## 4 Object-Aware Adaptive Convolution Kernel Attention Mechanism  
### Analysis of the Attention Mechanism in the Pre-Training Stage  
A typical attention module usually consists of a sub-neural network connected to the original neural network. The attention module and the original model are trained simultaneously to obtain the optimal parameters. In this work, we present the convolution kernel-oriented \"Squeeze-and-Excitation\" attention mechanism [19] (referred to as the SE attention module) based on the Siamese network object tracking framework. While the Siamese network uses a series of image pairs to train the network, the SE attention module has the ability to simultaneously learn the weight of each convolution kernel in the object tracking task and improve the neural network's feature extraction of the object sensitive area. The architecture of a typical SE attention module is shown in Figure 5.  
Figure 4: The response maps of different convolution kernels in response to the object’s position (“jump” video sequence). where \(U\) represents the output convolution feature by a certain layer and \(F_{sq}(\cdot)\) denotes a global information compression method for convolution feature \(U\). Here, this method is implemented using global average pooling. The calculation process is expressed as follows:  
\[z_{c}=F_{sq}(u_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}u_{c}(i,j) \tag{1}\]  
\(F_{\text{ex}}(\cdot,W)\) is a weighting process for \(z_{c}\), where \(W\) represents the parameters used in the weighting process. These parameters are obtained automatically during end-to-end training of the deep learning network. When embedding the SE attention module in the Siamese network, we use ReLU and a fully connected layer to realize \(F_{\text{ex}}(\cdot,W)\). Each convolution kernel \(F_{scale}(\cdot\cdot)\) uses the scalar \(z_{c}{{}^{\prime}}\) to multiply the original convolution feature \(u_{c}\in\mathbb{R}^{H\times W}\) to achieve the weighting of the feature. The \(F_{scale}(\cdot,\cdot)\) output weighted convolutional layer features are denoted by \(\widetilde{U}\). The core of the SE attention module lies in the acquisition of the weighted parameter \(W\). Under normal circumstances, the SE attention module should be combined and synchronized with the backbone network for training. The network architecture after embedding the SE attention module in the Siamese network object tracking framework is shown in Figure 6.  
During the training process, the SE attention module in the Siamese network learns the importance of each convolution kernel in the corresponding convolution layer based on back propagation. During the tracking process, the attention mechanism uses the importance weight of each convolution kernel to adjust the object convolution feature in order to improve the discrimination of the convolution feature and assist the object template correlation calculation for obtaining a more significant object response. It should be noted that the two branches of the Siamese network share the same parameters and that only one branch needs to be trained during the network training. In this work, we implement the aforementioned Siamese network architecture based on the Matconvnet framework [20] and embed the SE attention module in the Conv3, Conv4, and Conv5 convolutional layers of the SiamFC. We train the network using the same training process used by the classic Siamese network tracking method. After the training is completed, we use a Siamese network with SE attention mechanism to re-test the \"soccer\" video sequence and analyze its convolution kernel weights. The results and assigned weights of each convolution corresponding to the object are shown in Figure 7.  
Figure 5: The architecture of the SE attention module. Figure 6: The structure of Siamese neural network with SE attention module. In Figure 7, the number below each convolution kernel response map represents the weight given by the SE attention module. It is evident that the weights of the SE attention module are not clearly distinguishable.