activity
20182022
most citedConvolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition

14 citations · 24 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV202214 cited

Convolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition

Zhongxia Zhang, Mingwen Wang

Convolutional neural networks have become a popular research in the field of finger vein recognition because of their powerful image feature representation. However, most researche…

cs.CV2021

Finger Vein Recognition by Generating Code

Zhongxia Zhang, Mingwen Wang

Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishes ability, security and non-invasive procedur…

cs.CV20212 cited

StrokeGAN: Reducing Mode Collapse in Chinese Font Generation via Stroke Encoding

Jinshan Zeng, Qi Chen, Yunxin Liu +2

The generation of stylish Chinese fonts is an important problem involved in many applications. Most of existing generation methods are based on the deep generative models, particul…

cs.CV20218 cited

Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background

Qiaosi Yi, Yunxing Liu, Aiwen Jiang +3

Crowd counting is an important task that shown great application value in public safety-related fields, which has attracted increasing attention in recent years. In the current res…

cs.CV2018

Progressive Feature Fusion Network for Realistic Image Dehazing

Kangfu Mei, Aiwen Jiang, Juncheng Li +1

Single image dehazing is a challenging ill-posed restoration problem. Various prior-based and learning-based methods have been proposed. Most of them follow a classic atmospheric s…

cs.CV2018

An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks

Kangfu Mei, Aiwen Jiang, Juncheng Li +2

Recent works on single-image super-resolution are concentrated on improving performance through enhancing spatial encoding between convolutional layers. In this paper, we focus on…