Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?

Autor:	De Herrera, Alba García Seco, Kiziltepe, Rukiye Savran, Chamberlain, Jon, Constantin, Mihai Gabriel, Demarty, Claire-Hélène, Doctor, Faiyaz, Ionescu, Bogdan, Smeaton, Alan F.
Rok vydání:	2020
Předmět:	Computer Science - Multimedia Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition
Zdroj:	MediaEval Multimedia Benchmark Workshop Working Notes, 14-15 December 2020
Druh dokumentu:	Working Paper
Popis:	This paper describes the MediaEval 2020 \textit{Predicting Media Memorability} task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previous editions. This year the videos are a subset of the TRECVid 2019 Video-to-Text dataset, containing more action rich video content as compared with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for participants' run submissions. Comment: 3 pages, 1 Figure
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.15650 Zobrazit plný text záznamu View this record from Arxiv