## 2 Method
### Grouping Weighted Module  
The output features of MSL-FPN in Figure 1 are obtained after the mask branch convolutional layer. In order to reduce the number of parameters, the mask branch compacts the number of mask feature channels from 128 to 8. Moreover, the mask feature does not perform any cross-channel operation after passing through the convolutional layer of the mask branch, resulting in a lack of information interaction between the channels of the mask feature. To solve the above problems, we propose the Grouping Weighted Module (GWM), which is used after the mask branch convolutional layer to group the mask features, and perform cross-channel feature learning in the subspace of each group feature to solve the problem of weak interaction information and loss of deep semantic information in the masked feature dimension.  
As shown in Figure 4, GWM average mask features according to the channel can be divided into four groups: \(M_{q1},M_{q2},M_{q3},M_{q4}\), in which each group characteristics of the channel number is 2, and characteristics of each group are respectively sorted for empowerment residual operation. Firstly, the grouped features of each group are input into the grouped convolution \(Conv_{1\times 1}^{a}\) of a single channel, and the feature information of each channel of the single group features is extracted, respectively. After that, it is input into the Max pooling layer and the average pooling layer to extract the salient semantic features and the mean semantic features, respectively. Then, the features extracted by the two pooling layers are subjected to \(1\times 1\) convolution \(Conv_{1\times 1}^{b}\) for channel compression, and the number of channels is compressed from 2 to 1. After batch normalization and activation function, the results are input into the normalized exponential function to calculate their weights. Further, a \(1\times 1\) convolution \(Conv_{1\times 1}^{c}\) is used to expand the number of channels to the pre-compression size. Finally, the calculated weights are multiplied with the original single set of features to obtain the maximum weighted feature and the average weighted feature, respectively, and then the two weighted features are added with the original single set of features to obtain the weighted residual feature. After the above operations, the four groups of features are concatenated according to the channel, and finally the group-weighted features that complete cross-channel learning in different subspaces are obtained. The mask feature of GWM makes up for the loss of deep semantic information caused by channel plunge. The GWM calculation process can be described as follows:  
\[\begin{cases}M_{G}=ConC(IW(M_{q1}),IW(M_{q2}),\\ IW(M_{q3}),IW(M_{q4}))\\ IW(x)=ConC((MW(x)+x),\\ (AW(x)+x),x)\\ MW(x)=Conv_{1\times 1}^{c}(SM(Conv_{1\times 1}^{b}\\ (MP(Conv_{1\times 1}^{a}(x)))))\\ AW(x)=Conv_{1\times 1}^{c}(SM(Conv_{1\times 1}^{b}\\ (AP(Conv_{1\times 1}^{a}(x)))))\end{cases} \tag{6}\]  
where \(M_{G}\) denotes grouping weighted features; \(ConC(\cdot)\) represents the concatenation operation; \(M_{q1}\), \(M_{q2}\), \(M_{q3}\), \(M_{q4}\) for the mask features divide the channel after each group; \(IW\) represents the internal pooling weighting operation for each group of features. \(MW\) is the Max pooling weighting in the internal pooling weighting operation. \(AW\) is the average pooling weighting in the internal pooling weighting operation. \(MP(\cdot)\), \(AP(\cdot)\), and \(SM(\cdot)\) are max pooling, average pooling, and normalized exponential functions, respectively.  
The mask features extracted by GWM are concatenated with the relative position matrix and then input into the mask dynamic convolution to make the semantic information of the mask features extracted by GWM more abundant so as to predict a more accurate instance mask.