Skip to content
/ CSIB Public

Contextual Semantic Interpretability, ACCV 2020

Notifications You must be signed in to change notification settings

dmarcosg/CSIB

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Contextual Semantic Interpretability (CSIB)

This code allows to train a CNN model to predict a good map of penalizations for the different term of an Active Contour Model (ACM) such that the result gets close to a set of ground truth contours, as presented in [1] (to appear in ACCV 2020).

A preprint of the paper can be found in https://arxiv.org/pdf/2009.08720.pdf

Datasets

SUN Attributes

ScenicOrNot (TSV file that contains the scores and image URLs)

Please contact me at diego.marcos@wur.nl for questions and feedback.

[1] Marcos, D., Fong, R., Lobry, S., Flamary, R., Courty, N. & Tuia, D. Contextual Semantic Interpretability. ACCV 2020.

About

Contextual Semantic Interpretability, ACCV 2020

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Languages