# Multi-Level Fusion Model for Person Re-Identification by Attribute Awareness
## 4 Experiment
### Ablation Study  
To better illustrate the effectiveness of the proposed method, we carried out ablation experiments for the three modules of non-local, instance batch normalization, and attributes. According to the Rank1, mAP, and mINP metrics, we evaluated whether to join the experimental results of these three modules. It can be seen from Figures 7 and 8 that, compared with \"without multi-level\", both the performance of Rank-1 and mAP of MLAReID are improved more obviously in the iteration process, whether it be Market-1501 or DukemtMC-ReID. It shows that MLAReID proposed in this paper pays more attention to pedestrian images comprehensively and abstractly, and can better extract the semantic information of pedestrian images. In Table 4, \"\(\surd\)\" means to use a module, and a blank means to not use it. Without using the three modules of non-local, instance batch normalization, and attributes, we obtained 94.1% Rank1, 85.0% mAP, and 57.1% mINP on the Market-1501 dataset. When the attribute module was applied, Rank1 rose by 0.1%, mAP by 1%, and mINP by 2.1%. Application of the three modules produced much better results than the model with no modules. Rank1 improved by 2%, mAP by 5.3%, and mINP by 13.9%. For the DukeMTMC-reID dataset, without using the three modules, Rank1 was 85.9%, mAP was 74.8%, and mINP was 36.4%. After applying the three modules, Rank1 increased by 5.5%, mAP by 6.6%, and mINP by 11.5%. From the ablation experiment, we can see that these three modules improved network performance, and we verified the effectiveness of the proposed algorithm. In Figures 7 and 8, our ablation experiments on Market-1501 and DukeMTMC-reID for multi-level fusion module are listed. \"MLAReID\" represents a multi-level fusion model based on ResNet. \"without multi-level\" represents the model without a multi-level fusion module. As can be seen from Figures 7 and 8, for both Market-1501 and DukeMTMC-reID, the algorithm in this paper has a more comprehensive and abstract focus area for pedestrian images, which can better express the semantic information of images. To better verify the effectiveness of the proposed algorithm, we compared the visualization results of the two networks. \"ID\" in these figures (Figures 11-14) represents the query label, and serial numbers from 1 to 10 represent the sorting results from largest to smallest. Numbers in red indicate an incorrect match, and green indicates a correct match. The first line represents the visualization results of the baseline without attributes, and the second line represents the visualization results of the proposed method. From Figure 11, we can find that the baseline (without local branch, non-local, IBN, and attribute) using no attributes has more mismatches. For example, for input image with ID 94, the top 10 images have many matching errors. This method makes an error in the \"backpack\" attribute, and regards a pedestrian without a backpack as an exact match. In addition, there is a matching error in the \"clothing\" attribute. For the pedestrian with ID 934, the baseline (without non-local, IBN, and attribute) does not correctly match the \"hair\" attribute. The method proposed in this paper accurately recognizes the key attributes of the pedestrian. The network has learned the relationship between key attributes of the pedestrian and pedestrian features, as well as between pedestrian identity labels and features. Figure 10: Cumulative Match Curve (CMC) on different datasets. Figure 9: Receiver operating characteristic (ROC) curve on different datasets. From Figure 12, we selected pedestrian images with IDs 47 and 288 from the query. The baseline (without local branch, non-local, IBN, and attribute) that did not use attributes had more incorrect matches. For the pedestrian with ID 47, the baseline (without local branch, non-local, IBN, and attribute) had some errors on the \"backpack\" and \"bag\" attributes. For the pedestrian with ID 288, the baseline did not correctly match the \"hair\" attribute.