Unsupervised Maritime Vessel Re-Identification With Multi-Level Contrastive Learning

Qian Zhang, Mingxin Zhang, Jinghe Liu, Xuanyu He, Ran Song, Wei Zhang

Abstract: Re-identification (re-ID) of maritime vessels plays an important role in marine surveillance, but remains highly unexplored due to the lack of large-scale annotated datasets. In vessel re-ID, contrastive methods are supposed to learn discriminative representation from unlabeled vessel images in an unsupervised manner. However, directly introducing classical instance-level contrastive methods to maritime vessel re-ID suffers from the difficulty of finding vessel images with the same pseudo label as positive images, which potentially leads to inefficient training and unsatisfactory performance. This paper proposes a simple but effective method to solve such a hard positive problem. Our method takes all images in an intra-batch cluster as positives and excludes them from the set of negative samples when computing instance-level contrastive loss. Based on this strategy, we construct a multi-level contrastive learning (MCL) framework for vessel re-ID trained with the specifically designed intra-batch cluster-level contrastive loss along with the instance-level one. Experiments on a newly proposed dataset consisting of 1,248 vessel identities show that MCL achieves the state-of-the-art performance compared with other unsupervised methods.