ViT-ReID: A Vehicle Re-identification Method Using Visual Transformer

In the current vehicle re-identification task based on CNN method, due to the loss of detail information of image caused by convolution and downsampling operation, the ability to distinguish similar vehicles is affected. In this paper, a new vehicle Re-ID method ViT-ReID using visual Transformer was proposed. In order to extract robust discriminative features to enhance the ability of the model to distinguish similar vehicles, a SIE module based on ViT backbone network was designed to effectively reduce the deviation of learning features caused by camera perspective changes. At the same time, in order to improve the performance of the model, the latest VPT method for fine-tuning of large-scale Transformers was introduced. Finally, the ViT-ReID model proposed in this paper was compared with other methods in the VeRi-776 and VehicleID datasets. The experimental results showed that the proposed method can effectively extract robust discriminative features in vehicle re-identification and distinguish similar vehicles with high accuracy.