Research map / Label-efficient and robust learning / Recognition and long-tail
Asymmetric Joint Learning for Heterogeneous Face Recognition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add synthesized image pairs to joint Bayesian training with a symmetric model (ablation) | JB+VGG · CUFSF | rank-1 accuracy: 98.43% -> 98.56% | Helped |
| use asymmetric joint learning (inter-class from originals only) instead of symmetric joint Bayesian on synthesized-augmented data (ablation) | JB+VGG+Synthesis Methods · CUFSF | rank-1 accuracy: 98.56% -> 99.61% | Helped |
| use VGG deep features instead of SIFT/HOG/SURF/Gabor (ablation) | AJL-HFR · CUFSF | rank-1 accuracy | Helped |
| choose a few synthesis methods (RSLCR, MWF, GANs) rather than all eight (ablation) | AJL-HFR · CUFSF | rank-1 accuracy | Mixed |
| set PCA feature dimension to 750 (ablation) | AJL-HFR · CUFSF | rank-1 accuracy | Helped |
Closest papers by meaning
- Coupled Deep Learning for Heterogeneous Face Recognition (AAAI 2018 · similarity 0.88)
- Dual Variational Generation for Low Shot Heterogeneous Face Recognition (NeurIPS 2019 · similarity 0.88)
- Residual Compensation Networks for Heterogeneous Face Recognition (AAAI 2019 · similarity 0.86)
- UMFN: Unified Multi-Domain Face Normalization for Joint Cross-domain Prototype Learning and Heterogeneous Face Recognition (CVPR 2025 · similarity 0.84)
- Noisy Correspondence Rectification via Asymmetric Similarity Learning (AAAI 2025 · similarity 0.82)
- UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition (ICLR 2025 · similarity 0.81)
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