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Old Age
Translating Visual Art into Music
September 03, 2019 Β· Declared Dead Β· π 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
"No code URL or promise found in abstract"
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Authors
Maximilian MΓΌller-Eberstein, Nanne van Noord
arXiv ID
1909.01218
Category
cs.CV: Computer Vision
Cross-listed
cs.HC,
cs.LG,
cs.SD,
eess.AS
Citations
8
Venue
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Last Checked
4 months ago
Abstract
The Synesthetic Variational Autoencoder (SynVAE) introduced in this research is able to learn a consistent mapping between visual and auditive sensory modalities in the absence of paired datasets. A quantitative evaluation on MNIST as well as the Behance Artistic Media dataset (BAM) shows that SynVAE is capable of retaining sufficient information content during the translation while maintaining cross-modal latent space consistency. In a qualitative evaluation trial, human evaluators were furthermore able to match musical samples with the images which generated them with accuracies of up to 73%.
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