# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
## 3 Proposed Method
### Classification and Regression Sub-Network  
The aim of an attention mechanism is to allow the model to learn how to allocate its own attention and weight the input signal. An attention mechanism scores each dimension of the input and then weights the features according to the score, increasing the weight of interesting parts and decreasing the weight of uninteresting parts, so that the network adaptively highlights the features that are important to the downstream model or task. In this work, two attention modules, namely, channel attention and spatial attention modules, are implemented in the classification and regression sub-network (CNN2), as shown in Figure 5. The features are first reduced in dimensionality via a group convolution [13]; then, a PW convolution [27] is used for feature fusion, and finally the dual channel and spatial attention module is followed. The multi-scale feature aggregated channel attention block (MCA) is a mechanism for tuning the network at the channel level, as shown in Figure 6. The input features are first divided into four parts, each of which is reduced to half of the original channel via a convolution layer. Two operations are performed independently: one directly uses global average pooling to make the features \(1\times 1\times C\) in size, with a global perceptual field, aggregating the global features and squeezing information from the channels after the sigmoid activation to obtain the channel weights, which are then multiplied back to the divided features; the other uses an additional convolution layer and then performs the same operation as the former. The four parts adopt the same operation and concatenate together, completing the attention enhancement of the channel dimension, making the network automatically focus on the channels that are important. The MCA block is based on Equations (8)-(10), where \(F\) is the input, \(S\) is the split operation, \(Cat\) is the concatenate operation, \(\delta\) is the activation function, \(C_{1}\) and \(C_{2}\) represent the convolution layers, and \(GAP\) stands for global average pooling. \[F_{1}=C_{1}(S(F)) \tag{8}\]  
\[F_{SE1}=Cat(\delta(GAP(F_{1}))\times F_{1},\delta(GAP(C_{2}(F_{1})))\times C_{2 }(F_{1})) \tag{9}\]  
\[F_{SE}=Cat(F_{SE1},F_{SE2},F_{SE3},F_{SE4}) \tag{10}\]  
Figure 5: Illustration of the classification and regression sub-network (CNN2). The global-to-local-information-fused spatial attention block (GSA) is similar to the channel attention block in that it weights the network from the spatial dimension as shown in Figure 7. The same input features are divided into four parts, using two convolution layers, average pooling, and maximum pooling [28] for each feature point of the network along the channel direction to obtain four 1*h*w feature maps. The pooling map and convolution map are concatenated before another convolution layer to obtain weights in the spatial dimension, which are then multiplied back to the input. Two parts are then added to complete the attention enhancement of the spatial dimension, making the network focus on the more important regions. We employ the GSA block in Equations (11)-(13). \[F_{SPA1}=F\times C_{2}(Cat(C_{1}(F),GAP(F))) \tag{11}\]  
\[F_{SPA2}=F\times C_{4}(Cat(C_{3}(F),GMP(F))) \tag{12}\]  
\[F_{SPA}=F_{SPA1}+F_{SPA2}, \tag{13}\]  
where _GAP_ and _GMP_ represent average pooling and maximum pooling, \(F\) is the input feature, \(C_{1},C_{2},C_{3},C_{4}\) represent the convolution layers, and _Cat_ is the concatenate operation. After the template features are correlated with the search features (pixel-level correlation), they are fed into the classification and regression sub-networks (CNN2), which predict whether it is an object or background, along with the bounding box of the target. As shown in Figure 8, the two sub-networks use the same correlation module as the input and do not use separate correlation modules, which also reduces the amount of computation  
Figure 6.0 The multi-scale feature aggregated channel attention block (MCA).