activity
20172019
most citedSupport Vector Guided Softmax Loss for Face Recognition

44 citations · 156 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2019★ 32 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.CV2018★ 44 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…

cs.CL2018★ 2 cited

Multi-Source Pointer Network for Product Title Summarization

Fei Sun, Peng Jiang, Hanxiao Sun +3

In this paper, we study the product title summarization problem in E-commerce applications for display on mobile devices. Comparing with conventional sentence summarization, produc…

cs.CV2018★ 5 cited

Ensemble Soft-Margin Softmax Loss for Image Classification

Xiaobo Wang, Shifeng Zhang, Zhen Lei +3

Softmax loss is arguably one of the most popular losses to train CNN models for image classification. However, recent works have exposed its limitation on feature discriminability.…

cs.CV2017★ 33 cited

SFD: Single Shot Scale-invariant Face Detector

Shifeng Zhang, Xiangyu Zhu, Zhen Lei +3

This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (SFD), which performs superiorly on various scales of faces with a single deep ne…

cs.CV2017★ 39 cited

FaceBoxes: A CPU Real-time Face Detector with High Accuracy

Shifeng Zhang, Xiangyu Zhu, Zhen Lei +3

Although tremendous strides have been made in face detection, one of the remaining open challenges is to achieve real-time speed on the CPU as well as maintain high performance, si…