由两个模块组成,一个是FFN+DWConv,另一个是Multi-Scale Linear Att。 图右边是Multi-Scale Linear Att,也就是实现多尺度tokens的模块。 Input经过线性层生成QKV。 QKV分三路走,一路直接进入ReLU Linear Attention,第二路经过3x3的卷积再进入ReLU Linear Attention,第三路经过5x5的卷积再进入ReLU Linear Attention...
To improve the object detection performance of YOLOv5, this paper proposes Conv and Efficient Multi-Scale Attention (CEMA), a new novel module used in YOLOv5, it fusion C3 module and EMA attention. The performance at different locations is compared and analyzed. Experimental results used the ...
1、Multi-scale Large Kernel Attention (MLKA) MLKA首先使用 Point-wise conv 改变通道数,然后将特征 split 成三组,每个组都使用 VAN 里提出的大核卷积来处理(即depth-wise conv,dilated conv,和 point-wise conv 的组合)。三组分别使用不同尺寸的大核卷积(7×7、21×21、35×35),膨胀率分别设置为(2,3...
1、Multi-scale Large Kernel Attention (MLKA) MLKA首先使用 Point-wise conv 改变通道数,然后将特征 split 成三组,每个组都使用 VAN 里提出的大核卷积来处理(即depth-wise conv,dilated conv,和 point-wise conv 的组合)。三组分别使用不同尺寸的大核卷积(7×7、21×21、35×35),膨胀率分别设置为(2,3...
结构概述:HCANet采用了U型网络结构,其中包含多个Convolution Attention Mixing(CAMixing)块。每个CAMixing块由两部分组成:卷积和注意力融合模块(CAFM)以及多尺度前馈网络(MSFN)。 CAFM模块:在CAFM模块中,局部分支利用卷积和通道重排来提取局部特征,全局分支则利用注意力机制来捕获长距离依赖关系。这种结合了卷积和注意力的...
deep-learningpytorchstyle-transfermulti-scalepytorch-implementationavatar-netfeature-decoration UpdatedJul 24, 2023 Jupyter Notebook Implementation of "SpEx: Multi-Scale Time Domain Speaker Extraction Network". speech-processingspeech-separationmulti-scalespexconv-tasnettarget-speaker-extractionspeaker-separation ...
Multi-scale coupled attention for visual object detection Article Open access 16 May 2024 Introduction Recently, the amount of available data has considerably increased owing to the developments of Internet of Things, technological devices, and computational machines. Because of the widespread usage of...
YOLO series are very classic detection frameworks in the field of object detection, and they have achieved remarkable results on general datasets. Among them, YOLOv5, as a single-stage multi-scale detector, has great advantages in accuracy and speed, but
())# 结合高度和宽度特征,应用分组归一化x2=self.conv3x3(group_x)# 对重构后的张量应用3x3卷积x11=self.softmax(self.agp(x1).reshape(b*self.groups,-1,1).permute(0,2,1))# 对 x1 进行自适应平均池化并应用Softmaxx12=x2.reshape(b*self.groups,c//self.groups,-1)# 重构 x2 的形状为 (b...
论文《EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction》来自ICCV2023。 动机:高分辨率密集预测模型需要的计算成本高,因此提出多尺度线性注意模块,使用RELU相信注意力替换softmax注意力降低计算复杂度,并通过卷积增强RELU注意力的局部信息提取能力,通过多尺度token提高多尺度学习能力。