Dynamic Clustering and Label Weighting for Unsupervised Aerial-Ground Person Re-Identification

Aerial-ground person re-identification (AG-ReID) aims to match images of the same person across aerial and ground views. To reduce manual annotation costs, recent studies have explored unsupervised AG-ReID (UAG-ReID). Existing UAG-ReID methods typically perform intra-view clustering followed by cross-view matching to obtain identity pseudo-labels. Although these methods reduce annotation costs, they overlook the reliability of pseudo-labels. Specifically, clustering algorithms inevitably introduce errors that can cascade and amplify during the matching process. To address these issues, we propose a dynamic clustering and label weighting method (DCLW) that introduces global dynamic clustering (GDC) and label dynamic weighting (LDW) to improve the reliability of clustering and mitigate the impact of noisy labels. Specifically, GDC jointly clusters aerial and ground images while dynamically adjusting the proportion of images from each view among the K nearest neighbors of each image. This avoids cascading errors caused by the two-stage process and aligns the results of clustering more closely with the prior data distribution. Based on GDC, LDW reweights image pseudo-labels to further mitigate the impact of noisy labels. In addition, DCLW introduces a mixture-pure contrastive loss which leverages single-view clusters to supplement cross-view supervision and promote view-invariant identity learning. We evaluate DCLW under multiple cross-view datasets, and the results demonstrate its superiority and the effectiveness of its components.