From Calibration to Refinement: Seeking Certainty via Probabilistic Evidence Propagation for Noisy-Label Person Re-Identification

TL;DR

The CAlibration-to-REfinement (CARE) method is proposed, a two-stage framework that seeks certainty through probabilistic evidence propagation from calibration to refinement, and the evidence propagation refinement (EPR) that can more accurately distinguish between clean and noisy samples.

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.

Figures (7)

• Figure 1: Illustration of core idea. Left: two challenging instances in feature space, the sample 4 (clean but close to another identity) and the sample 5 (hard sample with occlusion). Right: (a) Original samples with noisy labels; (b) Sample selection methods filter out noisy but informative samples liang2024combatingmalach2017decouplingkarim2022unicon; (c) DistributionNet uses uncertainty to model features, yet it still confuses similar features between clean and noisy labels yu2019robust; (d) Label refinement methods based on softmax may produce the same probabilities for different samples, resulting in incorrectly refurbished labels ye2020purifynetye2022collaborativechen2023refiningzhong2023neighborhood; CARE contains (e) evidential calibration and (f) evidential refinement: (e) calibrates the high evidential instances in the Calibration stage, and (f) will refine the low evidential instances in the Refinement stage. Best viewed in color.
• Figure 2: Illustration of the core problems and our solution under 20% random noise. (a) Top row: conventional softmax-based scoring yields over-confident predictions on corrupted labels, while small-loss selection tends to discard informative but hard positives. (b) Bottom row: Our two-stage CARE framework addresses these problems by first calibrating uncertainty to isolate noise in the Calibration stage, then refining with angular metrics to preserve hard positives through soft weighting in the Refinement stage. Simple, noisy, and hard positive samples are marked as , , and , respectively.
• Figure 3: Illustration of overall CARE framework. Calibration stage: PEC integrates Dirichlet‑informed prediction calibration to break softmax's translation invariance to mitigate over‑confidence. Refinement stage: EPR, powered by the CAM metric, surpasses small-loss methods in distinguishing clean but hard‑to‑learn samples from mislabeled ones; then COSW dynamically reallocates sample importance to prioritize clean instances over noisy instances.
• Figure 4: Ablation results of hyperparameters ($\lambda, \alpha, \beta$) and compute overhead under 20% random noise ratio. Each row shows results on one person Re-ID dataset: Market1501 (top), DukeMTMC-ReID (middle), and CUHK03 (bottom). From left to right, the columns detail: the ablation studies for hyperparameters $\lambda$, $\alpha$, and $\beta$, followed by the compute overhead.
• Figure 5: Ablation results of hyperparameters ($\lambda, \alpha, \beta$) and compute overhead under 20% patterned noise ratio. Each row shows results on one person Re-ID dataset: Market1501 (top), DukeMTMC-ReID (middle), and CUHK03 (bottom). From left to right, the columns detail: the ablation studies for hyperparameters $\lambda$, $\alpha$, and $\beta$, followed by the compute overhead.