Video Memorability Prediction with Recurrent Neural Networks and Video Titles at the 2018 MediaEval Predicting Media Memorability Task

This paper describes the approach developed to predict the shortterm and long-term video memorability at the 2018 MediaEval Predicting Media Memorability Task [1]. This approach utilizes the scene semantics derived from the titles of the videos using natural language processing (NLP) techniques and a recurrent neural network (RNN). Compared to using video-based features, this approach has a low computational cost for feature extraction. The performance of the semantic-based methods are compared with those of the aesthetic feature-based methods using support vector regression (ε-SVR) and artificial neural network (ANN) models, and the possibility of predicting the highly subjective media memorability with simple features is explored.