Internet Archive Scholar
Filters
























1 Hit in 3.2 sec

EURECOM@MediaEval 2017: Media Genre Inference for Predicting Media Interestingness

Olfa Ben Ahmed, Jonas Wacker, Alessandro Gaballo, Benoit Huet
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  

Preserved Fulltext

fulltext thumbnail
Web Archive Capture PDF (569.2 kB)
https://web.archive.org/web/20220301214012/http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_29.pdf

A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL. The file type is application/pdf.

In this paper, we present EURECOM's approach to address the MediaEval 2017 Predicting Media Interestingness Task. We developed models for both the image and video subtasks.  ...  In particular, we investigate the usage of media genre information (i.e., drama, horror, etc.) to predict interestingness.  ...  The Titan Xp used for this research was donated by the NVIDIA Corporation.  ... 
dblp:conf/mediaeval/AhmedWGH17 fatcat:czfokeqbwzcnrk4iyfy4wa5a5m
Citation

Olfa Ben Ahmed, Jonas Wacker, Alessandro Gaballo, Benoit Huet. "EURECOM@MediaEval 2017: Media Genre Inference for Predicting Media Interestingness." MediaEval Benchmarking Initiative for Multimedia Evaluation (2017)