Despite the remarkable progress in recent years, person Re-Identification (ReID) approaches frequently fail in cases where the semantic body parts are misaligned between the detected human boxes. To mitigate such cases, we propose a novel High-Order ReID (HOReID) framework that enables semantic pose alignment by aggregating the fine-grained part details of multilevel feature maps. The HOReID adopts a high-order mapping of multilevel feature similarities in order to emphasize the differences of the similarities between aligned and misaligned part pairs in two person images. Since the similarities of misaligned part pairs are reduced, the HOReID enhances pose-robustness within the learned features. We show that our method derives from an intuitive and interpretable motivation and elegantly reduces the misalignment problem without using any prior knowledge from human pose annotations or pose estimation networks. This paper theoretically and experimentally demonstrates the effectiveness of the proposed HOReID, achieving superior performance over the state-of-the-art methods on the four large-scale person ReID datasets.

中文翻译：

---

HOReID：深度高阶映射增强了人员重新识别的姿势对齐


尽管近年来取得了显着的进展，但在检测到的人体框之间语义身体部分未对齐的情况下，人员重新识别（ReID）方法经常会失败。为了缓解这种情况，我们提出了一种新颖的高阶 ReID (HOReID) 框架，该框架通过聚合多级特征图的细粒度部分细节来实现语义姿态对齐。 HOReID 采用多级特征相似性的高阶映射，以强调两个人图像中对齐和未对齐部分对之间的相似性差异。由于未对齐部分对的相似性减少，HOReID 增强了学习特征内的姿势鲁棒性。我们表明，我们的方法源于直观且可解释的动机，并且在不使用来自人体姿势注释或姿势估计网络的任何先验知识的情况下优雅地减少了错位问题。本文从理论上和实验上证明了所提出的 HOReID 的有效性，在四个大规模行人 ReID 数据集上实现了优于最先进方法的性能。