Cummins, Seán, Sweeney, Lorin ORCID: 0000-0002-3427-1250 and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 (2022) Analysing the memorability of a procedural crime-drama TV series, CSI. In: 19th International Conference on Content-based Multimedia Indexing, CBMI 2022, September 14–16, 2022, Graz, Austria. ISBN 978-1-4503-9720-9
Abstract
We investigate the memorability of a 5-season span of a popular crime-drama TV series, CSI, through the application of a vision transformer fine-tuned on the task of predicting video memorability. By investigating the popular genre of crime-drama TV through the use of a detailed annotated corpus combined with video memorability scores, we show how to extrapolate meaning from the memorability scores generated on video shots. We perform a quantitative analysis to relate video shot memorability to a variety of aspects of the show. The insights we present in this paper illustrate the importance of video memorability in applications which use multimedia in areas like education, marketing, indexing, as well as in the case here namely TV and film production.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Uncontrolled Keywords: | Video memorability; vision transformers; CSI TV series |
Subjects: | Computer Science > Artificial intelligence Computer Science > Machine learning 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 |
Publisher: | Association for Computing Machinery (ACM) |
Official URL: | https://doi.org/10.1145/3549555.3549592 |
Copyright Information: | © 2022 The Authors |
Funders: | Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/ 2289_P2 (Insight SFI Research Centre for Data Analytics). |
ID Code: | 27499 |
Deposited On: | 12 Sep 2022 11:59 by Alan Smeaton . Last Modified 12 Sep 2022 11:59 |
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 847kB |
Metrics
Altmetric Badge
Dimensions Badge
Downloads
Downloads
Downloads per month over past year
Archive Staff Only: edit this record