## 3 Methods
### SliceSamp: The Downsampling Component  
The SliceSamp component consists of two main layers: a slicing layer and a depthwise separable convolution layer including depthwise convolution and pointwise convolution, as shown in Figure 2. For each channel of the input feature map, we first obtain four sub-maps by slicing and then implement depthwise separable convolution on the four sub-maps. Slicing provides a means of implementing downsampling by reducing the dimensions of feature maps without increasing the number of model parameters. Subsequently, depthwise separable convolutions (DSConvs) can simultaneously capture the spatial and channel-wise features of the downsampled outputs, thereby ensuring timely learning and the effective extraction of image features. To provide a clearer description of the designed neural network component, the pseudocode for the SliceSamp algorithm is provided in Algorithm 1:  
```
/*Performs downsampling on input features using the SliceSamp algorithm. */ Input:\(\mathbf{X}\in\mathbb{R}^{\mathbb{B}\times\mathbb{C}\times H\times W}\), vector representations of the input feature map, where B is the batch size, C is the dimension, H is the feature map height, and W is the feature map width. Output:\(\mathbf{output}\in\mathbb{R}^{\mathbb{B}\times\mathbb{C}\times\frac{H}{\times}\times \frac{W}{\times}}\), updated vector representations after downsampling using SliceSamp. Parameters:\(\mathbf{W_{\text{depthwise}}}\in\mathbb{R}^{\mathbb{C}_{\text{in}}\times\mathbb{C}_{ \text{in}}\times\mathbb{K}\times\mathbb{K}}\), \(\mathbf{b}_{\text{depthwise}}=0\) \(\mathbf{W_{\text{pointwise}}}\in\mathbb{R}^{\mathbb{C}_{\text{in}}\times\mathbb{C }_{\text{out}}\times 1\times 1}\), \(\mathbf{b}_{\text{pointwise}}=0\), where Cin is the number of input channels, C\({}_{\text{out}}\) is the number of output channels, and K represents the spatial dimensions of each convolutional kernel. 1. X_slice \(\leftarrow\) [ // Slice the input feature map \(\mathbf{X}\) [..., :2.; :2], // Upper-left corner \(\mathbf{X}\) [..., :1.; :2.; ], // Upper-right corner \(\mathbf{X}\) [..., :2.; 1.; 2], // Lower-left corner \(\mathbf{X}\) [..., :1.; 2.; 1.; 2] // Lower-right corner \(\mathbf{X}\) [..., :2.; 1.; 2] // Lower-right corner ] 2. X_concat \(\leftarrow\) concatenate (x_slice, axis=1) // Merge slices in channel dimension 3. Depthwise_conv \(\leftarrow\)\(\mathbf{W_{\text{depthwise}}}\) * X_concat + \(\mathbf{b}_{\text{depthwise}}\) // Depthwise Separable Convolution 4. Depthwise_bn \(\leftarrow\) BatchNormal (Depthwise_conv) 5. Depthwise_act \(\leftarrow\) GELU (Depthwise_bn) 6. Pointwise_conv \(\leftarrow\)\(\mathbf{W_{\text{pointwise}}}\) * Depthwise_act + \(\mathbf{b}_{\text{pointwise}}\) 7. Pointwise_bn \(\leftarrow\) BatchNormal (Pointwise_conv) 8. output \(\leftarrow\) GELU (Pointwise_bn) 9. returnoutput
```  
**Algorithm 1**_output_\(\leftarrow\) SliceSamp (X)  
In the slicing layer, four sub-maps are created by slicing each channel of the original feature map along the width and height dimensions with a stride of two, and the four sub-maps are then concatenated along the channel dimension. By slicing along the odd and  
Figure 2: The architecture of the SliceSamp component. even indexes on the spatial dimensions, respectively, the four sub-maps become half of the original feature map in both width and height, while the channel dimension is expanded to 4\(\times\) the input size, and the other dimensions remain unchanged. It is interesting that the slicing operation achieves downsampling and retains all features only via a simple dimensional transformation. In the depthwise separable convolution layer, a series of efficient convolution operations are employed for the concatenated sub-maps from the slicing layer. There are two main operations: depthwise convolution and pointwise convolution.