Vehicle re-identification (vehicle ReID) is designed to recognize all instances of a specific vehicle across various camera viewpoints, facing significant challenges such as high similarity among different vehicles from the same viewpoint and substantial variance for the same vehicle across different viewpoints. In this paper, we introduce the RAND network, which is equipped with relation attention mechanisms, nuance, and disparity masks to tackle these issues effectively. The disparity mask specifically targets the automatic suppression of irrelevant foreground and background noise, while the nuance mask reveals less obvious, sub-discriminative regions to enhance the overall feature robustness. Additionally, our relation attention module, which incorporates an advanced transformer architecture, significantly reduces intra-class distances, thereby improving the accuracy of vehicle identification across diverse viewpoints. The performance of our approach has been thoroughly evaluated on widely recognized datasets such as VeRi-776 and VehicleID, where it demonstrates superior effectiveness and competes robustly with other leading methods.

RANDnet: Vehicle Re-Identification with Relation Attention and Nuance–Disparity Masks