Video Captioning with Semantic Information from the Knowledge Base

Dan Wang, Dandan Song

科研成果: 书/报告/会议事项章节会议稿件同行评审

7 引用 (Scopus)
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摘要

Generating video description is a very challenging task due to the complex spatiotemporal information. Recently, many methods have been proposed by utilizing LSTM to generate sentence for video. Inspired by recent work in machine translation and object detection, we propose a new approach for video captioning which aims to incorporate Knowledge Base information with frame features of the video. We compare and analyze our approach with prior work and show that the large volumes information is available to generate video description. We experiment with our ideas on the S2VT model, and we demonstrate that our method outperforms the state-of-the-art on video captioning benchmarks.

源语言英语
主期刊名Proceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017
编辑Ruqian Lu, Xindong Wu, Tamer Ozsu, Xindong Wu, Jim Hendler
出版商Institute of Electrical and Electronics Engineers Inc.
224-229
页数6
ISBN(电子版)9781538631195
DOI
出版状态已出版 - 30 8月 2017
活动2017 IEEE International Conference on Big Knowledge, ICBK 2017 - Hefei, 中国
期限: 9 8月 201710 8月 2017

出版系列

姓名Proceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017

会议

会议2017 IEEE International Conference on Big Knowledge, ICBK 2017
国家/地区中国
Hefei
时期9/08/1710/08/17

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引用此

Wang, D., & Song, D. (2017). Video Captioning with Semantic Information from the Knowledge Base. 在 R. Lu, X. Wu, T. Ozsu, X. Wu, & J. Hendler (编辑), Proceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017 (页码 224-229). 文章 8023421 (Proceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBK.2017.26