University of Cambridge > Talks.cam > Machine Learning @ CUED > Towards User-Friendly Image Inpainting: Learning-to-Rank based Image Quality Assessment for Image Inpainting

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹ️ Information

Towards User-Friendly Image Inpainting: Learning-to-Rank based Image Quality Assessment for Image Inpainting

Download to your calendar using vCal

If you have a question about this talk, please contact .

Image inpainting, which removes and restores unwanted regions in images, is widely acknowledged as a task whose results vary largely depending on the parameter settings. In typical use cases, users have to choose parameters and observe the results by trial and error, until the desired results are obtained. Thus a way to automatically select the best result is needed. In this talk, I will introduce our current research for learning based image quality assessment (IQA) methods for inpainting to support this need. Our framework uses no subjectively annotated data; we use only simulated failure results of inpainted images whose subjective qualities are controlled as the training data.

This talk is part of the Machine Learning @ CUED series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Auditorium, Mary Allan Building, Homerton College, Hills Road, Cambridge 23 West Road Music Room, West Lodge at Downing College

Other talks

Title TBC Title to be confirmed Welcome Reception and Poster Session John Kendrew Lecture - Title TBC César Milstein Lecture - Title TBC Title to be confirmed
 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity