Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism.

翻译：这些深层结构越来越多地包括更多的组成部分,这些组成部分利用部分检测、属性预测、显示估计数据和其他辅助信息,以便更有效地定位和调整歧视性图像区域。在本文件中,我们采取了不同的方法,仔细设计了简单的深层结构的每个组成部分,并严格地设计了有效进行人员再识别培训的战略。我们广泛评价了每个设计选择,从而得出了个人再识别的良好做法清单。通过采用这些做法,我们的方法在四个基准数据集中大大优于最新水平,包括使用更复杂的辅助部件的方法。我们还对经过培训的代表进行了质量分析,表明虽然内容紧凑,它能够以类似于隐含的关注机制的方式从局部和歧视性区域获取信息。