GiT: Graph Interactive Transformer for Vehicle Re-identification

Understand

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.