CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Captioning Transformer with Stacked Attention Modules
Zhu, Xinxin1,2,4; Li, Lixiang1,2; Liu, Jing3; Peng, Haipeng1,2; Niu, Xinxin1,2
AbstractImage 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.
KeywordImage Caption Image Understanding Deep Learning Computer Vision
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
Indexed BySCI
Funding OrganizationNational Key R&D Program of China(2016YFB0800602) ; National Natural Science Foundation of China(61472045 ; 61573067)
WOS Research AreaChemistry ; Materials Science ; Physics
WOS SubjectChemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000437326800086
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Document Type期刊论文
Affiliation1.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
Recommended Citation
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).
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