Tips and tricks for visual question answering: learnings from the 2017 challenge

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dc.contributor.authorTeney, D.
dc.contributor.authorAnderson, P.
dc.contributor.authorHe, X.
dc.contributor.authorVan Den Hengel, A.
dc.contributor.conferenceIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (18 Jun 2018 - 23 Jun 2018 : Salt Lake City, USA)
dc.date.issued2018
dc.description.abstractDeep Learning has had a transformative impact on Computer Vision, but for all of the success there is also a significant cost. This is that the models and procedures used are so complex and intertwined that it is often impossible to distinguish the impact of the individual design and engineering choices each model embodies. This ambiguity diverts progress in the field, and leads to a situation where developing a state-of-the-art model is as much an art as a science. As a step towards addressing this problem we present a massive exploration of the effects of the myriad architectural and hyperparameter choices that must be made in generating a state-of-the-art model. The model is of particular interest because it won the 2017 Visual Question Answering Challenge. We provide a detailed analysis of the impact of each choice on model performance, in the hope that it will inform others in developing models, but also that it might set a precedent that will accelerate scientific progress in the field.
dc.description.statementofresponsibilityDamien Teney, Peter Anderson, Xiaodong He, Anton van den Hengel
dc.identifier.citationProceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, pp.4223-4232
dc.identifier.doi10.1109/CVPR.2018.00444
dc.identifier.isbn9781538664216
dc.identifier.issn1063-6919
dc.identifier.issn2575-7075
dc.identifier.orcidTeney, D. [0000-0003-2130-6650]
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]
dc.identifier.urihttp://hdl.handle.net/2440/128334
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartofseriesIEEE Conference on Computer Vision and Pattern Recognition
dc.rights© 2018 IEEE
dc.source.urihttps://ieeexplore.ieee.org/xpl/conhome/8576498/proceeding
dc.titleTips and tricks for visual question answering: learnings from the 2017 challenge
dc.typeConference paper
pubs.publication-statusPublished

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