Polarized self-attention是一种改进的自注意力机制,旨在提高自注意力模型的效率和准确性。传统的自注意力机制通常需要计算所有位置对所有位置的注意力,这会导致计算复杂度的增加和模型的训练时间的延长。Polarized self-attention通过引入极化因子来减少需要计算的位置对之间的注意力,从而提高自注意力模型的效率。极化因子...
classPolarizedSelfAttention(nn.Module):def__init__(self, channel=512):super().__init__()# 通道注意力权重计算层self.ch_wv = nn.Conv2d(channel, channel //2, kernel_size=(1,1))self.ch_wq = nn.Conv2d(channel,1, kernel_size=(1,1))# 通道和空间注意力的softmaxself.softmax_channel ...
基于上面的思想,作者提出了Polarized Self-Attention (PSA)机制,同上面的思想一样,作者也是现在一个方向上对特征进行压缩,然后对损失的强度范围进行提升,具体可分为两个结构: 1)滤波(Filtering):使得一个维度的特征(比如通道维度)完全坍塌,同时让正交方向的维度(比如空间维度)保持高分辨率。 2)High Dynamic Range(HD...
yolov10 引入 classPolarizedSelfAttention(nn.Module):def__init__(self,channel=512):super().__init__()# 通道注意力权重计算层self.ch_wv=nn.Conv2d(channel,channel//2,kernel_size=(1,1))self.ch_wq=nn.Conv2d(channel,1,kernel_size=(1,1))# 通道和空间注意力的softmaxself.softmax_channel=...
self.backbone(x) File "/home/aistudio/psanet-main/paddleseg/models/backbone/hrnetv2_psa.py", line 602, in forward def forward(self, x_in): x = self.conv1(x_in) x = self.bn1(x) ~~~ <--- HERE x = self.relu(x) x = self.conv2(x) File "/opt/conda/envs/python35-paddle12...
1. 论文和代码地址论文链接：https://arxiv.org/pdf/2107.00782.pdf官网代码：https://github.com/DeLightCMU/PSA （暂未开源）核心代码：https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/master/attention/PolarizedSelfAttention.py2. Motivation细粒度的像素级任务（比如语义分割）一直都是...
This is an official implementation of "Polarized Self-Attention: Towards High-quality Pixel-wise Regression" - DeLightCMU/PSA
In this paper, we present the Polarized Self-Attention (PSA) block targeting the high-quality pixel-wise mapping with: (1) Polarized filtering: keeping high internal resolution in both channel and spatial attention computation while completely collapsing input tensors along their counterpart dimensions...
Light-Atten-Pose algorithm uses lightweight EfficientNet network and polarized self-attention (PSA) mechanism on the basis of AlphaPose, which reduces the computation amount by using EfficientNet network, and the data is finely processed by using PSA mechanism in spatial and channel dimensions. ...
Polarized Self-Attention 结构简洁,由两个分支组成,即通道分支(Channel Branch)和空间分支(Spatial Branch),两个分支分别使用Softmax-Sigmoid 组合非线性对通道和空间维度进行处理。两个分支可并行、可串行。PSA 中的 Polarized Filter 及 Enhancement 分别通过以下步骤操作: Polarized Filter 折叠: 将输入张量沿着一个维...