  Transformers are more and more popular in computer vision, which treat an
image as a sequence of patches and learn robust global features from the
sequence. However, pure transformers are not entirely suitable for vehicle
re-identification because vehicle re-identification requires both robust global
features and discriminative local features. For that, a graph interactive
transformer (GiT) is proposed in this paper. In the macro view, a list of GiT
blocks are stacked to build a vehicle re-identification model, in where graphs
are to extract discriminative local features within patches and transformers
are to extract robust global features among patches. In the micro view, graphs
and transformers are in an interactive status, bringing effective cooperation
between local and global features. Specifically, one current graph is embedded
after the former level's graph and transformer, while the current transform is
embedded after the current graph and the former level's transformer. In
addition to the interaction between graphs and transforms, the graph is a
newly-designed local correction graph, which learns discriminative local
features within a patch by exploring nodes' relationships. Extensive
experiments on three large-scale vehicle re-identification datasets demonstrate
that our GiT method is superior to state-of-the-art vehicle re-identification
approaches.
