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Dublin City University and Partners’ Participation in the INS and VTT Tracks at TRECVid 2016

Marsden, Mark and Mohedano, Eva and McGuinness, Kevin and Calafell, Andrea and Giró-i-Nieto, Xavier and O'Connor, Noel E. and Zhou, Jiang and Azevedo, Lucas and Daudert, Tobias and Davis, Brian and Hurlimann, Manuela and Afli, Haithem and Du, Jinhua and Ganguly, Debasis and Li, Wei B. and Way, Andy and Smeaton, Alan F. (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.

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Abstract

Dublin City University participated with a consortium of colleagues from NUI Galway and Universitat Politecnica 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.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:No
Uncontrolled Keywords:Semantic Concept; Video Captions
Subjects:Engineering > Imaging systems
Computer Science > Computational linguistics
Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Multimedia systems
Computer Science > Image processing
Computer Science > Digital video
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
Funders:Science Foundation Ireland SFI/12/RC/2289 (Insight Centre) , Science Foundation Ireland SFI/13/RC/2106 (ADAPT Centre)
ID Code:21484
Deposited On:01 Dec 2016 15:24 by Alan Smeaton. Last Modified 08 Feb 2017 09:38

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