# Multi-Level Fusion Model for Person Re-Identification by Attribute Awareness
## 3 Proposed Method  
### Network Structure  
We describe the network structure in detail. Figure 1: Pedestrian flexibility and rigidity bring challenges to pedestrian re-recognition. (**a**) Learning the semantic information of pedestrian attributes. The red text represents some features of the pedestrian. (**b**) The different states of two different pedestrians. (**c**) The distance between different pedestrians. The double arrows show their distance. Different colors indicate the distance between different pedestrians, and these distances need to be pulled apart. Figure 2 shows the proposed network framework, which has two parts: attribute recognition and identity recognition, corresponding to A and B, respectively. A and B have parts in common. Features of attribute identification and the global branch are derived from \"Feature Aggregation 1\". The features of the local branch are derived from \"Feature Aggregation 2\". Unlike the global branch, the local branch needs to produce features of six different parts. We separate the identity network from the entire network framework to understand the structure more clearly, as shown in Figure 3. Figure 3 is a multi-level feature-fusion network focusing only on identity recognition. It is a person Re-ID network framework without attribute recognition. Figure 3 contains the global feature-extraction module and local feature-extraction module. The goal of the global module is to extract global information on pedestrians. The purpose of the local module is to align the pedestrian within the boxes. After the local average pooling layer, six different part-level features' expressions are generated. Figure 2: Network structure. It contains two parts: the training and testing phase. The middle part of the figure represents the common part of training and testing. It contains the pedestrian identity and attributes recognition. The training part needs to calculate the loss function, and the model weights are updated using backpropagation. In the test phase, the results of each indicator are output through feature fusion. Backbone uses different colors to distinguish the four different network layers of ResNet. In the training phase, the attribute-identification branch, the result of the global branch and the local branch calculate the loss value through the loss function, and complete backpropagation. The \"C\" in module A of Figure 2 represents a concat function to combine the classification results of all attributes. BCE loss is used to calculate the difference between the predicted and real attributes and to learn the relationship between pedestrian attributes and aggregated features through backpropagation. In the testing phase, the results of the global and local branches are fused, and the required results are output by an evaluation function. Points \"1\" and \"2\" of Figure 3 represent the output results of person re-identification with attributes in the testing phase. The metrics in \"1\" of Figure 3 are commonly used for person re-identification with attributes. It contains F1 score, recall and accuracy. To provide a clearer backbone to what is proposed in this paper, Figure 3 shows a pedestrian re-recognition network framework without attribute recognition. The framework describes the workflow of the global and local branches in more detail. \"G\" in the figure represents the global branch and \"L\" represents the local branch. In the training phase, we preprocess the input images, which plays the role of data enhancement. The preprocessing operation includes five data-enhancement modules: resize, random horizontal flip, pad, random crop, and random erasing. These can help prevent the network from falling into local extrema during the training process, which leads to overfitting. Figure 3: Global and local branch network, which is a multi-level feature-fusion network. It is a pedestrian re-recognition network framework without attribute recognition. After local average pooling, six different part features are generated, represented by different colors in this figure.