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...
MSCA: Multi-Scale Channel Attention Module. Contribute to eslambakr/EMCA development by creating an account on GitHub.
This paper proposes a multi-scale channel attention UNet (MSCA-UNet) to raise the accuracy of the segmentation in medical ultrasound images. Specifically, a multi-scale module is constructed to connect and to enhance the feature maps with different scales extracted by convolution. Subsequently, A ...
1研究动机 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...
Then the multi-scale channel attention module extracts context information of different scales from the high-level feature map, enhances the useful features and suppresses the useless feature response. Finally, the context information obtained in the previous step is passed into the decoder to obtain ...
当Sim(Q,K)=ReLU(Q)ReLU(K)T的时候,公式1就是论文的Linear attention。证明过程如下 Linear attention确实快,但是模型的容量、学习能力是比原始的softmax attention差一些的。 为此,论文引入多尺度tokens(multi-scale tokens)。 具体在下图展示。 图左边是论文提出的EfficientViT Module。由两个模块组成,一个是FFN...
Architecturally, it consists of a Multi-Scale Coupled 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 ...
In addition, an improved feature fusion module is applied to integrating both the low-level and high-level features for multi-scale object detection. Through such a manner, the accuracy of small object detection is improved. The backbone network adopts ResNet with s...
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...