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Ensemble of Streamlined Bilinear Visual Question Answering Models for the ImageCLEF 2019 Challenge in the Medical Domain
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
ARTORG Center, University of Bern, Bern, Switzerland.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Umeå University, Faculty of Science and Technology, Department of Chemistry.ORCID iD: 0000-0001-7119-7646
2019 (English)In: CLEF 2019: Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum / [ed] Linda Cappellato, Nicola Ferro, David E. Losada, and Henning Müller, 2019, Vol. 2380Conference paper, Published paper (Other academic)
Abstract [en]

This paper describes the contribution by participants from Umeå University, Sweden, in collaboration with the University of Bern, Switzerland, for the Medical Domain Visual Question Answering challenge hosted by ImageCLEF 2019. We proposed a novel Visual Question Answering approach that leverages a bilinear model to aggregateand synthesize extracted image and question features. While we did not make use of any additional training data, our model used an attention scheme to focus on the relevant input context and was further boosted by using an ensemble of trained models. We show here that the proposed approach performs at state-of-the-art levels, and provides an improvement over several existing methods. The proposed method was ranked 3rd in the Medical Domain Visual Question Answering challenge of ImageCLEF 2019.

Place, publisher, year, edition, pages
2019. Vol. 2380
National Category
Computer Vision and Robotics (Autonomous Systems) Medical and Health Sciences
Identifiers
URN: urn:nbn:se:umu:diva-166758OAI: oai:DiVA.org:umu-166758DiVA, id: diva2:1381723
Conference
CLEF 2019 - Conference and Labs of the Evaluation Forum, Lugano, Switzerland, Sept 9-12, 2019
Available from: 2019-12-27 Created: 2019-12-27 Last updated: 2020-01-02Bibliographically approved

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Nyholm, TufveLöfstedt, Tommy
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