  Recently, deep learning has been widely used in the field of vehicle
re-identification. When training a deep model, softmax loss is usually used as
a supervision tool. However, the softmax loss performs well for closed-set
tasks, but not very well for open-set tasks. In this paper, we sum up five
shortcomings of center loss and solved all of them by proposing a dual distance
center loss (DDCL). Especially we solve the shortcoming that center loss must
combine with the softmax loss to supervise training the model, which provides
us with a new perspective to examine the center loss. In addition, we verify
the inconsistency between the proposed DDCL and softmax loss in the feature
space, which makes the center loss no longer be limited by the softmax loss in
the feature space after removing the softmax loss. To be specifically, we add
the Pearson distance on the basis of the Euclidean distance to the same center,
which makes all features of the same class be confined to the intersection of a
hypersphere and a hypercube in the feature space. The proposed Pearson distance
strengthens the intra-class compactness of the center loss and enhances the
generalization ability of center loss. Moreover, by designing a Euclidean
distance threshold between all center pairs, which not only strengthens the
inter-class separability of center loss, but also makes the center loss (or
DDCL) works well without the combination of softmax loss. We apply DDCL in the
field of vehicle re-identification named VeRi-776 dataset and VehicleID
dataset. And in order to verify its good generalization ability, we also verify
it in two datasets commonly used in the field of person re-identification named
MSMT17 dataset and Market1501 dataset.
