[visionlist] AI4Media: Dissemination of Special Session on Computational Memorability of Imagery
ioannakoroni at csd.auth.gr
Fri Feb 17 06:40:01 -04 2023
Subject: AI4Media: CBMI 2023 Special Session on Computational Memorability of Imagery
Please find below the Call for Papers for the Special Session on Computational Memorability of Imagery <https://cbmi2023.org/special-sessions/#CMI> at CBMI 2023. The conference and special session are supported by the AI4Media project.
Please consider submitting your work to this special session.
Computational Memorability of Imagery
Special Session at CBMI 2023
20-22 September 2023
The subject of memorability has seen an influx in interest since the likelihood of images being recognised upon subsequent viewing was found to be consistent across individuals. Driven primarily by the MediaEval Media Memorability tasks which has just completed its 5th annual iteration, recent research has extended beyond static images, pivoting to the more dynamic and multi-modal medium of video memorability.
The memorability of a video or an image is an abstract concept and like other features such as aesthetics and beauty, is an intrinsic feature of imagery. There are many applications for predicting image and video memorability including marketing where some part of a video advertisement should strive to be the most memorable, in education where key parts of educational content should be memorable, in other areas of content creation such as video summaries of longer events like movies or wedding photography, and in cinematography where a director may want to make some parts of a movie or TV program more, or less, memorable than the rest.
For computing video memorability, researchers have used a variety of approaches including video vision transformers as well as more conventional machine learning, text features from text captions, a range of ensemble approaches, and even generating surrogate videos using stable diffusion methods. The performance of these approaches tells us that we are now close to the best performance for memorability prediction for video and for images that we could get using current techniques and that there are many research groups who can achieve such a level of performance.
We believe that image and video memorability is now ready for the spotlight and for researchers to be drawn to using video memorability prediction in creative ways. We invite submissions from researchers who wish to extend their reported techniques and/or apply those techniques to real-world applications like marketing, education, or other areas of content production. We hope that the output from this special session will be a community-wide realization of the potential for video memorability prediction and uptake in research into, and applications of, the topic.
The topics of the special session include, but are not limited to:
* Development and interpretation of single- or multi-modal models for Computational Memorability
* Transfer learning and transferability for Computational Memorability
* Computational Memorability applications
* Extending work from MediaEval Predicting Media Memorability task
* Cross- and multilingual aspects in Computational Memorability
* Evaluation and resources for Computational Memorability
* Computational memorability prediction based on physiological data (e.g.: EEG data)
The contributions to this special session are regular short papers (only) as 4 pages, plus additional pages for the list of references. The review process is single-blind, meaning authors do not have to anonymise their submissions.
Paper submission: April 12, 2023
Notification of acceptance: June 1, 2023
Camera ready paper: June 15, 2023
Conference dates: September 20-22, 2023
* Alba García Seco de Herrera, University of Essex (alba.garcia at essex.ac.uk <mailto:alba.garcia at essex.ac.uk> )
* Mihai Gabriel Constantin, University Politehnica of Bucharest (mihai.constantin84 at upb.ro <mailto:mihai.constantin84 at upb.ro> )
* Alan Smeaton, Dublin City University (alan.smeaton at dcu.ie <mailto:alan.smeaton at dcu.ie> )
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