  Vehicle Re-Identification (V-ReID) is a critical task that associates the
same vehicle across images from different camera viewpoints. Many works explore
attribute clues to enhance V-ReID; however, there is usually a lack of
effective interaction between the attribute-related modules and final V-ReID
objective. In this work, we propose a new method to efficiently explore
discriminative information from vehicle attributes (e.g., color and type). We
introduce AttributeNet (ANet) that jointly extracts identity-relevant features
and attribute features. We enable the interaction by distilling the
ReID-helpful attribute feature and adding it into the general ReID feature to
increase the discrimination power. Moreover, we propose a constraint, named
Amelioration Constraint (AC), which encourages the feature after adding
attribute features onto the general ReID feature to be more discriminative than
the original general ReID feature. We validate the effectiveness of our
framework on three challenging datasets. Experimental results show that our
method achieves state-of-the-art performance.
