Sweeney, Lorin ORCID: 0000-0002-3427-1250, Matran-Fernandez, Ana ORCID: 0000-0002-8409-3747, Halder, Sebastian ORCID: 0000-0003-1017-3696, García Seco de Herrera, Alba ORCID: 0000-0002-6509-5325, Smeaton, Alan F. ORCID: 0000-0003-1028-8389 and Healy, Graham ORCID: 0000-0001-6429-6339 (2021) Overview of the EEG pilot subtask at MediaEval 2021: predicting media memorability. In: Mediaeval Multimedia Evaluation Benchmark 2021, 13-15 Dec 2021, Online.
Abstract
The aim of the Memorability-EEG pilot subtask at MediaEval'2021 is to promote interest in the use of neural signals---either alone or in combination with other data sources---in the context of predicting video memorability by highlighting the utility of EEG data. The dataset created consists of pre-extracted features from EEG recordings of subjects while watching a subset of videos from Predicting Media Memorability subtask 1.
This demonstration pilot gives interested researchers a sense of how neural signals can be used without any prior domain knowledge, and enables them to do so in a future memorability task. The dataset can be used to support the exploration of novel machine learning and processing strategies for predicting video memorability, while potentially increasing interdisciplinary interest in the subject of
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Subjects: | Computer Science > Artificial intelligence Computer Science > Digital video |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing Research Institutes and Centres > INSIGHT Centre for Data Analytics |
Published in: | Proceedings of Mediaeval 2021. . CEUR-WS. |
Publisher: | CEUR-WS |
Official URL: | https://ceur-ws.org/Vol-3181/paper16.pdf |
Copyright Information: | © 2021 The Authors |
Funders: | Science Foundation Ireland, National Institute of Standards and Technology 60NANB19D155 |
ID Code: | 26545 |
Deposited On: | 05 Jan 2022 13:21 by Alan Smeaton . Last Modified 16 Nov 2023 12:41 |
Documents
Full text available as:
Preview |
PDF
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Creative Commons: Attribution-Noncommercial 3.0 508kB |
Downloads
Downloads
Downloads per month over past year
Archive Staff Only: edit this record