# Learning Part-Based Features for Vehicle Re-Identification with Global Context
## 2 Motivation and Related Works
### Vehicle Re-Identification Datasets
extracted region feature vectors for multiple region segmentation results using a convolutional neural network and obtained appearance feature vectors of the target vehicle by fusing them with global feature vectors [28]. Other works used short and densely connected convolutional units without a backbone network to develop an architecture from scratch for feature learning. This is then utilized with a Siamese network-type architecture for improving feature detection and with a modified quadrature pooling layer for feature generation [29; 30; 31]. [PERSON] proposed a feature learning approach that employs a spatial transformer network-based localization model for integrating global and local features into a representation for VReID [32]. The major disadvantage of models that learn attributes or local features is that a separate module is required to extract attributes, which increases complexity. Methods for learning part-wise features follow a strategy that involves dividing either the raw image or the convolutional tensor into different parts or directing attention to specific regions within them. Convolutional features are then learned from these targeted areas. This approach addresses a major limitation of the methods described above, i.e., it does not require any additional networks or training to identify the local features and can be used in a straightforward manner [33]. The part-based convolutional baseline, or PCB [14], which uses horizontal part-based features as local features, has been widely used in person re-identification. Refined part pooling has also been used to address partial inconsistencies by training the learned parts. Another work [34] introduced a two-branch, part-based network with horizontal and vertical partitioning, as well as an external identity memory to store the features from the two branches separately. The network also includes a triplet center loss and softmax loss for metric learning. The DGPM (Distance-Based Global and Partial Multi-Regional Feature) [35] model consists of three branches--one global and two local branches, one with horizontal parts and the other with vertical parts. The local branches incorporate a self-attention module prior to partitioning in order to suppress background information inside the parts. The authors of [36] utilized three branches, namely a global, local, and complementary branch, for feature extraction. Their method uses partition-based as well as local attribute extraction for robust feature learning. A two-branch model, with one global branch and another part-based local branch using horizontal parts and an attention block prior to partitioning, was described in [37]. In [38], the authors proposed the Multi-Granularity Network (MGN), a three-branch network with one global branch and two part-based branches with a varying number of parts. Each part-based branch includes its own global branch. In unsupervised re-identification methods, complementary part-based and global features are used to generate labels. The complementary association of these features is achieved by exploiting the k-nearest neighbors in the part and global feature spaces [33]. A global-local supervision scheme with channel partitions was proposed in [15]; however, it involves a complex attention layer applied to the channel parts and training of the global layer, which increases complexity. URRNet addresses the problem of viewpoint variation while preserving vehicle details in the re-identification problem by integrating 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 [39]. DRReid was trained on multiple source domains and evaluated on unseen target domains for person re-identification [40]. An identity-guided spatial attention mechanism was used to mask discriminative areas, without the need for manual annotation, to address vehicle re-identification in [41].