## 3 Research Methodology
### Convolutional Row-Column Attention Block (CRCAB)  
Recently, some studies [39; 42; 43] have shown that the performance of the network can be improved by spatial information interaction, which can capture global information by self-attention, but which causes a huge memory overhead for high resolution images. In this investigation, we implement spatial information interaction in a unique way, improving the memory overhead during information interaction. In the following, we describe our proposed method in detail. CRCAB uses convolution to obtain row-to-row and column-to-column dependencies and, through this dependence, it aggregates information to build features with stronger representation ability. First, CRCAB takes the maximum and mean values in the row and column directions of the features and adds them to obtain the compression features. The compressed features are then sent to the convolutional operation for learning to obtain weights. To obtain more comprehensive information, a row-column self-attention mechanism similar to [39] and a finer row-column attention mechanism are implemented in CRCAB. Finally, the results obtained by the two row-column attention mechanisms are stacked on the channel dimension and then convolution is implemented to fuse them to build new features. To describe the structure of our CRCAB more clearly, we explain the two operations separately (see Figures 5 and 6). The two parts of the compressed feature are shared during implementation. Figure 4: Detailed construction of DAB. The detailed structure of the convolutional row-column self-attention mechanism is shown in Figure 5; we implement a self-attention mechanism similar to that in [39] on both row and column directions. The row direction 1 is taken as an example and the column direction 2 is the same. Firstly, a squeeze operation is performed in the row direction (the maximum and mean values are added) to obtain the compressed features (the shape is B C H W; B C H W here still represents the batch-size, channels, height, and width of features, and the value of W is 1). Next, the convolution operation is performed to obtain weights (the shape is B H H 1). To realize the self-attention mechanism, it is necessary to adjust the learned weight to five dimensions (namely, the shape is B 1 H H 1). Then, a softmax operation is performed in the row direction. Because the weight is five dimensions, it is necessary to expand the input features into five dimensions (the shape is B C 1 H W), namely, the unsqueezed process shown in Figure 5. Then the expanded features are multiplied with the learned weights. Finally, a sum operation is performed in the row direction to reconstruct the features. To implement the self-attention mechanism in the row-column direction to construct features, it can be simply represented by Equations (1) and (2):  
\[X_{i}=\partial_{i1}X_{1}+\partial_{i2}X_{2}+\ldots+\partial_{iH}X_{H}\quad( \partial_{i1}+\partial_{i2}+\ldots+\partial_{iH}=1)\ (i=1,2,3,\ldots,H) \tag{1}\]  
\[X_{j}=\partial_{j1}X_{1}+\partial_{j2}X_{2}+\ldots+\partial_{jW}X_{W}\quad( \partial_{j1}+\partial_{j2}+\ldots+\partial_{jW}=1)\ (j=1,2,3,\ldots,W) \tag{2}\]  
In CRCAB, along the row and column directions, the features are divided into H and W feature vectors. These feature vectors are represented in Equations (1) and (2). \(X_{i}\) represents the vector of the \(i\)-th row of new features, \(X_{j}\) represents the vector of the \(j\)-th column of new features, \(X_{1}\), \(X_{2}\),..., \(X_{H}\) and \(X_{W}\) represent the row and column vectors of the original features, and H, W represent the height and width of the original features. Figure 5: Detailed structure of the row-column self-attention mechanism. Figure 6: More detailed structure of the row-column attention mechanism. In Equations (1) and (2), \(\partial\) represents the weights learned after the convolution operation (for an image, if the weights learned for the row direction are adjusted and the final shape is 1 1 H H 1, it can be considered as a H\(\times\)H matrix.