Abstract
To reduce the reliance of visible-infrared person re-identification (ReID) models on labeled cross-modal samples, this paper explores a weakly supervised cross-modal person ReID method that uses only single-modal sample identity labels, addressing scenarios where cross-modal identity labels are unavailable. To mitigate the impact of missing cross-modal labels on model performance, we propose a heterogeneous expert collaborative consistency learning framework, designed to establish robust cross-modal identity correspondences in a weakly supervised manner. This framework leverages labeled data from each modality to independently train dedicated classification experts. To associate cross-modal samples, these classification experts act as heterogeneous predictors, predicting the identities of samples from the other modality. To improve prediction accuracy, we design a cross-modal relationship fusion mechanism that effectively integrates predictions from different experts. Under the implicit supervision provided by cross-modal identity correspondences, collaborative and consistent learning among the experts is encouraged, significantly enhancing the model's ability to extract modality-invariant features and improve cross-modal identity recognition. Experimental results on two challenging datasets validate the effectiveness of the proposed method.
Abstract (translated)
为了减少可见光-红外行人重识别（ReID）模型对标注的跨模态样本的依赖，本文探索了一种仅使用单一模态样本身份标签的弱监督跨模态行人ReID方法，以应对跨模态身份标签不可用的情况。为减轻缺少跨模态标签对模型性能的影响，我们提出了一种异构专家协作一致性学习框架，旨在通过一种弱监督方式建立稳健的跨模态身份对应关系。该框架利用每个模态上的标注数据独立训练专用分类专家。为了关联跨模态样本，这些分类专家作为异构预测器，预测来自另一模态样本的身份信息。为提高预测准确性，我们设计了一种跨模态关系融合机制，有效整合不同专家的预测结果。在隐式提供的跨模态身份对应监督下，鼓励专家之间进行协作和一致学习，显著增强了模型提取模态不变特征的能力，并提高了跨模态身份识别性能。实验结果显示，在两个具有挑战性的数据集上验证了所提出方法的有效性。
URL
https://arxiv.org/abs/2507.12942
