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.
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...
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...
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 ...
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...
The proposed MSA module directly extracts the attention information of different scales from a feature map, that is, the multi-scale and attention methods are simultaneously completed in one step. In the MSA module, we obtain different scales of channel and spatial attention by controlling the ...
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: ...
multi-scale fine fusion: 在这个除段引入 了 channel attention unit 来实现 discriminative learning enahncement through focusing on the most informative scale-specific knowledge, making the cooperative representation more efficient. 为了降低复杂度,采用了 U-shape 的结构,如下图所示。 reain streak reconstruc...
channel dependencies of feature maps. One is Position-wise Attention Block (PAB), and the other is Multi-scale Fusion Attention Block (MFAB). ThePAB is used to obtain the spatial dependencies between pixels in feature maps by a self-attention mechanism manner. The MFAB is used to capture ...