# Multi-Level Fusion Model for Person Re-Identification by Attribute Awareness
## 2 Related Work  
Person re-identification solves the problem of matching pedestrian images between unrelated cameras. It faces challenges caused by different perspectives, postures, occlusion, and other issues. To solve these problems, we need to increase the inter-class distance and reduce the intra-class distance. Traditional methods depend on metric learning [10; 11; 12] and deep learning [13; 14]. Learning each attribute independently is an intuitive idea, but it makes person attribute recognition redundant and inefficient. Therefore, researchers tend to evaluate all attributes in a network model, and evaluate each attribute as its own task. Due to the high efficiency of multi-task learning, researchers have been paying increased attention to it [15; 16; 17; 18]. [PERSON] et al. [6; 7] proposed a network framework combining pedestrian ID labels and attributes, breaking through the traditional limitation of learning only using pedestrian ID labels. They construct a multi-task network that simultaneously learns pedestrian ID labels and predicts attribute labels by introducing pedestrian attribute labels. Person re-identification is challenging because pedestrians are both flexible and rigid, and camera devices are different and greatly affected by the environment. These multi-task networks have high accuracy in attribute recognition, but do not perform person re-identification reliably. Unlike these networks, to improve the accuracy of person re-identification, this paper uses pedestrian attribute labels as an aid, and discusses their function in pedestrian recognition. [PERSON] et al. [6] and [PERSON] et al. [7] build a multi-task network by introducing pedestrian attribute labels, enabling the network to learn identity and attributes. [PERSON] et al. labeled pedestrian attributes and combined attribute learning and global learning of pedestrian re-identification methods. However, there has been no quantitative analysis of attributes, and the problem of hard samples is not considered. [PERSON] et al. added hard-sample learning to force the network to extract more advanced semantic features. However, the network lacks local learning capability and has limited improvement effects. In this paper, we design a multi-level fusion model with joint local and global learning and introduce various tricks to improve the learning ability, improving the discriminative ability of the network as a whole. Figure 1 shows an example. Figure 1a shows the role of attributes in pedestrian re-recognition. Through ID tag learning and attribute learning, the network can learn semantic information of pedestrian attributes. However, pedestrians are flexible and rigid, which makes it difficult to re-identify pedestrians. For example, Figure 1b,c. Figure 1b show two different identities. Each identity has two pictures, but they have different points. For example, in the first identity, pedestrians sometimes carry a bag and sometimes do not. In identity 2, sometimes pedestrians ride bikes and sometimes they walk. Figure 1c shows three different identities. Their jackets are all yellow with similar characteristics. If you only use ID tags for learning, it is hard to tell these identities apart. The first identity and the second identity are both male, and the third identity is female. Attribute learning can separate them. The second identity is wearing a backpack and riding a bike. Attribute learning can separate these, too. [PERSON] et al. [18] used attributes to assist a person re-identification network, fusing the low-level feature distance and attribute-based distance as the final distance to distinguish whether a given image has the same identity. Attributes have been introduced to video-based person re-recognition because of their role in detection and recognition. [PERSON] et al. [19] proposed an attribute-driven method for feature decomposition and frame weighting. The sub-features are re-weighted through the confidence of attribute recognition and integrated into the time dimension as the final representation.