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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Dublin City University and partners’ participation in the INS and VTT tracks at TRECVid 2016 Azevedo, Lucas; Zhou, Jiang; Smeaton, Alan F.... In: Marsden, Mark, Mohedano, Eva, McGuinness, Kevin orcid:0000-0003-1336-6477 , Calafell, Andrea, Giró-i-Nieto, Xavier orcid:0000-0002-9935-5332 , O'Connor, Noel E. orcid:0000-0002-4033-9135 , Zhou, Jiang orcid:0000-0002-3067-8512 , Azevedo, Lucas, Daudert, Tobias, Davis, Brian, Hurlimann, Manuela, Afli, Haithem orcid:0000-0002-7449-4707 , Du, Jinhua, Ganguly, Debasis orcid:0000-0003-0050-7138 , Li, Wei B. orcid:0000-0001-7347-3501 , Way, Andy orcid:0000-0001-5736-5930 and Smeaton, Alan F. orcid:0000-0003-1028-8389 (2016) Dublin City University and partners’ participation in the INS and VTT tracks at TRECVid 2016. In: TRECVid Conference, 14-16 Nov 2016, Gaithersburg, Md., USA. (2016) BASE Show details 2 Dublin City University and Partners' participation in the INS and VTT Tracks at TRECVid 2016 Azavedo, Lucas; Marsden, Mark; Zhou, Jiang; Daudert, Tobias; Ganguly, Debasis; Afli, Haithem; Giro-i-Nieto, Xavier; McGuinness, Kevin; Smeaton, Alan F.; Davis, Brian; O'Connor, Noel E.; Hürlimann, Manuela; Calafell, Andrea; Li, Wei; Mohedano, Eva; Way, Andy; Du, Jinhua. - 2016 Abstract: Dublin City University participated with a consortium of colleagues from NUI Galway and Universitat Polit`ecnica de Catalunya in two tasks in TRECVid 2016, Instance Search (INS) and Video to Text (VTT). For the INS task we developed a framework consisting of face detection and representation and place detection and representation, with a user annotation of top-ranked videos. For the VTT task we ran 1,000 concept detectors from the VGG-16 deep CNN on 10 keyframes per video and submitted 4 runs for caption re-ranking, based on BM25, Fusion, Word2Vec and a fusion of baseline BM25 and Word2Vec. With the same pre-processing for caption generation we used an open source image-to-caption CNN-RNN toolkit NeuralTalk2 to generate a caption for each keyframe and combine them. ; The work reported here is based on research conducted with the support of Science Foundation Ireland under grant numbers SFI/12/RC/2289 (Insight Centre) and SFI/13/RC/2106 (ADAPT Centre). ; non-peer-reviewed Keyword: Computational linguistics; Data analytics; Imaging systems; Semantic concept; Video captions URL: http://hdl.handle.net/10379/6242 BASE Hide details

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