Vehicle re-identification is one of the core technologies of intelligent transportation systems,
and it is crucial for the construction of smart cities. With the rapid development of deep …

Transformers are more and more popular in computer vision, which treat an image as a
sequence of patches and learn robust global features from the sequence. However, pure …

Part feature learning is critical for fine-grained semantic understanding in vehicle re-
identification. However, existing approaches directly model part features and global …

Existing vehicle re-identification methods commonly use spatial pooling operations to
aggregate feature maps extracted via off-the-shelf backbone networks, such as visual …

Vehicle Re-Identification (ReID) aims to find images of the same vehicle from different
videos. It remains a challenging task in the video analysis field due to the huge appearance …

Vehicle re-identification (Re-ID) has been attracting increasing interest in the field of
computer vision due to the growing utilization of surveillance cameras in public security …

In general, vehicle images have varying resolutions due to vehicles' movements and
different camera settings. However, most existing vehicle reidentification models are single …

The misalignment of features caused by pose and viewpoint variances is a crucial problem
in Vehicle Re-Identification (ReID). Previous methods align the features by structuring the …

Abstract The AI City Challenge has been created to accelerate intelligent video analysis that
helps make cities smarter and safer. With millions of traffic video cameras acting as sensors …

Vehicle Re-Identification (ReID) is of great significance for public security and intelligent
transportation. Large and comprehensive datasets are crucial for the development of vehicle …