Abstract
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 this https URL.
Abstract (translated)
领域通用重识别（DG Re-ID）的目标是在一个或多个源域上训练模型，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的关联性而越来越受到关注。虽然已经提出了许多方法，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，这些方法往往未能缓解捷径学习问题（shortcut learning），导致性能不佳。在这项工作中，我们提出了一种新的方法——基于扩散模型辅助的表征学习与相关感知条件方案（DCAC）来增强DG Re-ID的效果。我们的方法通过一个相关感知条件方案将判别式和对比式的Re-ID模型与预训练的扩散模型相结合。该条件方案通过使用从Re-ID模型生成的身份分类概率以及一组可学习的身份提示，注入了捕捉身份之间相关性的暗知识（dark knowledge），以指导扩散过程。同时，来自扩散模型的反馈被通过条件方案反向传播到Re-ID模型中，有效提升了重识别特征的泛化能力。在单一源域和多源域DG Re-ID任务上的大量实验表明，我们的方法达到了最先进的性能表现。全面的消融研究进一步验证了所提方法的有效性，并提供了对其鲁棒性的见解。代码将在该链接提供。 这段描述详细介绍了针对领域通用重识别（Domain-Generalizable Re-Identification）问题的新方法——DCAC（Diffusion Model-Assisted Representation Learning with Correlation-Aware Conditioning），展示了该方法如何通过结合判别式/对比学习模型和预训练的扩散模型来提升特征表示的泛化能力，并在不同实验设置中实现了卓越性能。
URL
https://arxiv.org/abs/2502.06619
