Find Hidden Modality Divergence: Adversarial Aware Learning for Unsupervised Visible–Infrared Person Re-Identification

Unsupervised visible-infrared person re-identifi-cation (Unsupervised VI-ReID) aims to learn discriminative identity features under the large modality gap without any labeled data. Currently, the state-of-the-art methods optimize cross-modality differences by using contrastive learning as the underlying paradigm. However, they neglect the problem of modality divergence during the cross-modality optimization process. This problem means that the interclass instances between the cross-modality intraclass gaps can make cross-modality intraclass instances difficult to get closer to each other in the feature space due to the effect of contrastive learning on these interclass instances. To alleviate the negative impact of the modality divergence problem, we propose an adversarial aware learning (ADAL) framework to explore the instances that generate modal divergence and adversarially optimize these explored instances. Specifically, on the one hand, we explore the optimization directions of each cluster during the cross-modality optimization process, and the cluster centroids generating positive optimization are facilitated, while the others generating negative optimization are penalized. On the other hand, we further consider the instance-level optimization process, which increases the affinities of the positive instance pairs with large cross-modality gaps to further improve the centroid-level optimization. Extensive experiments conducted on the visible-infrared person Re-ID datasets show that the proposed method is used as a universally applicable plug-in module to add the existing unsupervised VI-ReID methods, which outperforms the existing state-of-the-art approaches.