GIBIS at MediaEval 2017: Predicting Media Interestingness Task
نویسندگان
چکیده
This paper describes the GIBIS team experience in the Predicting Media Interestingness Task at MediaEval 2017. In this task, the teams were required to develop an approach to predict whether images or videos are interesting or not. Our proposal relies on late fusion with rank aggregation methods for combining ranking models learned with different features and by different learning-to-rank algorithms.
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