Unsupervised Person Re-ID via Loose-Tight Alternate Clustering

Abstract

Recently developed clustering-guided unsupervised methods have shown their superior performance in the person re-identification (re-ID) problem, which aims to match the surveillance images containing the same person. However, the performance of these methods is usually very sensitive to the change of the hyper-parameters in the clustering methods, such as the maximum distance of the neighbors and the number of clusters, which determine the quality of the clustering results. Tuning these parameters may need a large-scale labeled validation set, which is usually not applicable in unlabeled domain and hard to be generalized to different datasets. To solve this problem, we propose a Loose-Tight Alternate Clustering method without using any sensitive clustering parameter for unsupervised optimization. Specifically, we address the challenge as a multi-domain clustering problem, and propose the Loose and Tight Bounds to alleviate two kinds of clustering errors. Based on these bounds, a novel Loose-Tight alternate clustering strategy is adopted to optimize the visual model iteratively. Furthermore, a quality measurement based learning method is proposed to mitigate the side-effects of the pseudo-label noise by assigning lower weight to those clusters with lower purity. Extensive experiments show that our method can not only outperform state-of-the-art methods without manual exploration of clustering parameters, but also achieve much higher robustness against the dynamic changing of the target domain.