Research map / Label-efficient and robust learning / Recognition and long-tail
Detecting Masked Faces in the Wild With LLE-CNNs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LLE-CNNs with similarity-based embedding from representative face and non-face dictionaries | Masked face detection · MAFA test (All) | AP (%): 60.8 -> 76.4 | Helped |
| Use LLE-CNNs instead of the strongest listed baseline | Masked face detection · MAFA test (Heavy occlusion) | AP (%): 7.12 -> 22.5 | Helped |
| Use LLE-CNNs instead of the strongest listed baseline | Masked face detection · MAFA test (Left) | AP (%): 7.91 -> 17.2 | Helped |
| Use LLE-CNNs instead of the strongest listed baseline | Masked face detection · MAFA test (Right) | AP (%): 5.43 -> 14.3 | Helped |
| Use LLE-CNNs instead of the strongest listed baseline | Masked face detection · MAFA test (Hybrid mask) | AP (%): 7.64 -> 24.2 | Helped |
| Use LLE-CNNs instead of the strongest listed baseline | Masked face detection · MAFA test (Front) | AP (%): 64.4 -> 79.6 | Helped |
Closest papers by meaning
- Masked Face Recognition with Generative-to-Discriminative Representations (ICML 2024 · similarity 0.88)
- FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning (CVPR 2025 · similarity 0.86)
- Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors (ICLR 2025 · similarity 0.84)
- FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models (AAAI 2026 · similarity 0.84)
- Guard Me If You Know Me: Protecting Specific Face-Identity from Deepfakes (NeurIPS 2025 · similarity 0.84)
- From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes (NeurIPS 2025 · similarity 0.84)
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