最后,Coordinate Attention Block的输出可以写成: 3.2.3 CA Block的PyTorch实现 import torchfrom torch import nnclass CA_Block(nn.Module):def __init__(self, channel, h, w, reduction=16):super(CA_Block, self).__init__()self.h = hself.w = wself.avg_pool_x = nn.AdaptiveAvgPool2d((h,...
一个coordinate attention块可以被看作是一个计算单元,旨在增强Mobile Network中特征的表达能力。它可以将任何中间特征张量作为输入并通过转换输出了与张量具有相同size同时具有增强表征的。为了更加清晰的描述CA注意力,这里先对SE block进行讨论。 3.1 Revisit SE Block 在结构上,SE block可分解为Squeeze和Excitation2步,...
CA attention通过两个步骤用精确地位置信息编码channel关系和长距离依赖关系:CA attention信息嵌入和坐标注意力生成,所提出的CA block如图2。 全局池化通常用于channel attention,对空间信息进行全局编码,但它将全局空间信息压缩到channel描述符中,因此很难保存位置信息。而位置信息对于捕捉视觉任务重的空间结构至关重要。为...
(1, 1)) if num_classes > 0: self.fc = nn.Linear(512 * block.expansion, num_classes) def _make_layer(self, block, planes, blocks, stride=1, dilate=False): norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride...
原理不做赘述,是一种类似于SE的即插即用的小模块,本文主要在于记录如何使用PaddlePaddle 实现这个block. CV技术指南:CVPR2021| 继SE,CBAM后的一种新的注意力机制Coordinate Attention249 赞同 · 9 评论文章 图(c) CA模块结构 # CA (coordinate attention)importpaddleimportpaddle.nnasnnimportmathimportpaddle.nn....
作者认为之前优秀的注意力模块如SE(Squeeze-and-Excitation attention)和CBAM(Convolutional block attention module)在对通道间关系进行建模时虽然取得了不错的效果,但是却丢失了空间上的位置信息。而其他没有这个问题的注意力模块虽然效果也不错,但是参数量又太大了,不适合应用于移动端设备的网络。所以作者希望能有⼀...
(RPN)fusion and coordinate attention into Siamese trackers.The proposed network framework consists of three parts:a feature-extraction sub-network,coordinate attention block,and cascaded RPN block.We exploit the coordinate attention block,which can embed location information into channel attention,to ...
Either horizontal or vertical direction attention performs the same to the SE attention When applied to MobileNeXt, adding the attention block after the first depthwise 3x3 convolution works better Note sure whether the results would be better if a softmax is applied between the horizontal and vertic...
Besides, in order to generate feature maps with high quality, a novel residual dense block with coordinate attention is proposed. In addition to reducing gradient explosion and gradient disappearance, it can reduce the number of parameters by 5.3 times compared to the original feature pyramid ...