| 基于注意力特征提取网络的图像描述生成算法 |
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| 引用本文： | 李金轩,杜军平,周南.基于注意力特征提取网络的图像描述生成算法[J].南京气象学院学报,2019,11(3):295-301. |
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| 作者姓名： | 李金轩 杜军平 周南 |
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| 作者单位： | 北京邮电大学 智能通信软件与多媒体北京市重点实验室/计算机学院, 北京, 100876,北京邮电大学 智能通信软件与多媒体北京市重点实验室/计算机学院, 北京, 100876,北京邮电大学 智能通信软件与多媒体北京市重点实验室/计算机学院, 北京, 100876 |
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| 基金项目： | 国家自然科学基金（61772083，6153 2006，61877006，61802028）；广西科技重大专项（桂科AA18118054） |
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| 摘 要： | 针对解决图像描述生成中对浅层图像特征利用不充分、图像目标间关系提取不足的问题，提出一种基于注意力图像特征提取的图像描述生成算法.通过语言模型上下文信息对不同深度图像特征进行自适应注意力权重分配，使带有注意力的图像特征参与指导图像描述生成，提升了图像描述生成的效果.在MSCOCO测试集中所提算法的BLEU-1和CIDEr得分分别达到0.752和0.934，从而验证了所提算法的有效性.
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| 关 键 词： | 注意力机制 图像描述 长短期记忆网络 图像特征提取 |
| 收稿时间： | 2019/5/16 0:00:00 |
Image caption algorithm based on an attention image feature extraction network |
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| LI Jinxuan,DU Junping and ZHOU Nan.Image caption algorithm based on an attention image feature extraction network[J].Journal of Nanjing Institute of Meteorology,2019,11(3):295-301. |
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| Authors: | LI Jinxuan DU Junping and ZHOU Nan |
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| Institution: | Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876,Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876 and Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876 |
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| Abstract: | To solve the problem of the lack of use of shallow image features in image captions and insufficient extraction of image objects,an image caption generation algorithm based on attention image feature extraction is proposed.Through context information of a language model,adaptive attention weight assignment is performed on different depth image features to ensure that the attention-grabbing image features guide the image caption generation,thereby improving the image caption effect.In the MSCOCO test set,the BLEU-1 and CIDEr scores of the proposed algorithm reached 0.752 and 0.934,respectively,thus verifying the effectiveness of the proposed method. |
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| Keywords: | attention mechanism image caption long and short term memory network image feature extraction |
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