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Vehicle Re-identification (ReID) is an important yet challenging problem incomputer vision. Compared to other visual objects like faces and persons,vehicles simultaneously exhibit much larger intraclass viewpoint variations andinterclass visual similarities, making most exiting loss functions designed forface recognition and person ReID unsuitable for vehicle ReID. To obtain ahigh-performance vehicle ReID model, we present a novel Distance Shrinking withAngular Marginalizing (DSAM) loss function to perform hybrid learning in boththe Original Feature Space (OFS) and the Feature Angular Space (FAS) using thelocal verification and the global identification information. Specifically, itshrinks the distance between samples of the same class locally in the OriginalFeature Space while keeps samples of different classes far away in the FeatureAngular Space. The shrinking and marginalizing operations are performed duringeach iteration of the training process and are suitable for different SoftMaxbased loss functions. We evaluate the DSAM loss function on three large vehicleReID datasets with detailed analyses and extensive comparisons with manycompeting vehicle ReID methods. Experimental results show that our DSAM lossenhances the SoftMax loss by a large margin on the PKU-VD1-Large dataset:10.41% for mAP, 5.29% for cmc1, and 4.60% for cmc5. Moreover, the mAP isincreased by 9.34% on the PKU-VehicleID dataset and 6.13% on the VeRi-776dataset. Source code will be released to facilitate further studies in thisresearch direction.

DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio