Captioning Transformer with Stacked Attention Modules

doi:10.3390/app8050739

	Captioning Transformer with Stacked Attention Modules
	Zhu, Xinxin1,2,4 ; Li, Lixiang 1,2; Liu, Jing3 ; Peng, Haipeng 1,2; Niu, Xinxin 1,2
发表期刊	APPLIED SCIENCES-BASEL
	2018-05-01
卷号	8 期号:5
文章类型	Article
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.
关键词	Image Caption Image Understanding Deep Learning Computer Vision
WOS标题词	Science & Technology ; Physical Sciences ; Technology
DOI	10.3390/app8050739
收录类别	SCI
语种	英语
项目资助者	National Key R&D Program of China(2016YFB0800602) ; National Natural Science Foundation of China(61472045 ; 61573067)
WOS研究方向	Chemistry ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:000437326800086
引用统计	被引频次：48[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/21853
专题	紫东太初大模型研究中心_图像与视频分析
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).