Abstract
Advanced feature extraction methods have significantly contributed to enhancing the task of person re-identification. In addition, modifications to objective functions have been developed to further improve performance. Nonetheless, selecting better class representatives is an underexplored area of research that can also lead to advancements in re-identification performance. Although past works have experimented with using the centroid of a gallery image class during training, only a few have investigated alternative representations during the retrieval stage. In this paper, we demonstrate that these prior techniques yield suboptimal results in terms of re-identification metrics. To address the re-identification problem, we propose a generalized selection method that involves choosing representations that are not limited to class centroids. Our approach strikes a balance between accuracy and mean average precision, leading to improvements beyond the state of the art. For example, the actual number of representations per class can be adjusted to meet specific application requirements. We apply our methodology on top of multiple re-identification embeddings, and in all cases it substantially improves upon contemporary results
Abstract (translated)
先进的特征提取方法在提升人员重识别任务性能方面发挥了重要作用。此外，针对目标函数的改进也进一步提升了性能表现。然而，选择更好的类别代表是一个尚未充分研究的研究领域，这一领域的探索也可以促进重识别性能的进步。虽然过去的工作中曾尝试在训练阶段使用图像类别的质心作为代表性样本，但在检索阶段探索其他替代表示形式的研究却很少。本文展示了先前的技术手段在人员重识别指标方面表现欠佳。 为了解决重识别问题，我们提出了一种通用的选择方法，该方法不局限于类别质心的选取，而是选择更加多样化的代表以优化性能。我们的方法在准确性和平均精度之间取得了平衡，并超越了现有的技术水平。例如，可以根据具体的应用需求调整每个类别的代表性样本数量。 我们将所提的方法应用于多种重识别嵌入上，在所有情况下均显著提升了当前的最佳结果。
URL
https://arxiv.org/abs/2510.17043
