Domain-generalizable re-identification (DG Re-ID) aims to train a model on one or more source domains and evaluate its performance on unseen target domains, a task that has attracted growing attention due to its practical relevance. While numerous methods have been proposed, most rely on discriminative or contrastive learning frameworks to learn generalizable feature representations. However, these approaches often fail to mitigate shortcut learning, leading to suboptimal performance. In this work, we propose a novel method called diffusion model-assisted representation learning with a correlation-aware conditioning scheme (DCAC) to enhance DG Re-ID. Our method integrates a discriminative and contrastive Re-ID model with a pre-trained diffusion model through a correlation-aware conditioning scheme. By incorporating ID classification probabilities generated from the Re-ID model with a set of learnable ID-wise prompts, the conditioning scheme injects dark knowledge that captures ID correlations to guide the diffusion process. Simultaneously, feedback from the diffusion model is back-propagated through the conditioning scheme to the Re-ID model, effectively improving the generalization capability of Re-ID features. Extensive experiments on both single-source and multi-source DG Re-ID tasks demonstrate that our method achieves state-of-the-art performance. Comprehensive ablation studies further validate the effectiveness of the proposed approach, providing insights into its robustness. Codes will be available at https://github.com/RikoLi/DCAC.

翻译：领域可泛化行人重识别旨在利用一个或多个源域数据训练模型，并在未见过的目标域上评估其性能，这一任务因其实际应用价值而受到日益广泛的关注。尽管已有多种方法被提出，但大多数依赖于判别式或对比学习框架来学习可泛化的特征表示。然而，这些方法往往难以缓解捷径学习问题，导致性能欠佳。在本研究中，我们提出了一种名为"基于相关性感知条件机制的扩散模型辅助表征学习"的新方法，以增强领域可泛化行人重识别性能。该方法通过相关性感知条件机制，将判别式与对比式行人重识别模型与预训练扩散模型相集成。该条件机制通过将行人重识别模型生成的ID分类概率与一组可学习的ID导向提示向量相结合，注入能够捕捉ID相关性的暗知识来引导扩散过程。同时，扩散模型的反馈通过条件机制反向传播至行人重识别模型，有效提升了重识别特征的泛化能力。在单源和多源领域可泛化行人重识别任务上的大量实验表明，本方法取得了最先进的性能。全面的消融研究进一步验证了所提方法的有效性，并揭示了其鲁棒性机制。代码将在https://github.com/RikoLi/DCAC发布。