NPLP: A Noisy Pseudo-Label Processing Approach for Unsupervised Domain-Adaptive Person Re-ID

Abstract

Most of the existing unsupervised cross-domain person re-identification (re-ID) methods utilize pseudo-labels estimation to cast the unsupervised problem into a supervised problem, whose performance is limited by the quality of pseudo-labels. To address the problem, we propose a noisy pseudo-label processing (NPLP) approach to suppress the pseudo-labels noise and improve the performance of the person re-ID model. Specifically, we first summarize two types of pseudo-label noise that lead to the collapse of the re-ID model, as defined as mixed noise and fragmented noise. Secondly, we propose a different method which is composed of Startup Stage and Correcting Stage for pseudo-labels estimation to relieve these two types of noise respectively. The Startup Stage aims to decrease the ratio of the fragmented noise by increasing the recall of the clustering results. At the Correcting Stage, we evaluate the quality of the pseudo-labels and correct those low-quality pseudo-labels to suppress the mixed noise and generate more reliable pseudo-labels for the re-ID model to learn. At last, we build a feature learning strategy for unsupervised re-ID task and learn from the denoised pseudo-labels iteratively. Extensive evaluations on three large-scale benchmarks show that the NPLP is competitive with most state-of-the-art unsupervised re-ID methods.