Abstract
In person re-identification, re-ranking is a crucial step to enhance the overall accuracy by refining the initial ranking of retrieved results. Previous studies have mainly focused on features from single-view images, which can cause view bias and issues like pose variation, viewpoint changes, and occlusions. Using multi-view features to present a person can help reduce view bias. In this work, we present an efficient re-ranking method that generates multi-view features by aggregating neighbors' features using K-nearest Weighted Fusion (KWF) method. Specifically, we hypothesize that features extracted from re-identification models are highly similar when representing the same identity. Thus, we select K neighboring features in an unsupervised manner to generate multi-view features. Additionally, this study explores the weight selection strategies during feature aggregation, allowing us to identify an effective strategy. Our re-ranking approach does not require model fine-tuning or extra annotations, making it applicable to large-scale datasets. We evaluate our method on the person re-identification datasets Market1501, MSMT17, and Occluded-DukeMTMC. The results show that our method significantly improves Rank@1 and mAP when re-ranking the top M candidates from the initial ranking results. Specifically, compared to the initial results, our re-ranking method achieves improvements of 9.8%/22.0% in Rank@1 on the challenging datasets: MSMT17 and Occluded-DukeMTMC, respectively. Furthermore, our approach demonstrates substantial enhancements in computational efficiency compared to other re-ranking methods.
Abstract (translated)
在人员再识别中，重排名是一个关键步骤，通过优化检索结果的初始排序来提高整体准确性。以往的研究主要集中在单一视角图像特征上，这可能导致视角偏差以及姿态变化、视点改变和遮挡等问题。利用多视角特征表示一个人有助于减少视角偏差。在这项工作中，我们提出了一种高效的重排名方法，该方法通过使用K最近邻加权融合(KWF)法聚合邻居的特征来生成多视角特征。具体而言，我们假设从再识别模型中提取的同一种身份的特征高度相似。因此，我们在无监督的方式下选择K个相邻的特征以生成多视角特征。此外，这项研究还探讨了在特征聚合过程中权重的选择策略，使我们可以确定有效的策略。我们的重排名方法不需要对模型进行微调或添加额外注释，使其适用于大规模数据集。我们在Market1501、MSMT17和Occluded-DukeMTMC等人员再识别数据集中评估了该方法。结果表明，在初始排序的前M名候选人中重新排列时，我们的方法在Rank@1和mAP上显著提升。具体来说，与初始结果相比，我们提出的重排名方法分别在具有挑战性的MSMT17和Occluded-DukeMTMC数据集上实现了9.8%和22.0%的Rank@1改进。此外，我们的方法展示了比其他重排名方法更高的计算效率。
URL
https://arxiv.org/abs/2509.04050
