Abstract
With the increasing demand for robust person Re-ID in unconstrained environments, learning from datasets with noisy labels and sparse per-identity samples remains a critical challenge. Existing noise-robust person Re-ID methods primarily rely on loss-correction or sample-selection strategies using softmax outputs. However, these methods suffer from two key limitations: 1) Softmax exhibits translation invariance, leading to over-confident and unreliable predictions on corrupted labels. 2) Conventional sample selection based on small-loss criteria often discards valuable hard positives that are crucial for learning discriminative features. To overcome these issues, we propose the CAlibration-to-REfinement (CARE) method, a two-stage framework that seeks certainty through probabilistic evidence propagation from calibration to refinement. In the calibration stage, we propose the probabilistic evidence calibration (PEC) that dismantles softmax translation invariance by injecting adaptive learnable parameters into the similarity function, and employs an evidential calibration loss to mitigate overconfidence on mislabeled samples. In the refinement stage, we design the evidence propagation refinement (EPR) that can more accurately distinguish between clean and noisy samples. Specifically, the EPR contains two steps: Firstly, the composite angular margin (CAM) metric is proposed to precisely distinguish clean but hard-to-learn positive samples from mislabeled ones in a hyperspherical space; Secondly, the certainty-oriented sphere weighting (COSW) is developed to dynamically allocate the importance of samples according to CAM, ensuring clean instances drive model updates. Extensive experimental results on Market1501, DukeMTMC-ReID, and CUHK03 datasets under both random and patterned noises show that CARE achieves competitive performance.
Abstract (translated)
随着对未约束环境中鲁棒人物重识别（Re-ID）需求的增加，从带有噪声标签和稀疏身份样本的数据集中学习仍然是一个关键挑战。现有的抗噪人物重识别方法主要依赖于利用softmax输出进行损失校正或样本选择策略。然而，这些方法存在两个关键限制：1) softmax具有平移不变性，在面对损坏的标签时会导致过于自信且不可靠的预测；2) 基于小损失标准的传统样本选择往往排除了对学习鉴别特征至关重要的有价值的困难正例。 为克服这些问题，我们提出了CAlibration-to-REfinement（CARE）方法，这是一种两阶段框架，通过校准到细化过程中的概率证据传播寻求确定性。在校准阶段，我们提出了一种基于注入适应性可学习参数分解softmax平移不变性的概率证据校正（PEC），并通过使用证据校准损失来减轻对错误标签样本的过度自信问题。在细化阶段，我们设计了证据传播细化（EPR）方法，能够更准确地区分干净和噪声样本。 具体而言，EPR包含两个步骤：首先，提出了一种复合角度余量（CAM）度量，在高维球形空间中精确区分难以学习的正例与误标样例；其次，开发了以确定性为中心的球体加权（COSW），根据CAM动态分配样本的重要性，确保干净实例驱动模型更新。 在Market1501、DukeMTMC-ReID和CUHK03数据集上进行的大量实验表明，在随机噪声和模式化噪声环境下，CARE方法实现了具有竞争力的表现。
URL
https://arxiv.org/abs/2602.23133
