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20202022
most citedDive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition

13 citations · 14 across the 3 of their papers we have counts for

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cs.CV2022

Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation

Hailin Shi, Hang Du, Yibo Hu +3

Human face images usually appear with wide range of visual scales. The existing face representations pursue the bandwidth of handling scale variation via multi-scale scheme that as…

cs.CV2021

Boosting Semi-Supervised Face Recognition with Noise Robustness

Yuchi Liu, Hailin Shi, Hang Du +4

Although deep face recognition benefits significantly from large-scale training data, a current bottleneck is the labelling cost. A feasible solution to this problem is semi-superv…

cs.CV202113 cited

Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition

Jiahui She, Yibo Hu, Hailin Shi +3

Due to the subjective annotation and the inherent interclass similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambigu…

cs.CV2021

FaceX-Zoo: A PyTorch Toolbox for Face Recognition

Jun Wang, Yinglu Liu, Yibo Hu +2

Deep learning based face recognition has achieved significant progress in recent years. Yet, the practical model production and further research of deep face recognition are in gre…

cs.CV20201 cited

Semi-Siamese Training for Shallow Face Learning

Hang Du, Hailin Shi, Yuchi Liu +4

Most existing public face datasets, such as MS-Celeb-1M and VGGFace2, provide abundant information in both breadth (large number of IDs) and depth (sufficient number of samples) fo…