29 citations · 122 across the 10 of their papers we have counts for
10 papers
PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition
Qiang Meng, Xiaqing Xu, Xiaobo Wang +6
Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e.g.…
An Efficient Training Approach for Very Large Scale Face Recognition
Kai Wang, Shuo Wang, Panpan Zhang +7
Face recognition has achieved significant progress in deep learning era due to the ultra-large-scale and welllabeled datasets. However, training on the outsize datasets is time-con…
Weakly Supervised Construction of ASR Systems with Massive Video Data
Mengli Cheng, Chengyu Wang, Xu Hu +2
Building Automatic Speech Recognition (ASR) systems from scratch is significantly challenging, mostly due to the time-consuming and financially-expensive process of annotating a la…
Loss Function Search for Face Recognition
Xiaobo Wang, Shuo Wang, Cheng Chi +2
In face recognition, designing margin-based (e.g., angular, additive, additive angular margins) softmax loss functions plays an important role in learning discriminative features.…
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…
A High-Efficiency Framework for Constructing Large-Scale Face Parsing Benchmark
Yinglu Liu, Hailin Shi, Yue Si +3
Face parsing, which is to assign a semantic label to each pixel in face images, has recently attracted increasing interest due to its huge application potentials. Although many fac…