## 4 The Components of FAGD-Net  
### Res-EMA Module  
Existing works claim that attention mechanisms play a crucial role in improving the expressive capability of grasping networks, as evidenced by studies such as [10; 11; 12; 14]. However, there is still potential for optimization in these attention mechanisms. To bridge this gap, we introduce advanced Multi-Scale Attention (EMA) [31] into the realm of grasping, thereby introducing a novel Res-EMA module. Figure 3 illustrates the structure of Res-EMA. The first part comprises the residual block structure [32], and the second part is EMA. Through skip connections, the features outputted by the backbone are aggregated with those from the attention module, aiding the network in avoiding the issues of gradient vanishing and exploding. Within the residual block structure, there are two convolution modules. Convblock1 includes a 3 \(\times\) 3 convolution, batch normalization, and Rectified Linear Unit (ReLU) activation. The second convolution module repeats the operations of the first convolution module, followed by a 3 \(\times\) 3 convolution and batch normalization. The second part consists of Efficient Multi-Scale Attention (EMA). Firstly, the features after convblock2, denoted as \(F\in\mathbb{R}^{C\times H\times W}\), are divided into \(G\) sub-features in the channel dimension. This grouping aims to learn distinct grasp semantic information, thereby  
Figure 3: The Res-EMA module. enhancing the model's expressive capacity. The grouping process can be described by Equation (8). \[F=[F_{0},F_{i},\dots,F_{G-1}],F_{i}\in\mathbb{R}^{C//G\times H\times W} \tag{8}\]  
Subsequently, similar to the operations in Coordinate Attention [15], given the input features, 1D global average-pooling is applied along the horizontal and vertical directions separately to encode spatial position information. Through this operation, positional information is encoded into the feature map to enhance sensitivity to spatial positions, highlighting feasible grasping regions. The 1D global average-pooling along the horizontal direction, denoted for the c-th channel over the spatial dimension \(H\), can be described by Equation (9). \[z_{c}^{H}(H)=\frac{1}{W}\sum_{0\leq i\leq W}f_{c}(H,i) \tag{9}\]  
Similarly, the 1D global average pooling along the vertical axis \(W\) for the c-th channel can be described by Equation (10):  
\[z_{c}^{W}(W)=\frac{1}{H}\sum_{0\leq j\leq H}f_{c}(j,W) \tag{10}\]  
where \(c\) represents the number of input channels and \(H\) and \(W\) denote the spatial dimensions of the input features. The aforementioned operations are consistent with Coordinate Attention [15]. The shared components with Coordinate Attention are named the 1 \(\times\) 1 branch, while the branch with the 3 \(\times\) 3 kernel is named the 3 \(\times\) 3 branch. After feature encoding along the two image height directions, a shared 1 \(\times\) 1 convolution is employed. Following the decomposition of the output of the 1 \(\times\) 1 convolution into two vectors, the sigmoid function is utilized to fit a 2D binomial distribution on the linear convolution. Feature interaction across channels is achieved by using multiplication to aggregate two channel-wise attention maps within each group, facilitating comprehensive feature representation. The 3 \(\times\) 3 branch employs a 3 \(\times\) 3 convolutional operation to capture inter-channel feature interactions, thereby expanding the feature space. The output of the 1 \(\times\) 1 branch is aggregated with the output of the 3 \(\times\) 3 branch across different spatial dimensions, enabling a more comprehensive aggregation of grasp-related features. Following the output of the 1 \(\times\) 1 branch, 2D global average pooling is applied to encode global spatial features and model long-range dependencies. The 2D global average pooling method can be described as Equation (11)  
\[z_{c}=\frac{1}{H\times W}\sum_{j}^{H}\sum_{i}^{W}f_{c}(i,j) \tag{11}\]  
After 2D global average pooling, a Softmax function is applied to fit a linear transformation.