Captioning Transformer with Stacked Attention Modules
文献类型：期刊论文
| 作者 | Zhu, Xinxin1,2,4 ; Li, Lixiang1,2; Liu, Jing3; Peng, Haipeng1,2; Niu, Xinxin1,2
|
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2018-05-01 |
| 卷号 | 8期号:5 |
| 关键词 | Image Caption Image Understanding Deep Learning Computer Vision |
| DOI | 10.3390/app8050739 |
| 文献子类 | Article |
| 英文摘要 | Image captioning is a challenging task. Meanwhile, it is important for the machine to understand the meaning of an image better. In recent years, the image captioning usually use the long-short-term-memory (LSTM) as the decoder to generate the sentence, and these models show excellent performance. Although the LSTM can memorize dependencies, the LSTM structure has complicated and inherently sequential across time problems. To address these issues, recent works have shown benefits of the Transformer for machine translation. Inspired by their success, we develop a Captioning Transformer (CT) model with stacked attention modules. We attempt to introduce the Transformer to the image captioning task. The CT model contains only attention modules without the dependencies of the time. It not only can memorize dependencies between the sequence but also can be trained in parallel. Moreover, we propose the multi-level supervision to make the Transformer achieve better performance. Extensive experiments are carried out on the challenging MSCOCO dataset and the proposed Captioning Transformer achieves competitive performance compared with some state-of-the-art methods. |
| WOS研究方向 | Chemistry ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000437326800086 |
| 资助机构 | National Key R&D Program of China(2016YFB0800602) ; National Natural Science Foundation of China(61472045 ; 61573067) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21853]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 作者单位 | 1.Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Informat Secur Ctr, Beijing 100876, Peoples R China 2.Beijing Univ Posts & Telecommun, Natl Engn Lab Disaster Backup & Recovery, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Beijing Univ Posts & Telecommun, Sch Cyberspace Secur, POB 145, Beijing 100876, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Xinxin,Li, Lixiang,Liu, Jing,et al. Captioning Transformer with Stacked Attention Modules[J]. APPLIED SCIENCES-BASEL,2018,8(5). |
| APA | Zhu, Xinxin,Li, Lixiang,Liu, Jing,Peng, Haipeng,&Niu, Xinxin.(2018).Captioning Transformer with Stacked Attention Modules.APPLIED SCIENCES-BASEL,8(5). |
| MLA | Zhu, Xinxin,et al."Captioning Transformer with Stacked Attention Modules".APPLIED SCIENCES-BASEL 8.5(2018). |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。