Person re-identification (re-ID) refers to matching people across multiple camera views at different times and locations. The challenge is mainly about the huge variance of visual appearance of a specific pedestrian owing to pose variations, illumination changes and various camera-styles. In this paper, an Attention-Aware Scoring Learning (AASL) framework is proposed to address these issues. The proposed AASL framework consists of two attention modules and a score learning head. Specifically, the two modules, Spatial Attention Grid and Channel Attention Grid, embedded respectively in the shallow and deep layer in the convolutional neural network, are put forward to help the network learn the most discriminative visual features. Furthermore, an adaptive module termed score learning head is proposed to optimize the parameters of the attention modules. The present paper carries out extensive experiments on three large-scale datasets, including Market-1501, DukeMTMC-reID and CUHK03, after which it is found that our Attention-Aware Scoring Learning framework significantly outperforms the baseline model and achieves a competitive performance compared with the state-of-the-art person re-ID methods.

人员重新识别（re-ID）是指在不同时间和位置在多个摄像机视图中匹配人员。挑战主要是由于姿势变化，照明变化和各种相机样式而导致的特定行人视觉外观的巨大差异。在本文中，提出了一种注意力感知评分学习（AASL）框架来解决这些问题。拟议的AASL框架包括两个注意模块和一个分数学习主管。具体来说，提出了分别嵌入到卷积神经网络的浅层和深层的两个模块，即空间注意力网格和通道注意力网格，以帮助网络学习最具判别力的视觉特征。此外，提出了一种称为分数学习头的自适应模块，以优化注意力模块的参数。本文在Market-1501，DukeMTMC-reID和CUHK03等三个大型数据集上进行了广泛的实验，之后发现我们的“注意感知评分学习”框架明显优于基线模型，并且与之相比具有竞争优势。最新的人员re-ID方法。