Unsupervised Object Re-identification via Instances Correlation Loss

This paper studies the fully unsupervised object re-identification (re-ID) problem which can learn re-ID without any human-annotated labeled data. Recent works show that self-supervised momentum contrastive learning is an effective method for unsupervised object re-ID, but they neglect to optimize one important component - the similarity relationships among instances. Previous works focus on enforcing instance-to-centroid learning, which does not fully utilize the inter-instances information. Thus, we propose an Instances Correlation Loss (ICL) to enforce instance-to-instance learning in each training iteration. Experimental results show that the proposed ICL effectively boost the performance, which demonstrates that learning strategy is also a central importance to unsupervised re-ID task. Extensive experiments are performed on three mainstream person re-ID datasets and one vehicle re-ID dataset.