Abstract
Person re-identification (Re-ID) aims to match the same pedestrian in a large gallery with different cameras and views. Enhancing the robustness of the extracted feature representations is a main challenge in Re-ID. Existing methods usually improve feature representation by improving model architecture, but most methods ignore the potential contextual information, which limits the effectiveness of feature representation and retrieval performance. Neighborhood information, especially the potential information of multi-order neighborhoods, can effectively enrich feature expression and improve retrieval accuracy, but this has not been fully explored in existing research. Therefore, we propose a novel model DMON-ARO that leverages latent neighborhood information to enhance both feature representation and index performance. Our approach is built on two complementary modules: Dynamic Multi-Order Neighbor Modeling (DMON) and Asymmetric Relationship Optimization (ARO). The DMON module dynamically aggregates multi-order neighbor relationships, allowing it to capture richer contextual information and enhance feature representation through adaptive neighborhood modeling. Meanwhile, ARO refines the distance matrix by optimizing query-to-gallery relationships, improving the index accuracy. Extensive experiments on three benchmark datasets demonstrate that our approach achieves performance improvements against baseline models, which illustrate the effectiveness of our model. Specifically, our model demonstrates improvements in Rank-1 accuracy and mAP. Moreover, this method can also be directly extended to other re-identification tasks.
Abstract (translated)
人再识别（Re-ID）的目标是在不同摄像头和视角的大图库中匹配同一行人。提高提取特征表示的鲁棒性是Re-ID中的主要挑战之一。现有方法通常通过改进模型架构来提升特征表示，但大多数方法忽视了潜在的上下文信息，这限制了特征表示的有效性和检索性能。邻域信息特别是多阶邻居的潜在信息能够有效丰富特征表达并提高检索精度，但在现有研究中这一潜力尚未得到充分挖掘。 因此，我们提出了一种新型模型DMON-ARO（Dynamic Multi-Order Neighbor Modeling and Asymmetric Relationship Optimization），该模型利用隐式邻域信息来增强特征表示和索引性能。我们的方法基于两个互补模块：动态多阶邻居建模（DMON）和非对称关系优化（ARO）。DMON模块通过自适应地聚合多阶邻居关系，能够捕捉更丰富的上下文信息，并通过灵活的邻居建模增强特征表示能力。同时，ARO模块通过对查询到图库的关系进行优化来精炼距离矩阵，进而提高索引准确性。 在三个基准数据集上的广泛实验表明，我们的方法相比基线模型取得了性能改进，这说明了我们模型的有效性。具体而言，我们在Rank-1准确率和mAP方面均有提升。此外，该方法也可以直接扩展到其他再识别任务中。
URL
https://arxiv.org/abs/2504.11798
