Abstract Object Re-identification (Re-ID) aims to identify specific objects across different
times and scenes, which is a widely researched task in computer vision. For a prolonged …

Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) is an
extremely important and challenging task, which can alleviate the issue of expensive cross …

Most existing unsupervised person re-identification (Re-ID) methods use clustering to
generate pseudo labels for model training. Unfortunately, clustering sometimes mixes …

Unsupervised visible-infrared person re-identification is a challenging task due to the large
modality gap and the unavailability of cross-modality correspondences. Cross-modality …

Unsupervised visible-infrared person re-identification (USL-VI-ReID) is a promising yet
highly challenging retrieval task. The key challenges in USL-VI-ReID are to accurately …

Person re-identification (Re-ID) has achieved great success in single-domain. However, it
remains a challenging task to adapt a Re-ID model trained on one dataset to another one …

The annual cost of vehicle insurance fraud is estimated to exceed 40 billion dollars. This is
an enormous amount considering the number of new vehicles insured yearly. In terms of …

Visible infrared person re-identification (VI-ReID) aims at searching out the corresponding
infrared (visible) images from a gallery set captured by other spectrum cameras. Recent …

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

In person re-identification (ReID), very recent researches have validated pre-training the
models on unlabelled person images is much better than on ImageNet. However, these …