MediaEval 2016 Predicting Media Interestingness Task

This paper provides an overview of the Predicting MediaInterestingness task that is organized as part of the Media-Eval 2016 Benchmarking Initiative for Multimedia Evalua-tion. The task, which is running for the first year, expectsparticipants to create systems that automatically select images and video segments that are considered to be the mostinteresting for a common viewer. In this paper, we presentthe task use case and challenges, the proposed data set andground truth, the required participant runs and the evalua-tion metrics

This paper describes the MediaEval 2021 Predicting Media Memorability task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 4th edition this year, as the prediction of short-term and long-term video memorability remains a challenging task. This year, two datasets of videos are used:...

High-level understanding of stories in video such as movies and TV shows from raw data is extremely challenging. Modern video question answering (VideoQA) systems often use additional human-made sources like plot synopses, scripts, video descriptions or knowledge bases. In this work, we present a new approach to understand the whole...

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