# Learning Part-Based Features for Vehicle Re-Identification with Global Context
## 4 Experimental Results and Analysis
Here, the global branch has two fully connected layers--one for ID prediction and the other for attribute prediction (model/type)--with a DenseNet backbone. TCPM uses part-based horizontal and vertical partitioning in two branches, along with a triplet center loss and a softmax loss for optimization [35]. A three-branch network with a global branch and two part-based branches (one with horizontal parts and the other with vertical parts) is used in DGPM, along with an attention module to suppress background information interference in the part-based branches [36]. The method also uses spatio-temporal information (camera and time) to improve accuracy. However, our method generates results without spatio-temporal information for a better comparison. AttributeNet is a joint network that generates attribute-based and global re-identification features [51]. A constraint is used to encourage the joint feature. PGAN is a local feature/attribute aggregating method with a local feature branch and a global feature branch [52]. However, it uses an attention mechanism to assign weights to the local features prior to aggregation with the global feature. URRNet integrates global and local features. The global features are enhanced using a graph convolutional network, and then the keypoints, viewpoint, and multiscale characteristics of the vehicle are extracted as local features [39]. MDFE-Net includes a non-local attention block to enhance the long-range dependence of discriminative features [42]. A three-branch structure with one global and two local branches was proposed in [16]. The local branches include channel attention modules to generate subtle discriminative features. A two-stagefeature generation method was proposed in [45], where the pixel-level feature map is first transformed into a patch-level map within a global context; then, the patches are further refined in a local context using self-attention. Analysis on VeRi: In Table 2, it can be seen that our proposed approach outperformed the other methods in mean average precision. However, AttributeNet [51] and PGAN [52] outperformed our proposed approach in Rank-1 accuracy. AttributeNet uses separate networks to generate attributes, which adds to the complexity of the network. PGAN uses attribute-based features along with an attention model, which increases complexity. Our model outperformed SAN, which combines a global network with a horizontal part-based network [27]. This result is significant because it establishes the motivation for our proposed approach in that our unifying framework can effectively replace a global feature branch. Our approach also outperformed general transformer-based methods like TransReid and Swin Transformer, as well as recent state-of-the-art methods like URRNet, MIMANet, and CFSA. Analysis on VehicleId: In Table 3 it can be seen that our proposed model outperformed the other models in Rank-5 accuracy. However, in Rank-1 accuracy, VANet [48] and DGPM [35] performed better due to their complex attention mechanism and partitioning in multiple dimensions. Again, our model outperformed SAN [27], thereby establishing the superiority of our induced global similarity learning over direct global feature learning. Our approach outperformed most recent state-of-the-art approaches on the VehicleId dataset.