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 (for instance, 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 the state-of-the-art performance.

车辆重新识别 (V-ReID) 是一项关键任务，可将来自不同相机视点的图像中的同一辆车关联起来。许多作品探索了属性线索来增强V-ReID；然而，属性相关模块和最终 V-ReID 目标之间通常缺乏有效的交互。在这项工作中，我们提出了一种有效探索车辆属性（例如，颜色和类型）的判别信息的新方法。我们引入了联合提取身份相关特征和属性特征的 AttributeNet (ANet)。我们通过提炼 ReID 有用的属性特征并将其添加到通用 ReID 特征中以增加区分能力来启用交互。此外，我们提出了一个约束，称为改善约束（AC），这鼓励在通用 ReID 特征上添加属性特征后的特征比原始通用 ReID 特征更具辨别力。我们在三个具有挑战性的数据集上验证了我们的框架的有效性。实验结果表明，我们的方法达到了最先进的性能。