# GLA in MediaEval 2018 Emotional Impact of Movies Task

arXiv: https://arxiv.org/abs/1911.12361 · HTML: https://mlsanity.com/paper/1911.12361/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Learning Emotion Representations from Verbal and Nonverbal Communication](https://mlsanity.com/paper/2305.13500/) (2023)
- [Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality](https://mlsanity.com/paper/2103.06541/) (2021)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [Temporal Convolution Networks with Positional Encoding for Evoked Expression Estimation](https://mlsanity.com/paper/2106.08596/) (2021)
- [Multi-Granularity Network with Modal Attention for Dense Affective Understanding](https://mlsanity.com/paper/2106.09964/) (2021)
- [Learning Subjective Time-Series Data via Utopia Label Distribution Approximation](https://mlsanity.com/paper/2307.07682/) (2023)

## Abstract (arXiv)

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.