Projects per year
Abstract
This paper describes a multimodal approach proposed by the MeMAD team for the MediaEval 2019 “Predicting Media memorability” task. Our best approach is a weighted average method combining predictions made separately from visual and textual representations of videos. In particular, we augmented the provided textual descriptions with automatically generated deep captions. For long term
memorability, we obtained better scores using the short term predictions rather than the long term ones. Our best model achieves Spearman scores of 0.522 and 0.277 respectively for the short and long term predictions tasks.
memorability, we obtained better scores using the short term predictions rather than the long term ones. Our best model achieves Spearman scores of 0.522 and 0.277 respectively for the short and long term predictions tasks.
Original language | English |
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Title of host publication | Working Notes Proceedings of the MediaEval 2019 Workshop, Sophia Antipolis, France, 27-30 October 2019 |
Publisher | CEUR |
Publication status | Published - 27 Oct 2019 |
MoE publication type | B2 Part of a book or another research book |
Event | Multimedia Benchmark Workshop - Sophia Antipolis, France Duration: 27 Oct 2019 → 30 Oct 2019 |
Publication series
Name | CEUR Workshop Proceedings |
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Publisher | CEUR |
Volume | 2670 |
ISSN (Electronic) | 1613-0073 |
Conference
Conference | Multimedia Benchmark Workshop |
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Abbreviated title | MediaEval |
Country | France |
City | Sophia Antipolis |
Period | 27/10/2019 → 30/10/2019 |
Projects
- 1 Active
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MeMAD Laaksonen
Laaksonen, J., Sjöberg, M. & Pehlivan Tort, S.
01/01/2018 → 30/06/2021
Project: EU: Framework programmes funding