Knowledge Commons of Institute of Automation,CAS
Mixed-Supervised Scene Text Detection With Expectation-Maximization Algorithm | |
Zhao, Mengbiao1,2; Feng, Wei1,2; Yin, Fei1,2; Zhang, Xu-Yao1,2; Liu, Cheng-Lin1,2 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
2022 | |
卷号 | 31页码:5513-5528 |
摘要 | Scene text detection is an important and challenging task in computer vision. For detecting arbitrarily-shaped texts, most existing methods require heavy data labeling efforts to produce polygon-level text region labels for supervised training. In order to reduce the cost in data labeling, we study mixed-supervised arbitrarily-shaped text detection by combining various weak supervision forms (e.g., image-level tags, coarse, loose and tight bounding boxes), which are far easier to annotate. Whereas the existing weakly-supervised learning methods (such as multiple instance learning) do not promote full object coverage, to approximate the performance of fully-supervised detection, we propose an Expectation-Maximization (EM) based mixed-supervised learning framework to train scene text detector using only a small amount of polygon-level annotated data combined with a large amount of weakly annotated data. The polygon-level labels are treated as latent variables and recovered from the weak labels by the EM algorithm. A new contour-based scene text detector is also proposed to facilitate the use of weak labels in our mixed-supervised learning framework. Extensive experiments on six scene text benchmarks show that (1) using only 10% strongly annotated data and 90% weakly annotated data, our method yields comparable performance to that of fully supervised methods, (2) with 100% strongly annotated data, our method achieves state-of-the-art performance on five scene text benchmarks (CTW1500, Total-Text, ICDAR-ArT, MSRA-TD500, and C-SVT), and competitive results on the ICDAR2015 Dataset. We will make our weakly annotated datasets publicly available. |
关键词 | Costs Annotations Training Labeling Detectors Data models Benchmark testing Mixed-supervised learning scene text detection weak supervision forms expectation-maximization algorithm |
DOI | 10.1109/TIP.2022.3197987 |
关键词[WOS] | LOCALIZATION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program[2020AAA0108003] ; National Natural Science Foundation of China (NSFC)[61733007] ; National Natural Science Foundation of China (NSFC)[61721004] |
项目资助者 | National Key Research and Development Program ; National Natural Science Foundation of China (NSFC) |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000844128200003 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49874 |
专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
通讯作者 | Liu, Cheng-Lin |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Zhao, Mengbiao,Feng, Wei,Yin, Fei,et al. Mixed-Supervised Scene Text Detection With Expectation-Maximization Algorithm[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022,31:5513-5528. |
APA | Zhao, Mengbiao,Feng, Wei,Yin, Fei,Zhang, Xu-Yao,&Liu, Cheng-Lin.(2022).Mixed-Supervised Scene Text Detection With Expectation-Maximization Algorithm.IEEE TRANSACTIONS ON IMAGE PROCESSING,31,5513-5528. |
MLA | Zhao, Mengbiao,et al."Mixed-Supervised Scene Text Detection With Expectation-Maximization Algorithm".IEEE TRANSACTIONS ON IMAGE PROCESSING 31(2022):5513-5528. |
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Mixed-Supervised_Sce(5999KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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