Abstract
Object re-identification (Re-ID) methods are highly sensitive to label noise, which typically leads to significant performance degradation. We address this challenge by reframing Re-ID as a supervised image similarity task and adopting a Siamese network architecture trained to capture discriminative pairwise relationships. Central to our approach is a novel statistical outlier detection (OD) framework, termed Beta-SOD (Beta mixture Similarity-based Outlier Detection), which models the distribution of cosine similarities between embedding pairs using a two-component Beta distribution mixture model. We establish a novel identifiability result for mixtures of two Beta distributions, ensuring that our learning task is this http URL proposed OD step complements the Re-ID architecture combining binary cross-entropy, contrastive, and cosine embedding losses that jointly optimize feature-level similarity this http URL demonstrate the effectiveness of Beta-SOD in de-noising and Re-ID tasks for person Re-ID, on CUHK03 and Market-1501 datasets, and vehicle Re-ID, on VeRi-776 dataset. Our method shows superior performance compared to the state-of-the-art methods across various noise levels (10-30\%), demonstrating both robustness and broad applicability in noisy Re-ID scenarios. The implementation of Beta-SOD is available at: this https URL
Abstract (translated)
对象重识别（Re-ID）方法对标签噪声非常敏感，通常会导致性能显著下降。我们通过将Re-ID重构为监督下的图像相似度任务，并采用经过训练以捕捉判别性成对关系的Siamese网络架构来应对这一挑战。我们的方法核心是一种新的统计异常值检测（OD）框架，称为Beta-SOD（基于余弦相似性的贝塔混合异常值检测），该框架使用双组分贝塔分布混合模型来建模嵌入对之间余弦相似度的分布。 我们为两个贝塔分布的混合物建立了新颖的可识别性结果，确保了我们的学习任务具有良好的理论基础。所提出的OD步骤与结合二元交叉熵、对比损失和余弦嵌入损失的Re-ID架构相补充，共同优化特征级相似度。我们在CUHK03和Market-1501数据集上展示了Beta-SOD在人员重识别去噪和Re-ID任务中的有效性，并在VeRi-776数据集上的车辆Re-ID中也展示了相同的效果。 我们的方法在各种噪声水平（10%-30%）下均优于当前最先进的方法，显示了其在嘈杂的Re-ID场景下的鲁棒性和广泛应用性。Beta-SOD的实现可以在以下网址获取：[请在此处插入实际链接]
URL
https://arxiv.org/abs/2509.08926
