1 code implementation • CVPR 2021 • Shen Li, Jianqing Xu, Xiaqing Xu, Pengcheng Shen, Shaoxin Li, Bryan Hooi
Probabilistic Face Embeddings (PFE) is the first attempt to address this dilemma.
1 code implementation • CVPR 2021 • Xingkun Xu, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang, Yong Li, Zhen Cui
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.
no code implementations • 6 May 2021 • Fan Bai, Jiaxiang Wu, Pengcheng Shen, Shaoxin Li, Shuigeng Zhou
Face recognition has been extensively studied in computer vision and artificial intelligence communities in recent years.
no code implementations • 1 Jan 2021 • Shen Li, Jianqing Xu, Xiaqing Xu, Pengcheng Shen, Shaoxin Li, Bryan Hooi
To address these issues, in this paper, we propose a novel framework for face uncertainty learning in hyperspherical space.
no code implementations • ICCV 2021 • Jingshan Xu, Chuanwei Zhou, Zhen Cui, Chunyan Xu, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jian Yang
In this paper, we propose a progressive segmentation inference (PSI) framework to tackle with scribble-supervised semantic segmentation.
no code implementations • ICCV 2021 • Yun Wang, Tong Zhang, Xueya Zhang, Zhen Cui, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jian Yang
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.
2 code implementations • CVPR 2020 • Yuge Huang, YuHan Wang, Ying Tai, Xiaoming Liu, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang
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)
2 code implementations • ECCV 2020 • Yuge Huang, Pengcheng Shen, Ying Tai, Shaoxin Li, Xiaoming Liu, Jilin Li, Feiyue Huang, Rongrong Ji
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.