Abstract
Generative data augmentation has demonstrated gains in several vision tasks, but its impact on object re-identification - where preserving fine-grained visual details is essential - remains largely unexplored. In this work, we assess the effectiveness of identity-preserving image generation for object re-identification. Our novel pipeline, named PAT++, incorporates Diffusion Self-Distillation into the well-established Part-Aware Transformer. Using the Urban Elements ReID Challenge dataset, we conduct extensive experiments with generated images used for both model training and query expansion. Our results show consistent performance degradation, driven by domain shifts and failure to retain identity-defining features. These findings challenge assumptions about the transferability of generative models to fine-grained recognition tasks and expose key limitations in current approaches to visual augmentation for identity-preserving applications.
Abstract (translated)
生成式数据增强在多个视觉任务中展示了增益，但在对象再识别（其中保留细微的视觉细节至关重要）方面的影响仍然很大程度上未被探索。在这项工作中，我们评估了身份保持型图像生成在对象再识别中的有效性。我们的新型流程，名为PAT++，将扩散自我蒸馏集成到了广受认可的部分感知变压器中。利用Urban Elements ReID Challenge数据集，我们在模型训练和查询扩展过程中使用生成的图像进行了广泛的实验。 我们的结果表明，在驱动领域变化的情况下，性能出现了持续下降，并且无法保留定义身份的关键特征。这些发现挑战了关于生成式模型在细粒度识别任务中的可迁移性的假设，并揭示了当前用于身份保持应用的视觉增强方法中的一些关键限制。
URL
https://arxiv.org/abs/2507.15888
