive bias被引入Transformer之...
self-attention主要结论: Methods PairwiseSelf-attention乘在beta(xj)上的weight只由xi,xj决定。可以通过加position encoding让网络知晓xi,xj的位置关系。 PatchSelf-attention乘在beta(xj)上的weight是由整个batch R(i) (batch里所有的j locations) 决定的。这和 ...
以下是 Local Attention 的实现代码: python import torch import torch.nn as nn import torch.nn.functional as F class LocalAttention(nn.Module): def __init__(self, hidden_size, window_size=5): super(LocalAttention, self).__init__() self.hidden_size = hidden_size self.window_size = wind...
*Embedded Gaussian操作与self-attention很类似,实际上,self-attention是其一个特例。但是作者认为,这种注意力不是不可或缺的,f 函数的表现形式还可以有下列两种: Dot product 通过点乘进行相似度计算: 归一化因子可以直接设置为N,也就是X的所有位置数。
main 1Branch0Tags Code README Apache-2.0 license Ali Jamali,Swalpa Kumar Roy,Avik Bhattacharya, andPedram Ghamisi This Keras code is for the paper A. Jamali, S. K. Roy, A. Bhattacharya and P. Ghamisi, "Local Window Attention Transformer for Polarimetric SAR Image Classification," in IEEE ...
NA is just a subgraph of the self attention graph. There are some obvious disadvantages of this compared to Natten. Specifically, This has quadratic computation cost wrt height * width. It must materialize the entire attention mask. But, if we use some variant of sliding-window attention, we...