Unsupervised visible-infrared person re-identification (US-VI-ReID) centers on learning a
cross-modality retrieval model without labels reducing the reliance on expensive cross …

This paper proposes a unified multi-modal image generation method to address two critical
challenges in visible-infrared (VI) person re-identification (ReID): the insufficiency of training …

In recent years, growing needs for advanced security and traffic management have
significantly heightened the prominence of the visible-infrared person re-identification …

Person re-identification (Re-ID) emerges as a captivating realm within computer vision,
dedicated to the task of recognizing the same individual across diverse camera angles or …

Deep learning-based methods have become increasingly popular in person re-identification
(re-id) as they enhance the discriminative features while suppressing extraneous or …

Visible-infrared person re-identification (VI-ReID) remains a challenging task due to the
inconsistencies in data distribution and semantic inconsistency between heterogeneous …

Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality retrieval
task due to the large modality gap. While numerous efforts have been devoted to the …

Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to explore the
cross-modal associations and learn modality-invariant representations without manual …

Prevalent nighttime person re-identification (ReID) methods typically combine image
relighting and ReID networks in a sequential manner. However, their performance …

Average precision-based loss functions are effective for training deep neural networks in
ranking tasks and single-modality person re-identification. However, they have difficulty in …