  Automatically identifying harmful content in video is an important task with
a wide range of applications. However, there is a lack of professionally
labeled open datasets available. In this work VidHarm, an open dataset of 3589
video clips from film trailers annotated by professionals, is presented. An
analysis of the dataset is performed, revealing among other things the relation
between clip and trailer level annotations. Audiovisual models are trained on
the dataset and an in-depth study of modeling choices conducted. The results
show that performance is greatly improved by combining the visual and audio
modality, pre-training on large-scale video recognition datasets, and class
balanced sampling. Lastly, biases of the trained models are investigated using
discrimination probing.
  VidHarm is openly available, and further details are available at:
https://vidharm.github.io
