3G structure for image caption generation | |
Yuan, Aihong1,2; Li, Xuelong1; Lu, Xiaoqiang1 | |
作者部门 | 光谱成像技术研究室 |
2019-02-22 | |
发表期刊 | Neurocomputing |
ISSN | 9252312;18728286 |
卷号 | 330页码:17-28 |
产权排序 | 1 |
摘要 | It is a big challenge of computer vision to make machine automatically describe the content of an image with a natural language sentence. Previous works have made great progress on this task, but they only use the global or local image feature, which may lose some important subtle or global information of an image. In this paper, we propose a model with 3-gated model which fuses the global and local image features together for the task of image caption generation. The model mainly has three gated structures. (1) Gate for the global image feature, which can adaptively decide when and how much the global image feature should be imported into the sentence generator. (2) The gated recurrent neural network (RNN) is used as the sentence generator. (3) The gated feedback method for stacking RNN is employed to increase the capability of nonlinearity fitting. More specially, the global and local image features are combined together in this paper, which makes full use of the image information. The global image feature is controlled by the first gate and the local image feature is selected by the attention mechanism. With the latter two gates, the relationship between image and text can be well explored, which improves the performance of the language part as well as the multi-modal embedding part. Experimental results show that our proposed method outperforms the state-of-the-art for image caption generation. ? 2018 |
DOI | 10.1016/j.neucom.2018.10.059 |
收录类别 | EI |
语种 | 英语 |
出版者 | Elsevier B.V. |
EI入藏号 | 20184506050720 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/31099 |
专题 | 光谱成像技术研究室 |
通讯作者 | Lu, Xiaoqiang |
作者单位 | 1.Center for OPTical IMagery Analysis and Learning (OPTIMAL), Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; Shaanxi; 710119, China; 2.University of Chinese Academy of Sciences, Beijing; 100049, China |
推荐引用方式 GB/T 7714 | Yuan, Aihong,Li, Xuelong,Lu, Xiaoqiang. 3G structure for image caption generation[J]. Neurocomputing,2019,330:17-28. |
APA | Yuan, Aihong,Li, Xuelong,&Lu, Xiaoqiang.(2019).3G structure for image caption generation.Neurocomputing,330,17-28. |
MLA | Yuan, Aihong,et al."3G structure for image caption generation".Neurocomputing 330(2019):17-28. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
3G structure for ima(3091KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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