activity
20182022
most citedSupport Vector Guided Softmax Loss for Face Recognition

44 citations · 89 across the 4 of their papers we have counts for

collaborators

5 papers

eess.IV2022

High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss

Yucong Lin, Jinhua Su, Yuhang Li +12

Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. P…

physics.ins-det2020

Measurement of Liquid Flow Rate among the Annular Flow in Vertical Tee Junction

Shuo Huang, Tianyu Fu, Guanmin Zhang +1

Since the liquid flow rate of the annular flow is closely related to the heat exchange efficiency, it has great significance to measure the liquid flow rate of the annular flow in…

cs.CV201913 cited

Mis-classified Vector Guided Softmax Loss for Face Recognition

Xiaobo Wang, Shifeng Zhang, Shuo Wang +3

Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discri…

cs.CV201932 cited

Improved Selective Refinement Network for Face Detection

Shifeng Zhang, Rui Zhu, Xiaobo Wang +5

As a long-standing problem in computer vision, face detection has attracted much attention in recent decades for its practical applications. With the availability of face detection…

cs.CV201844 cited

Support Vector Guided Softmax Loss for Face Recognition

Xiaobo Wang, Shuo Wang, Shifeng Zhang +3

Face recognition has witnessed significant progresses due to the advances of deep convolutional neural networks (CNNs), the central challenge of which, is feature discrimination. T…