  Understanding the emotional impact of movies has become important for
affective movie analysis, ranking, and indexing. Methods for recognizing evoked
emotions are usually trained on human annotated data. Concretely, viewers watch
video clips and have to manually annotate the emotions they experienced while
watching the videos. Then, the common practice is to aggregate the different
annotations, by computing average scores or majority voting, and train and test
models on these aggregated annotations. With this procedure a single aggregated
evoked emotion annotation is obtained per each video. However, emotions
experienced while watching a video are subjective: different individuals might
experience different emotions. In this paper, we model the emotions evoked by
videos in a different manner: instead of modeling the aggregated value we
jointly model the emotions experienced by each viewer and the aggregated value
using a multi-task learning approach. Concretely, we propose two deep learning
architectures: a Single-Task (ST) architecture and a Multi-Task (MT)
architecture. Our results show that the MT approach can more accurately model
each viewer and the aggregated annotation when compared to methods that are
directly trained on the aggregated annotations. Furthermore, our approach
outperforms the current state-of-the-art results on the COGNIMUSE benchmark.
