Abstract
Unsupervised visible-infrared person re-identification (UVI-ReID) aims to retrieve pedestrian images across different modalities without costly annotations, but faces challenges due to the modality gap and lack of supervision. Existing methods often adopt self-training with clustering-generated pseudo-labels but implicitly assume these labels are always correct. In practice, however, this assumption fails due to inevitable pseudo-label noise, which hinders model learning. To address this, we introduce a new learning paradigm that explicitly considers Pseudo-Label Noise (PLN), characterized by three key challenges: noise overfitting, error accumulation, and noisy cluster correspondence. To this end, we propose a novel Robust Duality Learning framework (RoDE) for UVI-ReID to mitigate the effects of noisy pseudo-labels. First, to combat noise overfitting, a Robust Adaptive Learning mechanism (RAL) is proposed to dynamically emphasize clean samples while down-weighting noisy ones. Second, to alleviate error accumulation-where the model reinforces its own mistakes-RoDE employs dual distinct models that are alternately trained using pseudo-labels from each other, encouraging diversity and preventing collapse. However, this dual-model strategy introduces misalignment between clusters across models and modalities, creating noisy cluster correspondence. To resolve this, we introduce Cluster Consistency Matching (CCM), which aligns clusters across models and modalities by measuring cross-cluster similarity. Extensive experiments on three benchmarks demonstrate the effectiveness of RoDE.
Abstract (translated)
无监督可见光-红外行人再识别（UVI-ReID）的目标是在无需昂贵注释的情况下，在不同模态下检索行人的图像，但面临着由于模态差距和缺乏监督带来的挑战。现有的方法通常采用自训练技术，使用聚类生成的伪标签，但却隐含地假设这些标签总是正确的。然而在实践中，这一假设会因不可避免的伪标签噪声而失败，从而阻碍模型的学习。为了解决这个问题，我们引入了一种新的学习范式，该范式明确考虑了伪标签噪声（PLN），其特征包括三个关键挑战：噪声过拟合、错误积累和噪音聚类对应关系。 为此，我们提出了一种名为鲁棒二元学习框架（RoDE）的新方法来减轻噪声音频伪标签对UVI-ReID的影响。首先，为了对抗噪声过拟合，提出了一个鲁棒自适应学习机制（RAL），该机制可以动态地强调干净样本的同时抑制噪音样本的权重。 其次，为了解决错误积累的问题——即模型强化自身的错误——RoDE采用了两种不同的模型交替训练的方法，每个模型使用另一个模型产生的伪标签进行训练。这种方法鼓励多样性，并防止模型崩溃。然而，这种双模型策略在不同模型和模态之间引入了聚类错配问题，从而产生了噪音的聚类对应关系。 为了解决这一问题，我们提出了聚类一致性匹配（CCM）方法，通过测量跨集群相似性来对齐不同模型和模态之间的聚类。大量的实验显示，在三个基准测试上RoDE的表现优于其他现有技术，证明了其有效性和鲁棒性。
URL
https://arxiv.org/abs/2505.02549
