标题:Reconstruction Network for Video Captioning
作者:Wang, Bairui; Ma, Lin; Zhang, Wei; Liu, Wei
通讯作者:Ma, L;Zhang, W;Ma, Lin
作者机构:[Ma, Lin; Liu, Wei] Tencent AI Lab, Bellevue, WA 98004 USA.; [Wang, Bairui; Zhang, Wei] Shandong Univ, Sch Control Sci & Engn, Jinan, Shandong, Peop 更多
会议名称:31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
会议日期:JUN 18-23, 2018
来源:2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
出版年:2018
页码:7622-7631
DOI:10.1109/CVPR.2018.00795
摘要:In this paper, the problem of describing visual contents of a video sequence with natural language is addressed. Unlike previous video captioning work mainly exploiting the cues of video contents to make a language description, we propose a reconstruction network (RecNet) with a novel encoder-decoder-reconstructor architecture, which leverages both the forward (video to sentence) and backward (sentence to video) flows for video captioning. Specifically, the encoder-decoder makes use of the forward flow to produce the sentence description based on the encoded video semantic features. Two types of reconstructors are customized to employ the backward flow and reproduce the video features based on the hidden state sequence generated by the decoder The generation loss yielded by the encoder-decoder and the reconstruction loss introduced by the reconstructor are jointly drawn into training the proposed RecNet in an end-to-end fashion. Experimental results on benchmark datasets demonstrate that the proposed reconstructor can boost the encoder-decoder models and leads to significant gains in video caption accuracy.
收录类别:CPCI-S;EI;SCOPUS
Scopus被引频次:5
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061564798&doi=10.1109%2fCVPR.2018.00795&partnerID=40&md5=cdb7e37017d22b6ddd4b80f2f99253c6
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