MSCA: Multi-Scale Channel Attention Module. Contribute to eslambakr/EMCA development by creating an account on GitHub.
例如,Self-Attention只关注图像内部的相互关系,忽略了跨空间相关性;Channel Attention只考虑通道的权重分配,无法捕捉到细粒度的空间信息;Spatial Attention只通过人工规则对不同区域进行加权,缺乏自适应性。因此,这些传统方法在面对复杂场景下的多尺度特征提取和整合任务时存在一定的局限性。 4.2 现有的多尺度模型及其不足...
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 ...
Efficient Pyramid Multi-Scale Channel Attention Modules: To capture the fine-grained multi-scale local feature and establish the long-term dependencies between channels, an efficient pyramid-type multi-scale channel attention (EPMCA) module is proposed, as shown in Fig. 5. It first extracts the ...
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 ...
To solve these two problems, a channel attention module is adopted to use a local cross-channels interaction strategy without dimensionality reduction. This module realizes the information association between channels and learns the correlation between features of different ch...
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: b).Channel attention module(CAM):捕获通道间存在的依赖关系,增强特定的语义特征表示 ...
In order to focus on key expression features, an attention mechanism is introduced in the network, the channel attention module is improved by grouping convolution operations, learning the weight information of different channels, obtaining attention feature maps, enhancing the expression ability of ...
Channel Attention Module 整个注意力模块 给定引导注意模块输入时的特征图F,由F_{MS }和F_{s}^{'}连接生成,它通过多步细化生成注意特征 (以下部分不是太懂) 其中Ei(.)是第i个编码器-解码器网络的编码表示,FiA表示在第i个双重注意模块之后产生的注意特征,M是迭代次数。具体来说,在第一个编码器-解码器(n...
Qi J, Peng Y, Yuan Y (2018) Cross-media multi-level alignment with relation attention network. In: Proceedings of the 27th Intrnational Joint conference on artificial intelligence, pp 892–898 Qian K, Tian L (2021) A topic-based multi-channel attention model under hybrid mode for image cap...