Already for the 3rd time the team around Valerie Gouet-Brunet (IGN France/Université Gustave Eiffel/TMO executive board) organises a workshop staged within the frame of the international conference ACM Multimedia 2021 in Chengdu, China, from 20. – 24. October 2021.
The workshop is meant to act as an international stage for the presentation and discussion of the latest and most significant trends in the analysis, structuring and understanding of multimedia contents dedicated to the valorization of heritage, with emphasis on the unlocking of and access to the Big Data of the Past. You are most welcome to contribute your research papers for the following (but not limited to) topics:
- Multimedia and cross-domain data interlinking and recommendation
- Dating and spatialisation of historical data
- Mixed media data access and indexing
- Deep learning in adverse conditions (transfer learning, learning with side information, etc.)
- Multi-modal time series analysis, evolution modelling
- Multi-modal and multi-temporal data rendering
- Heritage – Building Information Modelling, Art Virtualisation
- HCI / Interfaces for large-scale datasets
- Smart digitisation of massive quantities of data
- Bench-marking, Open Data Movement
- Generative modelling of cultural heritage
Like in the previous year, the organisers and committee will award a prize of 500 euros for the best article, chosen by a selected jury and to be announced at the end of the workshop. The award is co-sponsored by the Friedrich-Alexander-Universität (FAU) Erlangen, the French Mapping Agency (IGN) and the French National Research Agency (ANR, Alegoria project).
Submission of Papers
Submission Due: 30 July 2021
Acceptance Notification: 26 August 2021
Camera Ready Submission: 2 September 2021
Workshop Date: 20 October 2021
All submissions must be original work not under review at any other workshop, conference, or journal. The workshop will accept papers describing completed work as well as work in progress. One submission format is accepted: full paper, which must follow the formatting guidelines of the main conference ACM MM 2021. Full papers should be from 6 to 8 pages (plus 2 additional pages for the references), encoded as PDF and using the ACM Article Template. For paper guidelines, please visit the conference website, and refer to the ‘Paper Format’ under ‘Submission Instructions’.
Peer Review and publication in ACM Digital Library
Paper submissions must conform with the “double-blind” review policy. All papers will be peer-reviewed by experts in the field, they will receive at least two reviews. Acceptance will be based on relevance to the workshop, scientific novelty, and technical quality. Depending on the number, maturity and topics of the accepted submissions, the work will be presented via oral or poster sessions. The workshop papers will be published in the ACM Digital Library.
Go to the link: Submission Portal & in the Author’s Console, create a new submission for the track: “3rd workshop on Structuring and Understanding of Multimedia heritAge Contents”
Jon Hardeberg (Professor at the Computer Science Department of the Norwegian University of Science and Technology, Gjøvik; coordinator of the project CHANGE):
Cultural Heritage (CH) Objects:
Mathieu Aubry (Senior researcher, Imagine team, LIGM lab, ENPC, École des Ponts ParisTech, France; coordinator of the ANR EnHerit project):
Deep Learning for Historical Data Analysis:
This presentation will give an overview of projects on leveraging deep learning for historical data analysis my group did in the last 3 years, partly in the context of the ANR EnHerit project. I will discuss in particular how deep learning can be used to establish links between artworks and historical documents: repeated patterns discovery in artwork collections, fine artwork alignment, document images segmentation, historical watermarks recognition, generic clustering, and scientific illustration propagation analysis. In all cases, I will show that standard approaches can give useful baseline results when tuned adequately, but that developing dedicated approaches that take into account the specificity of the data and the problem significantly improves the results.