  Video Question Answering (VideoQA) is a task that requires a model to analyze
and understand both the visual content given by the input video and the textual
part given by the question, and the interaction between them in order to
produce a meaningful answer. In our work we focus on the Egocentric VideoQA
task, which exploits first-person videos, because of the importance of such
task which can have impact on many different fields, such as those pertaining
the social assistance and the industrial training. Recently, an Egocentric
VideoQA dataset, called EgoVQA, has been released. Given its small size, models
tend to overfit quickly. To alleviate this problem, we propose several
augmentation techniques which give us a +5.5% improvement on the final accuracy
over the considered baseline.
