International audienceIn this paper, the Predicting Media Interestingness task which is running for the second year as part of the MediaEval 2017 Bench-marking Initiative for Multimedia Evaluation, is presented. For the task, participants are expected to create systems that automatically select images and video segments that are considered to be the most interesting for a common viewer. All task characteristics are described, namely the task use case and challenges, the released data set and ground truth, the required participant runs and the evaluation metrics
Integrating media elements of various medium, multimedia is capable of expressing complex informatio...
ABSTRACT This paper describes the system developed by team MLP-BOON for MediaEval 2016 Predicting Me...
This paper outlines 6 approaches taken to computing video memorability, for the MediaEval media ...
International audienceIn this paper, the Predicting Media Interestingness task which is running for ...
Volume: 1739 Host publication title: MediaEval 2016 Multimedia Benchmark Workshop Host publication s...
ABSTRACT This working notes paper describes the TUD-MMC entry to the MediaEval 2016 Predicting Media...
International audienceThis paper summarizes the computational models that Technicolor proposes to pr...
International audienceThe ability of multimedia data to attract and keep people's interest for longe...
This paper describes our approach for the submission to the Media-eval 2017 Predicting Media Interes...
In this paper, we report on the creation of a publicly available, common evaluation framework for im...
In this paper, we present the Predicting Media Memorability task, which is proposed as part of the M...
This paper describes the MediaEval 2020 Predicting Media Memorability task. After first being propos...
This paper describes the MediaEval 2020 Predicting Media Memorability task. After first being propos...
| openaire: EC/H2020/780069/EU//MeMADIn this paper, we present the Predicting Media Memorability tas...
In the context of the ever growing quantity of multimedia content from social, news and educational ...
Integrating media elements of various medium, multimedia is capable of expressing complex informatio...
ABSTRACT This paper describes the system developed by team MLP-BOON for MediaEval 2016 Predicting Me...
This paper outlines 6 approaches taken to computing video memorability, for the MediaEval media ...
International audienceIn this paper, the Predicting Media Interestingness task which is running for ...
Volume: 1739 Host publication title: MediaEval 2016 Multimedia Benchmark Workshop Host publication s...
ABSTRACT This working notes paper describes the TUD-MMC entry to the MediaEval 2016 Predicting Media...
International audienceThis paper summarizes the computational models that Technicolor proposes to pr...
International audienceThe ability of multimedia data to attract and keep people's interest for longe...
This paper describes our approach for the submission to the Media-eval 2017 Predicting Media Interes...
In this paper, we report on the creation of a publicly available, common evaluation framework for im...
In this paper, we present the Predicting Media Memorability task, which is proposed as part of the M...
This paper describes the MediaEval 2020 Predicting Media Memorability task. After first being propos...
This paper describes the MediaEval 2020 Predicting Media Memorability task. After first being propos...
| openaire: EC/H2020/780069/EU//MeMADIn this paper, we present the Predicting Media Memorability tas...
In the context of the ever growing quantity of multimedia content from social, news and educational ...
Integrating media elements of various medium, multimedia is capable of expressing complex informatio...
ABSTRACT This paper describes the system developed by team MLP-BOON for MediaEval 2016 Predicting Me...
This paper outlines 6 approaches taken to computing video memorability, for the MediaEval media ...