## 4 Method
### Bottleneck Part
#### 4.2.2 Loss-Guided Collaborative Attention Block (LGCA Block)  
During the forward propagation of features, semantic features are enhanced while detailed information is gradually suppressed, thereby compromising the discriminative capability of the features. In this study, we propose a novel LGCA block, as shown in Figure 6, that combines the cross-entropy auxiliary loss function with the ground truth and deep features from the grasping region, self-attention mechanism, and MUS module. This module guides the network to focus on contextual information within graspable regions in order to enhance their discriminative ability. Compared to existing multi-branch and multi-scale context strategies based on hole convolution, which only considers spatial relationships between pixels, our LGCA block enhances each pixel's contribution to grasp representation by considering associations between all the graspable object regions and individual pixel points. It also facilitates the selective learning of graspable regions while suppressing interference from non-graspable regions, thus improving the generalization capabilities of grasping detection algorithms. The LGCA block consists of a loss-guided layer, a collaborative attention block based on local jump, and a multi-scale module. Each of these components will be individually introduced in the following sections. 1. Loss guide block During the process of network training, the loss-guided layer will be up-sampled into \(T_{6}\in\mathbb{R}^{128\times h/4\times w/4}\) to \(F_{l}\in\mathbb{R}^{1\times h\times w}\) with the same initial input size through the \(1\times 1\) convolution layer and the up-sampling layer based on the bilinear interpolation method. Then, the difference between \(F_{l}\) and the standard grab pose pixels generated by the data  
Figure 5: RSE block structure. Utilizing \(T\in\mathbb{R}^{128\times h/4\times w/4}\) as the input image, undergoing two convolutional layers and one SE block, while maintaining consistent input and output image sizes. Figure 6: Loss-guided collaborative attention block (LGCA block). The input feature T6 is initially processed by the loss-guided layer to obtain \(\text{F}_{l}\), followed by subsequent processing through the coordination attention module and the MUS module, resulting in a refined feature map \(T_{7}\in\mathbb{R}^{128\times h/4\times w/4}\). label is taken as a loss. The loss is added to the total loss to guide the network to notice the best pose of the object. 2. Collaborative attention block  
It is crucial to accurately understand the object feature information in complex backgrounds, but traditional convolutional calculations with sliding operations can only establish connections between adjacent pixels, while they cannot establish effective connections between pixels in different channels or positions. Moreover, the semantic information in the deep layers of the network is expressed in different channels, but traditional 2D convolution methods only operate within a single channel, without establishing connections between channels. Therefore, in space, we establish spatial connections between pixels by means of cutting, transposition, and assembly, and in different channels, we establish connections between channels by performing average pooling down-sampling on each channel and then concatenating the results of the convolution. By adding channel-wise attention blocks, we can more accurately capture the semantic features of objects, as shown in the structure of the collaborative attention block in Figure 7.  
The collaborative attention block comprises a spatial-correlation attention block and a channel-correlation attention block. The input of the channel-correlation attention module is T6, and its output has an identical shape. Initially, the vector Sc is obtained by average pooling down-sampling for each channel, and then the 1 \(\times\) 3 convolution kernel (step size is 1) is employed for the one-dimensional convolution of Sc, and the feature information of the adjacent channels is convolved and fused.