Overview of The MediaEval 2021 Predicting Media Memorability Task

Autor:	Kiziltepe, Rukiye Savran, Constantin, Mihai Gabriel, Demarty, Claire-Helene, Healy, Graham, Fosco, Camilo, de Herrera, Alba Garcia Seco, Halder, Sebastian, Ionescu, Bogdan, Matran-Fernandez, Ana, Smeaton, Alan F., Sweeney, Lorin
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Multimedia
Druh dokumentu:	Working Paper
Popis:	This paper describes the MediaEval 2021 Predicting Media Memorability}task, which is in its 4th edition this year, as the prediction of short-term and long-term video memorability remains a challenging task. In 2021, two datasets of videos are used: first, a subset of the TRECVid 2019 Video-to-Text dataset; second, the Memento10K dataset in order to provide opportunities to explore cross-dataset generalisation. In addition, an Electroencephalography (EEG)-based prediction pilot subtask is introduced. In this paper, we outline the main aspects of the task and describe the datasets, evaluation metrics, and requirements for participants' submissions. Comment: 3 pages, to appear in Proceedings of MediaEval 2021, December 13-15 2021, Online
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.05982 Zobrazit plný text záznamu View this record from Arxiv