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The visual and audio information from movies can evoke a variety of emotionsin viewers. Towards a better understanding of viewer impact, we present ourmethods for the MediaEval 2018 Emotional Impact of Movies Task to predict theexpected valence and arousal continuously in movies. This task, using theLIRIS-ACCEDE dataset, enables researchers to compare different approaches forpredicting viewer impact from movies. Our approach leverages image, audio, andface based features computed using pre-trained neural networks. These featureswere computed over time and modeled using a gated recurrent unit (GRU) basednetwork followed by a mixture of experts model to compute multiclasspredictions. We smoothed these predictions using a Butterworth filter for ourfinal result. Our method enabled us to achieve top performance in threeevaluation metrics in the MediaEval 2018 task.

GLA in MediaEval 2018 Emotional Impact of Movies Task