# An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification
## 3 Methods
### Multi-Scale Focusing Attention Network  
In this section, the architecture of MSFANet is depicted in detail, which specializes in learning multi-scale feature representation for a person ReID task. We mainly introduce the backbone of MSFANet, following two heads as full connected (FC) layer for classifica  
Figure 2: Examples of the results of different data augmentation methods on MSFANet. These methods contain random flipping, random cropping, random erasing, random patching, and autoaugment, shown in columns 2 through 5, respectively. tion and global average pooling layer for capturing person's ReID features, respectively. The backbone consists of a standard \(7\times 7\) convolution and average pooling layer, connecting three multi-scale focusing attention with different quantities and sizes. The benefits of this design are that it cannot only extract higher robustness multi-scale person ReID features, but also has much lower computational costs, compared with OSNet [27]. **Focusing Attention (FA) Block. ** Attention is a more efficient way of capturing more robust multi-scale features in computer vision tasks, which is often used in transition block or aggregation block after multiple branches network rather than focusing on the inside of a feature extraction module in each branch. The FA block, shown in Figure 2(a), is a lightweight feature extraction block with attention in the proposed MSFANet. FA block contains depthwise separable \(1\times 1\) convolutions and \(3\times 3\) convolutions in the front, then connects batch normalization and Leaky_ReLU layers, and binds a Squeeze-and-Excitation (SE) block [18] at the back, shown in Figure 2(b). A significant advantage of the FA block is that it can extract more elaborative ReID features inside the block, which can be embedded into a subnet of deep neural network expediently. Furthermore, it is a relatively lightweight design that can reduce the computational cost in training and inferencing. SE block is referred to SENet [18], while we replace Relu with Leaky_ReLU in the activation layer. In our experiments, this small change can provide a better convergence during the training process. The SE block principles are leveraging two fully connected (FC) layers to reduce and increase the channel's dimension, respectively, obtaining the attention weight \(w_{c}\) of the input \(x\), and then employing the channel-wise multiplication between the \(w_{c}\) and the input \(x\). Thus, the FA block provides a means of gaining robust attention features based on depthwise separable convolution, which will primarily be employed to the MSFANet. **Multi-Scale Focusing Attention (MSFA) Block. ** In order to achieve multi-scale feature learning, we design a new multi-branch structure with residual connection based on FA blocks, called Multi-Scale Focusing Attention (MSFA) block, shown in Figure 4. The block has three branches, each of which has 1, 3, 5 FA blocks that corresponding receptive fields are 3, 7, 11, respectively. This design can effectively extract the features of bigger cross-scale targets. Besides, we refer to the Aggregation Gate (AG) module of OSNet [27] to fuse the features of different branches. Thus, our proposed MSFA Block is capable of  
Figure 3: An illustration of Focusing Attention (FA) block as (**a**) and Squeeze-and-Excitation (SE) block as (**b**). FA block consists of depthwise separable convolution and SE block, the latter’s architecture is shown in (**b**). capturing much more discriminative and robust features for ReID. The input \(x_{l}\) is from the last layer's output, \(x_{l}\in R^{N\times C\times H\times W}\).