# A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification
## 3 Approach  
### Multi-Scale Convolutional Model Based on Single Attention  
ResNet proved that using residual blocks to learning the residuals between inputs and outputs is simpler and more efficient than learning the mapping between them directly. Res2 Net further adopts grouping convolution to construct finer quadratic residual connections in the residual block, allowing the network to have richer receptive fields to learn multi-scale features. However, the random grouping of Res2 Net in the residual block affects the robustness and intuitive interpretability of deep neural networks. This section will introduce a multi-scale convolutional model based on single attention (AMS-CNN). AMS-CNN is superimposed by multiple AMS-blocks. Its core content is the addition of the \"Channels-Sort\" module to Res2-block. The \"Channels-Sort\" module ranks each channel in the residual block in descending order by feature importance. The AMS-block then groups the sorted residual blocks and performs secondary residual learning. Finally, the \"\(1\times 1\)\" convolution is used to adjust the number of channels and fuse all channel features. AMS-bclok subtly combines the attention mechanism with multi-scale features through the Channels-Sort module, which can focus on specific targets and achieve multi-scale observation of objects. The AMS-block structure is shown in Figure 1.  
As shown in Figure 1, Res2-block is a decomposition of the traditional Residual block, so that the model can learn multi-scale features in a single residual block. AMS-block introduces the Channels-Sort module based on the structure of Res2-block. The other difference from Res2-block is that we perform secondary residual learning for each grouping of the residual block, as shown in the right of Figure 1, where \(X_{1}\) is transformed to \(Y_{1}\). This can further enrich the receptive fields of convolution operations and increase multi-scale information. The structure of the Channels-Sort module is illustrated in Figure 2.  
Figure 1: The structure schematic of three blocks. The former is Res-block, the middle represents Res2-block, and the latter describes AMS-block. Figure 2: The structure schematic of Channels-Sort module. As shown in Figure 2, \(X_{0}\) obtains \(X\) via a normal convolution operation or a convolution operation that introduces channel attention [22; 24] or space attention [23; 24]. The dotted box indicates the Channels-Sort module. The first half performs the Squeeze-and-Excitation operation, and the second half sorts the channels of the feature maps in descending order according to the feature importance learned by Squeeze-and-Excitation. The process of transforming \(X\) into \(V\) adopts two methods: Global average-pooling and depth-wise convolution, where \(V=[V1,V2]\). \[V_{1}=DWConV(K_{a},X)=\sum_{i=1}^{2}(K_{ai}\odot X_{i}) \tag{1}\]  
\[V_{2}=GlobleAvgPool(X) \tag{2}\]  
\[V=ReLU(Concat(V_{1},V_{2})) \tag{3}\]  
where \"\(DWConV\)\" means depth-wish convolution [31], and \(K_{a}\) is the convolution kernel of \(X\) to \(V\), \(K_{a}\) = [\(K_{a1}\), \(K_{a2}\),..., \(K_{a2}\)], where the number of channels of the convolution kernel is 1. \"\(GlobleAvgPool\)\" represent global average-pooling operation. Finally, \(V_{1}\), and \(V_{2}\) are connected in series along the direction of the feature channel and activated by the \(ReLU\) function to obtain \(V\). \[V^{{}^{\prime}}=Sigmoid(ConV(K_{b},V))=Sigmoid(K_{b1}\odot V) \tag{4}\]  
After the convolution and activation operations, \(V\) obtains the vector \(V^{\prime}\) that characterizes the importance of the feature channels. The convolution kernel is \(K_{b}\) (\(K_{b}\) = [\(K_{b1}\)]) and the activation function is \(ReLU\). \[X^{{}^{\prime}}=Add(Multiply(V^{{}^{\prime}},X),X)=(V^{{}^{\prime}}\otimes X) \oplus X \tag{5}\]  
where \(V^{\prime}\) represents an important factor of the feature channel. \(X^{\prime}\) is first multiplied by \(X\) and \(V^{\prime}\) element by element, and then the result is added to \(X\) element by element.