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

Domain generalization (DG), which doesn't require any data from target domains during
training, is more challenging but practical than unsupervised domain adaptation (UDA) …

Vehicle re-identification (Re-ID) aims to retrieve images with the same vehicle ID across
different cameras. Current part-level feature learning methods typically detect vehicle parts …

Reidentification (Re-id) of vehicles in a multicamera system is an essential process for traffic
control automation. Previously, there have been efforts to reidentify vehicles based on shots …

Vehicle re-identification aims to identify vehicles from different cameras and has drawn
much attention in the multimedia community. In recent years, significant achievements in …

Fine-grained image categorization aims to distinguish the sub-categories from a certain
category of images. Generally, fine-grained data sets have the characteristics of the intra …

Data sparsity and cold start are two critical issues which need to be addressed in
recommender systems (RSs). Currently, most methods address these issues by applying …

The key challenge of unsupervised vehicle re-identification (Re-ID) is learning discriminative
features from unlabelled vehicle images. Numerous methods using domain adaptation have …

Vehicle reidentification has seen increasing interest, thanks to its fundamental impact on
intelligent surveillance systems and smart transportation. The visual data acquired from …

Abstract Re-identification (re-ID) aims to search the target images of pedestrians or vehicles
with the same identity in the non-overlapping camera network. Some factors, such as …