https://www.selleckchem.com/products/OSI-906.html
Visible infrared person reidentification (VI-REID) plays a critical role in night-time surveillance applications. Most methods attempt to reduce the cross-modality gap by extracting the modality-shared features. However, they neglect the distinct image-level discrepancies among heterogeneous pedestrian images. In this article, we propose a reciprocal bidirectional framework (RBDF) to achieve modality unification before discriminative feature learning. The bidirectional image translation subnetworks can learn two opposite mappings betwee