论文标题
魔鬼在细节中：自我监督的注意力重新识别
The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification
论文作者
论文摘要
近年来，研究界通过基于注意力的模型解决了车辆重新识别（RE-ID）的问题，专门针对包含歧视性信息的车辆区域。这些重新ID方法依赖于昂贵的关键点标签，零件注释以及其他属性，包括车辆制造，型号和颜色。鉴于大量具有不同级别注释的车辆重新ID数据集，强烈监督的方法无法在不同的域上扩展。在本文中，我们提出了自我监督的注意力重新识别（Saver），这是一种有效地学习车辆特异性隔离特征的新方法。通过大规模的实验，我们表明Saver在具有挑战性的VERI，VEARI，车辆1M和Veri-Wild数据集方面有所改善。
In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information. These re-id methods rely on expensive key-point labels, part annotations, and additional attributes including vehicle make, model, and color. Given the large number of vehicle re-id datasets with various levels of annotations, strongly-supervised methods are unable to scale across different domains. In this paper, we present Self-supervised Attention for Vehicle Re-identification (SAVER), a novel approach to effectively learn vehicle-specific discriminative features. Through extensive experimentation, we show that SAVER improves upon the state-of-the-art on challenging VeRi, VehicleID, Vehicle-1M and VERI-Wild datasets.
