Translation, Association and Augmentation: Learning Cross-Modality Re-Identification From Single-Modality Annotation

Daytime visible modality (RGB) and night-time infrared (IR) modality person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. However, training a cross-modality ReID model requires plenty of cross-modality (visible-infrared) identity labels that are more expensive than single-modality person ReID. To alleviate this issue, this paper studies unsupervised domain adaptive visible infrared person re-identification (UDA-VI-ReID) task without the reliance on any cross-modality annotation. To transfer learned knowledge from the labelled visible source domain to the unlabelled visible-infrared target domain, we propose a Translation, Association and Augmentation (TAA) framework. Specifically, the modality translator is firstly utilized to transfer visible image to infrared image, formulating generated visible-infrared image pairs for cross-modality supervised training. A Robust Association and Mutual Learning (RAML) module is then designed to exploit the underlying relations between visible and infrared modalities for label noise modeling. Moreover, a Translation Supervision and Feature Augmentation (TSFA) module is designed to enhance the discriminability by enriching the supervision with feature augmentation and modality translation. The extensive experimental results demonstrate that our method significantly outperforms current state-of-the-art unsupervised methods under various settings, and even surpasses some supervised counterparts, providing a powerful baseline for UDA-VI-ReID.