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Found 8 papers, 4 papers with code
Spherical Confidence Learning for Face Recognition
Probabilistic Face Embeddings (PFE) is the first attempt to address this dilemma.
Consistent Instance False Positive Improves Fairness in Face Recognition
Then, an additional penalty term, which is in proportion to the ratio of instance FPR overall FPR, is introduced into the denominator of the softmax-based loss.
Federated Face Recognition
Face recognition has been extensively studied in computer vision and artificial intelligence communities in recent years.
Hypersphere Face Uncertainty Learning
To address these issues, in this paper, we propose a novel framework for face uncertainty learning in hyperspherical space.
Wasserstein Coupled Graph Learning for Cross-Modal Retrieval
Then, a Wasserstein coupled dictionary, containing multiple pairs of counterpart graph keys with each key corresponding to one modality, is constructed for further feature learning.
Scribble-Supervised Semantic Segmentation Inference
In this paper, we propose a progressive segmentation inference (PSI) framework to tackle with scribble-supervised semantic segmentation.
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability.
Ranked #13 on
Face Verification
on IJB-C
(TAR @ FAR=1e-4 metric)
Improving Face Recognition from Hard Samples via Distribution Distillation Loss
To improve the performance on those hard samples for general tasks, we propose a novel Distribution Distillation Loss to narrow the performance gap between easy and hard samples, which is a simple, effective and generic for various types of facial variations.
