International audienceThis paper summarizes the computational models that Technicolor proposes to predict interestingness of images and videos within the MediaEval 2017 Predicting Media Interestingness Task. Our systems are based on deep learning architectures and exploit the use of both semantic and multimodal features. Based on the obtained results, we discuss our findings and obtain some scientific perspectives for the task
Media is created by humans for humans to tell stories. There exists a natural and imminent need for ...
| openaire: EC/H2020/780069/EU//MeMADThis paper describes a multimodal approach proposed by the MeMA...
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 ...
In the context of the ever growing quantity of multimedia content from social, news and educational ...
Volume: 1739 Host publication title: MediaEval 2016 Multimedia Benchmark Workshop Host publication s...
In this paper, we report on the creation of a publicly available, common evaluation framework for im...
International audienceThe ability of multimedia data to attract and keep people's interest for longe...
ABSTRACT This working notes paper describes the TUD-MMC entry to the MediaEval 2016 Predicting Media...
This thesis explores the application of a deep learning approach for the prediction of media interes...
This paper describes our approach for the submission to the Media-eval 2017 Predicting Media Interes...
ABSTRACT This paper describes the system developed by team MLP-BOON for MediaEval 2016 Predicting Me...
International audienceInterestingness has recently become an emerging concept for visual content ass...
Integrating media elements of various medium, multimedia is capable of expressing complex informatio...
International audienceIn this working note paper we present the contribution and results of the part...
Media is created by humans for humans to tell stories. There exists a natural and imminent need for ...
| openaire: EC/H2020/780069/EU//MeMADThis paper describes a multimodal approach proposed by the MeMA...
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 ...
In the context of the ever growing quantity of multimedia content from social, news and educational ...
Volume: 1739 Host publication title: MediaEval 2016 Multimedia Benchmark Workshop Host publication s...
In this paper, we report on the creation of a publicly available, common evaluation framework for im...
International audienceThe ability of multimedia data to attract and keep people's interest for longe...
ABSTRACT This working notes paper describes the TUD-MMC entry to the MediaEval 2016 Predicting Media...
This thesis explores the application of a deep learning approach for the prediction of media interes...
This paper describes our approach for the submission to the Media-eval 2017 Predicting Media Interes...
ABSTRACT This paper describes the system developed by team MLP-BOON for MediaEval 2016 Predicting Me...
International audienceInterestingness has recently become an emerging concept for visual content ass...
Integrating media elements of various medium, multimedia is capable of expressing complex informatio...
International audienceIn this working note paper we present the contribution and results of the part...
Media is created by humans for humans to tell stories. There exists a natural and imminent need for ...
| openaire: EC/H2020/780069/EU//MeMADThis paper describes a multimodal approach proposed by the MeMA...
This paper outlines 6 approaches taken to computing video memorability, for the MediaEval media ...