GLA in MediaEval 2018 Emotional Impact of Movies Task

11/27/2019

∙

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.

READ FULL TEXT

GLA in MediaEval 2018 Emotional Impact of Movies Task

Enhancing the Prediction of Emotional Experience in Movies using Deep Neural Networks: The Significance of Audio and Language

Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging

Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies

Emotional Video to Audio Transformation Using Deep Recurrent Neural Networks and a Neuro-Fuzzy System

Multimodal Deep Models for Predicting Affective Responses Evoked by Movies

Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformers

Emotional Expression Detection in Spoken Language Employing Machine Learning Algorithms

GLA in MediaEval 2018 Emotional Impact of Movies Task

Related Research

Enhancing the Prediction of Emotional Experience in Movies using Deep Neural Networks: The Significance of Audio and Language

Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging

Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies

Emotional Video to Audio Transformation Using Deep Recurrent Neural Networks and a Neuro-Fuzzy System

Multimodal Deep Models for Predicting Affective Responses Evoked by Movies

Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformers

Emotional Expression Detection in Spoken Language Employing Machine Learning Algorithms