GLA in MediaEval 2018 Emotional Impact of Movies Task
This work addresses the challenge of understanding emotional impact in movies for researchers, but it is incremental as it applies existing methods to a specific dataset and task.
The paper tackled the problem of predicting viewer emotions (valence and arousal) continuously from movies using visual and audio information, achieving top performance in three evaluation metrics in the MediaEval 2018 task.
The visual and audio information from movies can evoke a variety of emotions in viewers. Towards a better understanding of viewer impact, we present our methods for the MediaEval 2018 Emotional Impact of Movies Task to predict the expected valence and arousal continuously in movies. This task, using the LIRIS-ACCEDE dataset, enables researchers to compare different approaches for predicting viewer impact from movies. Our approach leverages image, audio, and face based features computed using pre-trained neural networks. These features were computed over time and modeled using a gated recurrent unit (GRU) based network followed by a mixture of experts model to compute multiclass predictions. We smoothed these predictions using a Butterworth filter for our final result. Our method enabled us to achieve top performance in three evaluation metrics in the MediaEval 2018 task.