activity
20162022
most citedSupport Vector Guided Softmax Loss for Face Recognition

44 citations · 155 across the 12 of their papers we have counts for

collaborators

23 papers

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.CV2022

PetsGAN: Rethinking Priors for Single Image Generation

Zicheng Zhang, Yinglu Liu, Congying Han +3

Single image generation (SIG), described as generating diverse samples that have similar visual content with the given single image, is first introduced by SinGAN which builds a py…

cs.CV2021

FasterPose: A Faster Simple Baseline for Human Pose Estimation

Hanbin Dai, Hailin Shi, Wu Liu +3

The performance of human pose estimation depends on the spatial accuracy of keypoint localization. Most existing methods pursue the spatial accuracy through learning the high-resol…

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.CV2021

Towards NIR-VIS Masked Face Recognition

Hang Du, Hailin Shi, Yinglu Liu +2

Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition, which aims to match a pair of face images captured from two different…

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…