Research map / Label-efficient and robust learning / Domain adaptation and generalization
Regularized Fine-Grained Meta Face Anti-Spoofing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| apply Regularized Fine-Grained Meta-learning instead of MADDG | face anti-spoofing · O&C&I to M | HTER (%): 17.69 -> 13.89 | Helped |
| apply Regularized Fine-Grained Meta-learning instead of MADDG | face anti-spoofing · O&M&I to C | HTER (%): 24.5 -> 20.27 | Helped |
| apply Regularized Fine-Grained Meta-learning instead of MADDG | face anti-spoofing · O&C&M to I | HTER (%): 22.19 -> 17.3 | Helped |
| apply Regularized Fine-Grained Meta-learning instead of MADDG | face anti-spoofing · I&C&M to O | HTER (%): 27.98 -> 16.45 | Helped |
| use simultaneous fine-grained domain-pair meta-learning rather than one aggregated pair (ablation) | Regularized Fine-Grained Meta-learning · O&M&I to C | HTER (%): 24.28 -> 20.27 | Helped |
| use simultaneous fine-grained domain-pair meta-learning rather than one aggregated pair (ablation) | Regularized Fine-Grained Meta-learning · O&C&I to M | HTER (%): 14.54 -> 13.89 | Helped |
| use simultaneous fine-grained domain-pair meta-learning rather than one aggregated pair (ablation) | Regularized Fine-Grained Meta-learning · O&C&M to I | HTER (%): 20.07 -> 17.3 | Helped |
| use simultaneous fine-grained domain-pair meta-learning rather than one aggregated pair (ablation) | Regularized Fine-Grained Meta-learning · I&C&M to O | HTER (%): 17.94 -> 16.45 | Helped |
| retain second-order derivative information instead of using a first-order approximation (ablation) | Regularized Fine-Grained Meta-learning · O&M&I to C | HTER (%): 27.47 -> 20.27 | Helped |
| retain second-order derivative information instead of using a first-order approximation (ablation) | Regularized Fine-Grained Meta-learning · O&C&M to I | HTER (%): 26.24 -> 17.3 | Helped |
| add meta-learning (ablation) | Regularized Fine-Grained Meta-learning · O&M&I to C | face anti-spoofing performance | Helped |
| add face-depth domain-knowledge regularization (ablation) | Regularized Fine-Grained Meta-learning · O&M&I to C | face anti-spoofing performance | Helped |
Closest papers by meaning
- Test-Time Domain Generalization for Face Anti-Spoofing (CVPR 2024 · similarity 0.89)
- Gradient Alignment for Cross-Domain Face Anti-Spoofing (CVPR 2024 · similarity 0.89)
- FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models (AAAI 2026 · similarity 0.88)
- CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing (CVPR 2024 · similarity 0.88)
- From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing (CVPR 2026 · similarity 0.87)
- Rethinking Generalizable Face Anti-spoofing via Hierarchical Prototype-guided Distribution Refinement in Hyperbolic Space (CVPR 2024 · similarity 0.87)
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