CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Learning explicit video attributes from mid-level representation for video captioning
Nian, Fudong1; Li, Teng1,2; Wang, Yan1; Wu, Xinyu3; Ni, Bingbing4; Xu, Changsheng2
AbstractRecent works on video captioning mainly learn the map from low-level visual features to language description directly without explicitly representing the high-level semantic video concepts (e.g. objects, actions in the annotated sentences). To bridge the semantic gap, in this paper, addressing it, we propose a novel video attribute representation learning algorithm for video concept understanding and utilize the learned explicit video attribute representation to improve video captioning performance. To achieve it, firstly, inspired by the success of spectrogram in audio processing, a novel mid-level video representation named "video response map" (VRM) is proposed, by which the frame sequence could be represented by a single image representation. Therefore, the video attributes representation learning could be converted to a well-studied multi-label image classification problem. Then in the captions prediction step, the learned video attributes feature is integrated with the single frame feature to improve previous sequence-to sequence language generation model by adjusting the LSTM (Long-Short Term Memory) input units. The proposed video captioning framework could both handle variable frame inputs and utilize high-level semantic video attribute features. Experimental results on video captioning tasks show that the proposed method, utilizing only RGB frames as input without extra video or text training data, could achieve competitive performance with state-of-the-art methods. Furthermore, the extensive experimental evaluations on the UCF-101 action classification benchmark well demonstrate the representation capability of the proposed VRM. (C) 2017 Elsevier Inc. All rights reserved.
KeywordMid-level Video Representation Video Attributes Learning Video Caption Sequence-to-sequence Learning
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation (NSF) of China(61572029) ; China Postdoctoral Science Foundation(156613 ; 2016T90148)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000418726800011
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei, Anhui, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Beijing, Peoples R China
4.Shandong Jiaotong Univ, Shanghai, Peoples R China
Recommended Citation
GB/T 7714
Nian, Fudong,Li, Teng,Wang, Yan,et al. Learning explicit video attributes from mid-level representation for video captioning[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2017,163:126-138.
APA Nian, Fudong,Li, Teng,Wang, Yan,Wu, Xinyu,Ni, Bingbing,&Xu, Changsheng.(2017).Learning explicit video attributes from mid-level representation for video captioning.COMPUTER VISION AND IMAGE UNDERSTANDING,163,126-138.
MLA Nian, Fudong,et al."Learning explicit video attributes from mid-level representation for video captioning".COMPUTER VISION AND IMAGE UNDERSTANDING 163(2017):126-138.
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