Vis enkel innførsel

dc.contributor.authorZhang, Yujia
dc.contributor.authorKampffmeyer, Michael C.
dc.contributor.authorLiang, Xiaodan
dc.contributor.authorZhang, Dingwen
dc.contributor.authorTan, Min
dc.contributor.authorXing, Eric P.
dc.date.accessioned2020-03-05T06:44:05Z
dc.date.available2020-03-05T06:44:05Z
dc.date.issued2019-10-12
dc.description.abstractThe large amount of videos popping up every day, make it more and more critical that key information within videos can be extracted and understood in a very short time. Video summarization, the task of finding the smallest subset of frames, which still conveys the whole story of a given video, is thus of great significance to improve efficiency of video understanding. We propose a novel Dilated Temporal Relational Generative Adversarial Network (DTR-GAN) to achieve frame-level video summarization. Given a video, it selects the set of key frames, which contain the most meaningful and compact information. Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner. A new dilated temporal relation (DTR) unit is introduced to enhance temporal representation capturing. The generator uses this unit to effectively exploit global multi-scale temporal context to select key frames and to complement the commonly used Bi-LSTM. To ensure that summaries capture enough key video representation from a global perspective rather than a trivial randomly shorten sequence, we present a discriminator that learns to enforce both the information completeness and compactness of summaries via a three-player loss. The loss includes the generated summary loss, the random summary loss, and the real summary (ground-truth) loss, which play important roles for better regularizing the learned model to obtain useful summaries. Comprehensive experiments on three public datasets show the effectiveness of the proposed approach.en_US
dc.descriptionThis is a post-peer-review, pre-copyedit version of an article published in Multimedia Tools and Applications. The final authenticated version is available online at: http://dx.doi.org/<a href=https://doi.org/10.1007/s11042-019-08175-y>https://doi.org/10.1007/s11042-019-08175-y</a>.en_US
dc.identifier.citationZhang Y, Kampffmeyer MC, Liang X, Zhang, Tan M, Xing EP. Dilated temporal relational adversarial network for generic video summarization. Multimedia tools and applications. 2019;78(24):35237-35261en_US
dc.identifier.cristinIDFRIDAID 1749907
dc.identifier.doi10.1007/s11042-019-08175-y
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.urihttps://hdl.handle.net/10037/17624
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.journalMultimedia tools and applications
dc.relation.projectIDNorges forskningsråd: 239844en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/IKTPLUSS/239844/Norway/Next Generation Kernel-Based Machine Learning for Big Missing Data Applied to Earth Observation//en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright © 2019, Springer Natureen_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.titleDilated temporal relational adversarial network for generic video summarizationen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel