摘要
针对开发人员难以快速从众多模型中找到自己所需的模型的问题,提出了一种基于自然语言处理技术的视觉类深度神经网络的自动标注方法。首先,划分视觉类神经网络的领域类别,根据词频等信息计算关键词及其对应的权值;其次,建立关键词提取器从论文摘要中提取出关键词;最后,将提取得到的关键词和已知权值进行相似度计算,从而得到模型的应用领域。从三大国际计算机视觉领域会议,即国际计算机视觉大会(ICCV)、IEEE国际计算机视觉与模式识别会议(CVPR)和欧洲计算机视觉国际会议(ECCV)发表的论文中选取实验数据进行实验。实验结果表明,所提方法能够提供宏平均值为0.89的高精度分类结果,验证了该方法的有效性。
Focused on the issue that developers cannot quickly figure out the models they need from various models,an automatic annotation method of visual deep neural network based on natural language processing technology was proposed.Firstly,the field categories of visual neural networks were divided,the keywords and corresponding weights were calculated according to the word frequency and other information. Secondly,a keyword extractor was established to extract keywords from paper abstracts. Finally,the similarities between extracted keywords and the known weights were calculated in order to obtain the application fields of a specific model. With experimental data derived from the papers published in three top international conferences of computer vision:IEEE International Conference on Computer Vision(ICCV),IEEE Conference on Computer Vision and Pattern Recognition(CVPR) and European Conference on Computer Vision(ECCV),the experiments were carried out. The experimental results indicate that the proposed method provides highly accurate classification results with a macro average value of 0. 89. The validity of this proposed method is verified.
作者
李鸣
郭晨皓
陈星
LI Ming;GUO Chenhao;CHEN Xing(College of Mathematics and Computer Science,Fuzhou University,Fuzhou Fujian 350108,China;Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing(Fuzhou University),Fuzhou Fujian 350108,China)
出处
《计算机应用》
CSCD
北大核心
2020年第6期1593-1600,共8页
journal of Computer Applications
基金
国家重点研发计划项目(2018YFB1004800)
福建省高校杰出青年科研人才计划项目
福建省引导性项目(2018H0017)。
关键词
计算机视觉
深度神经网络
文本分类
关键词提取
自动标注
模型应用领域
computer vision
deep neural network
text classification
keyword extraction
automatic annotation
model application field
