Abstract
In recent years, attention models have been extensively used for person and vehicle re-identification. Most reidentification methods are designed to focus attention at key-point locations. However, depending on the orientation the contribution of each key-point varies. In this paper, we present a novel dual path adaptive attention model for vehicle re-identification (AAVER). The global appearance path captures macroscopic vehicle features while the orientation conditioned part appearance path learns to capture localized discriminative features by focusing attention to the most informative key-points. Through extensive experimentation, we show that the proposed AAVER method is able to accurately re-identify vehicles in unconstrained scenarios, yielding state of the art results on the challenging dataset VeRi-776. As a byproduct, the proposed system is also able to accurately predict vehicle key-points and shows an improvement of more than 7% over state of the art.
Abstract (translated)
近年来，注意力模型被广泛应用于人和车辆的再识别。大多数再鉴定方法都是为了把注意力集中在关键点上。但是，根据方向不同，每个关键点的贡献也不同。本文提出了一种新的车辆识别的双路径自适应注意模型。全局外观路径捕获宏观车辆特征，而定向条件零件外观路径通过将注意力集中到最具信息性的关键点来学习捕获局部识别特征。通过大量的实验，我们证明了所提出的AAVER方法能够在无约束的情况下准确地重新识别车辆，从而在具有挑战性的数据集Veri-776上获得最新的结果。作为副产品，该系统还能够准确预测车辆关键点，比现有技术进步了7%以上。
URL
https://arxiv.org/abs/1905.03397
