Research map / Image generation / Editing and style transfer
Dual Variational Generation for Low Shot Heterogeneous Face Recognition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add distribution alignment loss (ablation) | DVG · CASIA NIR-VIS 2.0 | Rank-1: 94.3 -> 99.2 | Helped |
| Add pairwise identity preserving loss (ablation) | DVG · CASIA NIR-VIS 2.0 | Rank-1: 96.1 -> 99.2 | Helped |
| Add diversity loss (ablation) | DVG · CASIA NIR-VIS 2.0 | Rank-1: 98.5 -> 99.2 | Helped |
| Add DVG-generated paired heterogeneous images | LightCNN-9 · Oulu-CASIA NIR-VIS | VR@FAR=0.1%: 43.8 -> 89.5 | Helped |
| Increase generated training pairs from 1K to 50K | DVG with LightCNN-9 · Oulu-CASIA NIR-VIS | VR@FAR=0.1%: 68.7%, 85.9%, 89.5% and 89.4% | Mixed |
Closest papers by meaning
- Asymmetric Joint Learning for Heterogeneous Face Recognition (AAAI 2018 · similarity 0.88)
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- UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition (ICLR 2025 · similarity 0.84)
- HyperFace: Generating Synthetic Face Recognition Datasets by Exploring Face Embedding Hypersphere (ICLR 2025 · similarity 0.84)
- GIF: Generative Inspiration for Face Recognition at Scale (CVPR 2025 · similarity 0.83)
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