|本期目录/Table of Contents|

[1]邓远远,沈炜.基于注意力反馈机制的深度图像标注模型[J].浙江理工大学学报,2019,41-42(自科二):208-216.
　DENG Yuanyuan,SHEN Wei.Depth image caption model based on  attention feedback mechanism[J].Journal of Zhejiang Sci-Tech University,2019,41-42(自科二):208-216.
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基于注意力反馈机制的深度图像标注模型()
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浙江理工大学学报[ISSN:1673-3851/CN:33-1338/TS]

卷:
第41-42卷
期数:
2019年自科二期
页码:
208-216
栏目:
出版日期:
2019-04-23

文章信息/Info

Title:
Depth image caption model based on  attention feedback mechanism
文章编号:
1673-3851 (2019) 03-0208-09
作者:
邓远远; 沈炜
浙江理工大学信息学院，杭州 310018
Author(s):
DENG Yuanyuan; SHEN Wei
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
关键词:
卷积神经网络; 深度学习; 图像识别; 注意力机制
分类号:
TP181
文献标志码:
A
摘要:
针对图像标注任务提出了一种基于注意力反馈机制的深度图像标注模型。该模型采用编码器解码器框架；编码器采用VGG16的网络结构，以提取图像的特征信息；在解码器部分设计了一种堆叠方式自上而下的处理注意力信息，使网络的每一层都可以获得额外的特征信息。然后从生成的标注语句中提取特征，将关注特征和图像的关注区域结合，增强和图像关注区域的匹配性，使生成的标注语句近似真实语境。在Flickr8k、Flickr30k和MSCOCO等数据集进行实验，实验结果显示，所提出模型的识别率比经典图像识别模型高5%~9%。

参考文献/References:

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［7］ Fang F, Wang H, Chen Y, et al. Looking deeper and transferring attention for image captioning［J］. Multimedia Tools and Applications, 2018(8): 1-17.
［8］ Chang Y S. Finegrained attention for image caption generation［J］. Multimedia Tools and Applications, 2018, 77(3): 2959-2971.
［9］ Vinyals O, Toshev A, Bengio S, et al. Show and tell: A neural image caption generator［C］//Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, 2015: 3156-3164.
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备注/Memo

备注/Memo:
收稿日期： 2018-09-08
网络出版日期： 2018-12-28
作者简介：邓远远(1992-)，男，河南安阳人，硕士研究生，主要从事图像识别方面的研究
通信作者：沈炜，E-mail：120259565@qq.com
更新日期/Last Update: 2019-03-19