TimeMatrix for Researchers Webinar

Type of event

Place: Online

Date: 9-10 September 2020

Time: 9:00 - 12:00 (CEST)


In caption generator systems, the identification of the figures depicted in an image depends on a matrix of weights for each of the classes with which the system has been trained. Current caption generators are trained with images that reflect present time lifestyles. Therefore the matrix is in fact a TimeMatrix of the present. The course will show how the identification of classes varies as the matrix of weights depends on data relative to past centuries. This produces a time machine effect where the bike of a person in the TimeMatrix of the present becomes a horse of Saint George in the TimeMatrix of the 15th century.

This webinar will demonstrate the SGoaB results and potential to adapt automatically produced descriptions of paintings  to the time period when they were created. The course will pose and discuss challenges for researchers. At the end of the course, a demo will be conducted to show correction of anachronisms and class refinement examples.

Learning outcomes

Participants will be introduced to what we call the time machine effect, which consists of the objects of an image being transformed via deep learning methods to similar concepts that are more appropriate to another time period. The technical challenges and current solutions will be discussed.

Target group

Academic researchers in data mining and data science that are interested in art and culture.


Active interest in cultural heritage a must.
Basic knowledge of Natural Language Processing a plus.
Using Neural Networks a plus.

Course lecturers

Maria Cristina Marinescu (CASE Department, Barcelona Supercomputing Center)
Joaquim More Lopez (CASE Department, Barcelona Supercomputing Center)
Artem Reshetnikov (CASE Department, Barcelona Supercomputing Center)


Day 1. Understanding the past through its parallels with the present

9:00-9:20 Introduction to the general issue of describing cultural heritage images: what is done, and what is used for? Maria Cristina Marinescu
9:20-10:05 AI-powered semantic labelling of image datasets- formats, target concepts, challenges and description of TimeMatrix and its purpose Joaquim More Lopez 
10:05-10:35 Training data in image datasets - images and metadata, challenges Artem Reshetnikov
10:35-10:45 Break  

Present state-of-the-art caption generators and their limitations when dealing with anachronic images

Joaquim More Lopez 
11:10-11:45 Transfer learning to describe cultural heritage images as they differ from the present way of life - anachronisms, evolution of concepts through time, minorities, symbols Artem Reshetnikov

Wrap-up + Connecting the dots to see the bigger picture

Maria Cristina Marinescu


Day 2. Technical implementation of the TimeMatrix

9:00-9:15 Filtering anachronic classes Joaquim More Lopez 
9:15-9:45 Defining new visual classes and visual relations for  cultural and  iconographic symbols Joaquim More Lopez 
9:45-10:15 Refining the identification of classes and visual relations according to time contex Artem Reshetnikov
10:15-10:30 Deep Learning implementation of anachronism correction and class refinement Artem Reshetnikov
10:30-10:40 Break  
10:40-11:00 Current state of the Saint George on a Bike project Joaquim More Lopez 
11:00-11:30 Class features and Deep Learning implementation Artem Reshetnikov
11:30-12:00 Discussion of anachronic correction and class refinement examples (demo) Artem Reshetnikov

Registration is closed. 

Read the news

TimeMatrix for Researchers banner