Kyle-Davidson, Cameron Philip, Bors, Adrian Gheorghe orcid.org/0000-0001-7838-0021 and Evans, Karla orcid.org/0000-0002-8440-1711 (2019) Predicting Visual Memory Schemas with Variational Autoencoders. In: Proc. British Machine Vision Conference (BMVC).
Abstract
Visual memory schema (VMS) maps show which regions of an image cause that image to be remembered or falsely remembered. Previous work has succeeded in generating low resolution VMS maps using convolutional neural networks. We instead approach this problem as an image-to-image translation task making use of a variational autoencoder. This approach allows us to generate higher resolution dual channel images that represent visual memory schemas, allowing us to evaluate predicted true memorability and false memorability separately. We also evaluate the relationship between VMS maps, predicted VMS maps, ground truth memorability scores, and predicted memorability scores.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York > Faculty of Sciences (York) > Psychology (York) |
Depositing User: | Pure (York) |
Date Deposited: | 07 Oct 2019 14:30 |
Last Modified: | 18 Dec 2024 00:38 |
Status: | Published |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151860 |