PAT++: a cautionary tale about generative visual augmentation for Object Re-identification
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915403979030528 |
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| author | Pereira, Leonardo Santiago Benitez Jeevan, Arathy |
| author_facet | Pereira, Leonardo Santiago Benitez Jeevan, Arathy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15888 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PAT++: a cautionary tale about generative visual augmentation for Object Re-identification Pereira, Leonardo Santiago Benitez Jeevan, Arathy Computer Vision and Pattern Recognition 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. |
| title | PAT++: a cautionary tale about generative visual augmentation for Object Re-identification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.15888 |