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Convolutional neural network (CNN) based vehicle reidentificatioin (ReID) inevitably has many disadvantages, such as information loss caused by downsampling operation. Therefore we propose a vision transformer (Vit) based vehicle ReID method to solve this problem. To improve the feature representation of vision transformer and make full use of additional vehicle information, the following methods are presented. (I) We propose a Quadratic Split Architecture (QSA) to learn both global and localdoi:10.1587/transfun.2022eal2008 fatcat:hf7uyck3zngzbl6lw743yhxlcy