南京理工大学学报（自然科学版）2024，Vol.48Issue(4)：451-459,9.DOI:10.14177/j.cnki.32-1397n.2024.48.04.006
基于协同训练的半监督图文关系抽取方法
Semi-supervised image-text relation extraction method based on co-training
摘要
Abstract
In order to overcome the expensive cost of obtaining a large number of relational labeled samples,a semi-supervised image-text relationship extraction model based on co-training is proposed to improve the accuracy of image-text relationship extraction by using a large amount of unlabeled data.First,an image view and a text semantic view are constructed based on two modalities of image and text,and classifiers of two different views are trained on the labeled dataset;then,the data under the two views are crossed into the classifier of the other view,fully mining the information of labeled data and unlabeled data to output more accurate classification results;finally,the classifiers are used in both views to predict unlabeled data to output consistent results.The experimental results on the public datasets VRD and VG show that compared with 6 current state-of-the-art relationship detection methods,the proposed method improves by 2.24%and 1.41%respectively in the VRD dataset for the image view and text semantic view,and 3.59%in the VG dataset.关键词
协同训练/半监督/多模态/关系抽取/视觉关系检测Key words
co-training/semi-supervised/multimodal/relationship extraction/visual relationship detection分类
信息技术与安全科学引用本文复制引用
王亚萍,王智强,王元龙,梁吉业..基于协同训练的半监督图文关系抽取方法[J].南京理工大学学报（自然科学版）,2024,48(4):451-459,9.基金项目
国家自然科学基金(61876103 （）
61906111) （）
