## 1 Introduction  
Vehicle re-identification (re-ID) returns a series of images containing the same vehicle ID as that of an image from a database. It is widely used in intelligent transportation, public security, and urban computing [1; 2; 3]. The straightforward way of vehicle re-ID is license-plate recognition [4]; however, a license plate is not always visible. Figure 1 shows the cases in which a vehicle ID cannot be determined based on the license plate. For example, the license plates of vehicles are sometimes occluded, illegally used, or invisible in some views. In particular, the number plates, models, and colors of genuine cars are sometimes used in other vehicles for performing illegal activities, such as smuggling, assembling, and scrapping and stealing vehicles, and the act of doing so is known as \"car cloning.\" Therefore, vehicle IDs cannot be distinguished only based on license plates in several scenarios, and therefore, vehicle re-ID through other image-based features is urgently needed (Code available at [[https://github.com/[PERSON]/LFASM_pytorch](https://github.com/[PERSON]/LFASM_pytorch)]([https://github.com/[PERSON]/LFASM_pytorch](https://github.com/[PERSON]/LFASM_pytorch))). In recent years, deep learning [5], person re-ID [6; 7; 8] and fine-grained retrieval [9; 10] have gained remarkable success. Vehicle re-ID datasets, such as VeRi-776 [3], VehicleID [11], and PKU-VD [12], have been released, thereby facilitating research on deep learning. However, because of the inconspicuous divergences among vehicles, vehicle re-ID is still difficult. The main challenge of vehicle re-ID is distinguishing between two vehicles of the same or similar types. Images of different IDs which are captured from the same view may be more alike than those with the same ID but captured from different angles. Owing to camera resolution and shooting angle, obtaining a very high-quality vehicle image is sometimes difficult. Thus, vehicles always have inconspicuous differences, as shown in Figure 2. Different vehicles of the same model are similar in global appearance, and thus difficult to distinguish. Most existing studies focus on the entire image, and such subtle differences cannot be easily distinguished. Unlike general classification problems, the number of categories in a re-ID problem is uncertain. Therefore, some metric learning methods are committed to reducing the distance between images of the same vehicle, and enlarging the distance between images of different vehicles. [PERSON] et al. [13] proposed triplet loss, which directly optimizes the feature embedding. [PERSON] et al. [14] combined the local structural  
Figure 1: Cases in which vehicle IDs cannot be determined according to the license plate: (**a**) License plate occlusion (e.g., dirt and reflection); (**b**) illegal use of license plate (e.g., License plate does not match the car); and (**c**) invisible license plate. Figure 2: (**a**) The two vehicles above are of the same type but have different IDs. They can be distinguished based on the windshield stickers (green box) and ornaments (red box). The two vehicles below are of a similar type but have different engine hoods (pink box), headlights (blue box), and air intakes (yellow box). (**b**) It is obviously easier to distinguish the vehicles based on their key parts. constraints to generate feature embedding more effectively. [PERSON] et al. (2015) proposed the Triplet-Center loss, which jointly considers the distances inside a class and relationships between different classes. In this paper, we propose an effective feature extractor to find more fine-grained features by training it using two supervising methods. The first is an end-to-end classification module, in which a local net is aimed at selecting the regions of interest and another extractor, transposed convolutional layer (CTL), is proposed to find more implicit features. The other supervising method is a Siamese net, which matches the local features of two images and supervises attention better.