Pseudo Label Based on Multiple Clustering for Unsupervised Cross-Domain Person Re-Identification

Abstract

Person re-identification (Re-ID) has achieved great improvement with the development of deep learning. However, domain adaptation in unsupervised Re-ID has always been a challenging task. Most existing works based on clustering only cluster once, which may lead to pseudo labels of poor quality. In this letter, we propose a Pseudo Label based on Multiple Clustering (PLMC) approach, which makes full advantage of multiple clustering to obtain more robust pseudo labels. In particular, our PLMC framework consists of two stages, namely, global training stage, and local training stage. We adopt the training strategy that combines the information learned from global features, and local features by training two stages alternately. Extensive experiments are carried out on three standard benchmark datasets (e.g., Maket1501, DukeMTMC-ReID, CUHK03). The results demonstrate that our PLMC method is superior to the previous methods based on single clustering, and achieves state-of-the-art person Re-ID performance under the unsupervised cross-domain setting.