Global reference attention network for vehicle re-identification.

Vehicle re-identification (Re-ID) aims to find the image of the same vehicle in different cameras. One of the reasons that this task remains challenging is that different vehicles of the same type and color look very similar in appearance. In recent years, attention mechanisms have been widely used in vehicle Re-ID, including some mechanisms that use the relationships between nodes to infer attention. However, such methods are vulnerable to interference by some noise information in the image, especially partial information from other vehicles. For this reason, we propose in this paper a global reference attention mechanism for attention learning by utilizing the relationships between nodes and the global reference node, where the global reference node is built by all nodes in the image. At the same time, we propose a Global Reference Attention Network (GRA-Net) based on the mechanism to mine a large number of discriminative features useful for vehicle re-identification, thus easing the difficulty of distinguishing between different vehicles of similar appearance. Specifically, to extract more discriminative features, we adopt a multi-branch neural network and embed different global reference attention modules in each branch to compose our GRA-Net. Extensive experiments valiyear the effectiveness of GRA-Net and show that our approach achieves state-of-the-art performance on two massive vehicle Re-ID datasets. [ABSTRACT FROM AUTHOR]