Relieving Universal Label Noise for Unsupervised Visible-Infrared Person
Re-Identification by Inferring from Neighbors
Relieving Universal Label Noise for Unsupervised Visible-Infrared Person
Re-Identification by Inferring from Neighbors
Unsupervised visible-infrared person re-identification (USL-VI-ReID) is of great research and practical significance yet remains challenging due to the absence of annotations. Existing approaches aim to learn modality-invariant representations in an unsupervised setting. However, these methods often encounter label noise within and across modalities due to suboptimal clustering results and considerable …