Extracting robust feature representation is one of the key challenges in object re-
identification (ReID). Although convolution neural network (CNN)-based methods have …

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 …

Cross-view geo-localization is a task of matching the same geographic image from different
views, eg, unmanned aerial vehicle (UAV) and satellite. The most difficult challenges are the …

In recent years, the research community has approached the problem of vehicle re-
identification (re-id) with attention-based models, specifically focusing on regions of a …

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 …

Pre-trained vision-language models like CLIP have recently shown superior performances
on various downstream tasks, including image classification and segmentation. However, in …

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

Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention
recently. Particularly given the reliance on emerging deep learning methods, which have …

To achieve robustness in Re-Identification, standard methods leverage tracking information
in a Video-To-Video fashion. However, these solutions face a large drop in performance for …