Consistency-driven feature scoring and regularization network for visible–infrared person re-identification

Recently, visible–infrared person re-identification (VI-ReID) has received considerable attention due to its practical importance. A number of methods extract multiple local features to enrich the diversity of feature representations. However, some local features often involve modality-relevant information, leading to deteriorated performance. Moreover, existing methods optimize the models by only considering the samples at each batch while ignoring the learned features at previous iterations. As a result, the features of the same person images drastically change at different training epochs, hindering the training stability. To alleviate the above issues, we propose a novel consistency-driven feature scoring and regularization network (CFSR-Net), which consists of a backbone network, a local feature learning block, a feature scoring block, and a global–local feature fusion block, for VI-ReID. On the one hand, we design a cross-modality consistency loss to highlight modality-irrelevant local features and suppress modality-relevant local features for each modality, facilitating the generation of a reliable compact local feature. On the other hand, we develop a feature consistency regularization strategy (including a momentum class contrastive loss and a momentum distillation loss) to impose consistency regularization on the learning of different levels of features by considering the learned features at historical epochs. This effectively enables smooth feature changes and thus improves the training stability. Extensive experiments on public VI-ReID datasets clearly show the effectiveness of our method against several state-of-the-art VI-ReID methods. Code will be released at https://github.com/cxtjl/CFSR-Net.