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Mixed-Supervised Scene Text Detection With Expectation-Maximization Algorithm 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 卷号: 31, 页码: 5513-5528
作者:  Zhao, Mengbiao;  Feng, Wei;  Yin, Fei;  Zhang, Xu-Yao;  Liu, Cheng-Lin
Adobe PDF(5999Kb)  |  收藏  |  浏览/下载：385/38  |  提交时间：2022/09/19
Costs  Annotations  Training  Labeling  Detectors  Data models  Benchmark testing  Mixed-supervised learning  scene text detection  weak supervision forms  expectation-maximization algorithm  
Adaptable Global Network for Whole-Brain Segmentation with Symmetry Consistency Loss 期刊论文
COGNITIVE COMPUTATION, 2022, 页码: 14
作者:  Zhao, Yuan-Xing;  Zhang, Yan-Ming;  Song, Ming;  Liu, Cheng-Lin
Adobe PDF(2496Kb)  |  收藏  |  浏览/下载：298/79  |  提交时间：2022/07/25
Whole-brain segmentation  Adaptable global network  Semi-supervised learning  Symmetry consistency loss  
Unconstrained end-to-end text reading with feature rectification 期刊论文
PATTERN RECOGNITION LETTERS, 2021, 卷号: 149, 页码: 1-8
作者:  Du, Chen;  Wang, Yanna;  Wang, Chunheng;  Xiao, Baihua;  Shi, Cunzhao
Adobe PDF(1133Kb)  |  收藏  |  浏览/下载：311/66  |  提交时间：2021/11/02
Text recognition  Text detection  Position-sensitive network  Features incompatibility  End-to-end  
DetectGAN: GAN-based text detector for camera-captured document images 期刊论文
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2020, 卷号: 23, 期号: 4, 页码: 267-277
作者:  Zhao, Jinyuan;  Wang, Yanna;  Xiao, Baihua;  Shi, Cunzhao;  Jia, Fuxi;  Wang, Chunheng
Adobe PDF(3817Kb)  |  收藏  |  浏览/下载：326/56  |  提交时间：2020/09/21
Text detection  Camera-captured document images  Multi-scale context features  Generative adversarial networks  
Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector 期刊论文
PATTERN RECOGNITION LETTERS, 2020, 卷号: 129, 页码: 108-114
作者:  Du, Chen;  Wang, Chunheng;  Wang, Yanna;  Shi, Cunzhao;  Xiao, Baihua
Adobe PDF(2583Kb)  |  收藏  |  浏览/下载：364/55  |  提交时间：2020/03/30
Feature combination  Network architecture  Selective feature connection mechanism  Convolutional neural network