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About this paper
Wenbo Dai, Lijing Lu, Zhihang Li
DiVE, a framework developed by researchers at Nanjing Tech University, Peking University, and the Chinese Academy of Sciences, generates identity-preserving synthetic visible-infrared person images by leveraging a pre-trained diffusion model to decouple identity from modality information, thereby enhancing the performance of various state-of-the-art VI-ReID models.
Key insights
- Effective decoupling of identity and modality within a diffusion model's latent space is crucial for generating high-fidelity, identity-preserving cross-modal person images.
- Incorporating fine-grained modality and view identifiers during training significantly enhances the model's ability to learn precise contextual and modality information.
- Pre-trained diffusion models can be effectively adapted to address data scarcity in challenging, fine-grained computer vision tasks like VI-ReID.
Summary by alphaXiv.
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