However, there are still challenges in predicting large-scale soil properties. This study proposes a new convolutional neural network architecture (MCA-CNN) combined with visible and near-infrared spectroscopy for predicting soil properties. The architecture introduces a multi-scale channel attention ...
This is an original Pytorch Implementation for our paper "EMCA: Efficient Multi-Scale Channel Attention Module" 1- Abstract: Attention mechanisms have been explored with CNNs,both across the spatial and channel dimensions. However,all the existing methods devote the attention modules to cap-ture loc...
链接:https://scholar.google.com/scholar?hl=zh-CN&as_sdt=0%2C5&q=Efficient+Multi-Scale+Attention+Module+with+Cross-Spatial+Learning&btnG= 动机:通道或空间注意机制对于产生更多可识别的特征表示具有显着的有效性。然而,通过通道降维来建模跨通道关系可能会对提取深度视觉表征带来副作用。 方法: Multi-Scale...
3. 解释说明 efficient multi-scale attention module 的关键要点: 3.1 多尺度特征提取和整合策略: 多尺度特征提取是指在图像或视频处理中,通过使用不同感受野大小的卷积核进行多层级的特征提取。在efficient multi-scale attention module中,采用了一种创新的策略来同时提取不同尺度下的特征。具体而言,模块中包含多个并...
这篇论文提出了一种新型的高效多尺度注意力(Efficient Multi-Scale Attention, EMA)模块,旨在解决现有注意力机制在提取深度视觉表示时可能带来的计算开销问题。作者指出,尽管通道或空间注意力机制在多种计算机视觉任务中表现出显著的有效性,但通过通道降维来建模跨通道关系可能会影响特征的深度表示。因此,EMA模块专注于在...
Channel Attention (MSCCA) module, and a Multi-Scale Coupled Spatial Attention (MSCSA) module. Specifically, the MSCCA module is developed to achieve the goal of self-attention learning linearly on the multi-scale channels. In parallel, the MSCSA module is constructed to achieve this goal ...
Aiming to address the abovementioned issues, we first propose a multi-scale channel attention network with an adaptive feature fusion strategy (MSCAN-AFF), which calibrates the multi-scale feature-wise responses and then fuses only relevant feature channels to improve the representational power of ...
Firstly, the fusion of global and local features is adopted to obtain more information of the vehicle and enhance the learning ability of the model; Secondly, the channel attention module in the feature extraction branch is embedded to extract the personalized features of the targeting vehicle; ...
2.2 Spatial and Channel self-attention modules a).Position attention module(PAM):捕获长距离依赖,解决局部感受野的问题 3个分支,前两个分支 和 计算位置与位置之间的相关性矩阵: 再由位置之间的相关性矩阵 指导第三条分支 计算得到空间注意力图,与输入进行加权和: ...
C. Spatial and Channel self-attention modules 我们使用上标p来表示特征图属于位置注意模块。同样地,我们也将使用上标c来表示通道注意模块的特征。 Position attention module (PAM):设表示F∈R^{C\times W\times H}为注意模块的输入特征映射,其中C、W、H分别表示通道、宽度和高度维度。在上分支F通过一个卷积块...