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 …

Abstract Density Peak (DPeak) clustering algorithm is not applicable for large scale data,
due to two quantities, ie, ρ and δ, are both obtained by brute force algorithm with complexity …

We analyze the drawbacks of DBSCAN and its variants, and find the grid technique, which is
used in Fast-DBSCAN and ρ-approximate DBSCAN, is almost useless in high dimensional …

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 …

One fundamental challenge of vehicle re-identification (re-id) is to learn robust and
discriminative visual representation, given the significant intra-class vehicle variations …

Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field
of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to …

Large-scale data clustering is an essential key for big data problem. However, no current
existing approach is “optimal” for big data due to high complexity, which remains it a great …

Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across
different cameras. It is fundamentally challenging because differences between vehicles are …

With the continuous development of intelligent transportation systems, vehicle-related fields
have emerged a research boom in detection, tracking, and retrieval. Vehicle re-identification …

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