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BMVC2019.pdf

BMVC2019.pdf
Preview of BMVC2019.pdf
🔗 Source: eprints.whiterose.ac.uk
📊 Size: 360 KB
👤 Author: Kyle-Davidson, Cameron Philip, Bors, Adrian Gheorghe and Evans, Karla
⬇️ Downloads: 25

Summary

Researchers propose a Variational Autoencoder (VAE) model to predict Visual Memory Schemas (VMS) maps, which represent regions of an image that cause it to be remembered or falsely remembered. The VAE approach generates higher-resolution dual-channel images, allowing for separate evaluation of true and false memorability. Experiments verify that features leading to strong true and false VMS maps are grouped together, and conclude that saliency alone is not the driving feature for this approach.

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Description

Researchers propose a Variational Autoencoder (VAE) model to predict Visual Memory Schemas (VMS) maps, which represent regions of an image that cause it to be...

Technical Information

  • File Format: PDF
  • File Size: 360 KB
  • Pages: 12
  • Language: EN
  • Author: Kyle-Davidson, Cameron Philip, Bors, Adrian Gheorghe and Evans, Karla
  • Total Downloads: 25
  • Last Updated: 2 months ago

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