Multimodality Adaptive Transformer and Mutual Learning for Unsupervised Domain Adaptation Vehicle Re-Identification

Unsupervised Domain Adaptation Vehicle Re-Identification (UDA vehicle re-ID) aims to enable the model trained in the source domain dataset to adapt to the target domain data and obtain accurate re-identification results, which has received widespread attention due to its practicality in the field of intelligent transportation systems. Most current UDA vehicle re-ID research ignores the mining and utilization of attribute information. Meanwhile, the Convolutional Neural Networks-based (CNN-based) network will cause the loss of fine-grained information, reducing the expression and generalization ability of vehicle features. To alleviate such issues, we are motivated by the Transformer, which can exploit distinguishable attribute information and fuse multimodal features effectively. Therefore, this paper proposes a Multimodality Adaptive Transformer Network (MATNet) to intensify the ability to learn vehicle fine-grained features related to attributes. Moreover, the noise contained in pseudo-labels assigned by cluster algorithms interferes with the performance of the UDA vehicle re-ID method. We also design the Dual Mutual Dynamic Update Pseudo-Label generation strategy (DMDU) to improve the accuracy of pseudo-labels and alleviate error accumulation. The strategy is based on mutual learning, which can effectively utilize the congruous and particular knowledge of the two models to generate pseudo-labels. Extensive experiments on two large-scale public datasets, including VeRi-776 and VehicleID, illustrate that our method outperforms the state-of-the-art methods.